ANNU

AL RE

PORT

2020-2021

Institute MissionMessage from the PresidentNote from the Chief Academic OfficerPersisting Through a Global PandemicInstitute Overview Awards and Honors New Faculty Faculty Promotion and Tenure Faculty by Area Post-DocsInstitute Progress Newly Renovated Facility Board Chair Succession New Award Recognizes Administrative Achievements Communicating the Story of TTIC Sponsored ResearchInstitute Research Research Philosophy Algorithms and Complexity Computational Biology Computer Vision and Computational Photography Machine Learning Robotics Speech and Language TechnologiesVisiting and Adjoint FacultyCourtesy FacultyCollaboration and CooperationTalks and Seminars

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Table of Contents

Workshops Women in Theoretical Machine Learning Symposium The Multifaceted Complexity of Machine Learning New Horizons in Theoretical Computer Science Distinguished Lecture Series Education The PhD Program Graduates, Diplomas, and Awards Quality Curriculum Course Enrollment Numbers for TTIC Courses Financial Support for Students Academics in the COVID-19 Pandemic New Courses Student Publications, Posters, and Abstracts 2020-21 Student Admissions and Student Body GrowthNotes from the Chief Financial OfficerFinancial ReportsInterns and Visiting ScholarsConstituent and Community Outreach Addressing Violence Against People of Asian Descent Institute Donation to India in Crisis Promoting Diversity, Equity and InclusionGovernance Board of Trustees Leadership and Administration Non-Discrimination StatementSpecial Thanks

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Achieving international impact through world-class research and education in fundamental computer science and information technology.

The Research MissionTTIC aims to achieve international impact through world-class research in fundamental computer science and information technology. Here we clarify the intended meaning of the terms in this statement.

Impact. The mission statement focuses on academic impact. A number of criteria may serve to evaluate such impact. These include volumes of peer-reviewed publications; reputation of venues in which publications appear; visibility of work in the community, as expressed in citations by others; number and reputation of co-authors, in particular in other institutions; recognition by the research community, including awards, prizes, invited talks, and invitation or election to serve in senior service positions in professional organizations; reports by external advisory bodies comprised of reputable senior researchers, etc. Precise objective measures of academic impact are controversial and elusive, and no one of the criteria above is alone a solid measure in itself. However, the combined evaluation of these and similar criteria helps assess the academic impact achieved by TTIC researchers.

Note that the number of patents filed, or the amount of extramural research funding, are not considered measures of academic impact. Although funding is clearly an important tool in achieving impact, it is only a tool and not an end in itself.

Fundamental. The mission statement is intended to focus on scientifically fundamental research. A scientific result is fundamental to the extent that it has open-ended implications. It is important to distinguish being fundamental from being economically important. A calendar program can be economically successful, and hence important, without adding to fundamental knowledge. The concept of NP-completeness adds greatly to the fundamental understanding of computation without having clear economic significance.

Computer Science and Information Technology. Computer science and information technology encompass many sub-disciplines. In the selection of sub-disciplines for study at TTIC, there should be some consideration of relevance to society as a whole. The interpretation of “computer science” and “information technology” should be such that TTIC remains relevant to the societal impact of computation and information.

Institute Mission

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The Education MissionThe education mission of TTIC is to achieve international impact through the accomplishments of its graduates as productive scientists and citizens. The notion of “impact” in the education mission is broader than in the research mission. Graduates of TTIC may achieve impact by starting successful companies, managing successful products, or influencing government directions in research funding. Of course, TTIC also strives to produce PhDs who achieve academic impact throughout their careers.

The institute strives to produce graduates who contribute to society through their intellectual leadership in computer science and information technology. Success in the education mission requires appropriate selection of curriculum, effective teaching to enable learning, effective assessment and mentorship of students, and effective marketing of students in the job market.

TTIC strives to place its PhD graduates at high-quality research institutions. TTIC also strives to make its PhD students visible to the academic community before graduation. This can be done most effectively through publications prior to graduation.

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The 2020-2021 academic year was a new and different experience for all of us, due to the continuing impact of the COVID-19 global pandemic, but it was also a year of important and substantial progress in TTIC’s mission of research and education. Despite the challenges of remote operations, TTIC students and faculty had many research accomplishments, important publications, and completed dissertations. We held many interesting and enlightening seminars and distinguished lectures, had several successful qualifying exams, and hosted a large number of visitors – almost exclusively remotely.

Our new students, faculty, and administrative staff became vital and contributing members of the TTIC community, even though they rarely met in person. While the coming year is expected to bring significantly

increased – and sorely missed – face-to-face interactions, I am proud of how TTIC has met the year’s challenges, continued to excel in our work and achievements, and supported one another both professionally and personally.

During the past academic year, we welcomed new PhD students, Research Assistant Professors, postdoctoral scholars, an administrative staff member (Liz Clay), and several remote visiting students. We graduated PhD students, who are now taking the next steps in their careers. We also welcomed four new Trustees to the Board, and Dr. Eric Grimson became the new Chair of the Board upon Dr. Sadaoki Furui’s retirement. The TTIC community acknowledges and greatly appreciates Dr. Furui’s service to TTIC as both former President and Board Chair – thank you!

Despite limited opportunities for face-to-face interaction in 2020-2021, TTIC continued its strong partnerships with TTI in Nagoya, Japan and the University of Chicago, including remote teaching and research collaboration. We were honored to have TTIC President Prof. Kazuo Hotate and UChicago Vice President Prof. Juan de Pablo join the TTIC Board of Trustees this year.

Several awards and honors were bestowed on TTIC faculty and students this academic year, providing more evidence of TTIC’s excellence and expanding our scholarly reputation. The faculty also received several new grants from various sources, including the National Institutes of Health, the National Science Foundation, Google, and Adobe, totaling about $1.6M.

Congratulations are in order to Prof. Karen Livescu, who received a promotion to Full Professor this year. Karen is a leader in the area of speech and language processing and related aspects of machine learning, and her contributions and insights in areas such as representation learning, acoustic word embeddings, multi-lingual speech embeddings, and applications to sign language recognition and low-resource languages have had tremendous impact. She has been a superb teacher, mentor, and contributor in a variety of ways at TTIC, and the promotion is well deserved.

Message from the President

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After the renovation of our space was completed in November 2020, including the addition of about a half-floor for TTIC to expand into, we opened our doors for (limited) in-person activities. The renovated space will better suit TTIC’s needs in several ways, and we are all excited to make great use of it as we transition back to regular operations.

TTIC is a special place for students, faculty, and staff to learn, collaborate, discover, and work. I am constantly impressed by the quality of the people and the work here, and it is exciting to interact daily with people who are current and future leaders in some of the most important and impactful areas of computing. It has been a great year, and I look forward to the new opportunities and accomplishments at TTIC in 2021-2022.

Matthew TurkPresident

© Patsy McEnroe | Design by Gensler

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2020-2021 was a fully virtual year, and I am again proud of the TTIC community – our students, faculty, and staff – for continuing to excel and produce fantastic work under these challenging conditions. In the meantime, our renovation was completed, and it looks great. Hopefully we will be able to have an increasingly in-person / in-person-optional experience this coming year. TTIC’s faculty and students won a number of notable awards. Prof. Karen Livescu was elected Fellow of the International Speech Communication Association, and President Matthew Turk was elected Fellow of the ACM. Student Naren Manoj received an NSF Graduate Fellowship, and student Freda Shi was awarded a Google PhD Fellowship. Also in collaborative work, TTIC students Takuma Yoneda and Chip Schaff, TTIJ student Takahiro

Maeda, and Prof. Matt Walter won first place at the Max Planck Institute Real Robot Challenge. To see how they did it, see their paper “Grasp and Motion Planning for Dexterous Manipulation for the Real Robot Challenge”. Congratulations to all! This year we hired five new Research Assistant Professors (RAPs): Kartik Goyal (PhD, Carnegie Mellon University), Hongyuan Mei (PhD, Johns Hopkins University), Derek Reiman (PhD, University of Illinois at Chicago), Lingxiao Wang (PhD, UCLA), and Raymond Yeh (PhD, University of Illinois at Urbana-Champaign).

We also said farewell to five who will be going on to new positions next year: Arturs Backurs and Sepideh Mahabadi who will be Senior Researchers at Microsoft Research, Steve Hanneke who will be an Assistant Professor at Purdue, Mina Karzand who will be an Assistant Professor at UC Davis, and Sam Wiseman who will be an Assistant Professor at Duke.

We anticipate that four of our current PhD candidates will complete the program this fall: Nick Kolkin (advised by Greg Shakhnarovich), RT Luo (advised by Greg Shakhnarovich), Lifu Tu (advised by Kevin Gimpel), and Blake Woodworth (advised by Nathan Srebro). Congratulations! I wish them all the best, and look forward to seeing their continued accomplishments in the years to come. TTIC continues to be an exciting place full of intellectual activity and a spirit of scientific curiosity. I am proud to be a part of this amazing academic community.

Avrim BlumChief Academic Officer

Message from the Chief Academic Officer

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© Patsy McEnroe | Design by Gensler

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The COVID-19 global pandemic led TTIC and institutions around the globe to operate in a remote delivery format starting in March 2020. The 2020-2021 academic year started with a rapid spread of COVID-19 throughout the fall quarter and into the winter. TTIC’s COVID-19 Response Group continued to meet weekly and to monitor the city, state, and federal responses as well as University of Chicago announcements and measures.

The internal COVID-19 information and resources page was frequently updated to reflect the changing public health protocols, and forms were made accessible so the TTIC community could communicate to TTIC management (anonymously if desired) any needs, struggles, or suggestions regarding TTIC’s COVID-19 planning, response, or remote operations, and to report any safety or health protocol concerns. The resources site also includes information regarding safety protocols and responsibilities, self-monitoring and reporting of COVID-19 exposure or infection, vaccination availability, mental health and coping resources, and special considerations for international students and scholars.

For Autumn, Winter, Spring, and Summer Quarters, the Response Group provided information to the community about plans for the upcoming term regarding courses, building access, meetings, travel, etc. TTIC continued to provide an allowance to all personnel in order to help cover costs related to remote work, which started when we first began remote operations in early 2020.

Most TTIC operations were fully remote all year, including courses, talks, meetings, interviews, external conferences, Board of Trustee meetings, and even tea times. Admission and recruitment seasons were conducted virtually, and we explored new ways to connect with applicants and researchers on the market. Once the building opened in November 2020 after the renovation, students, staff, and faculty who wanted to come into the building could complete safety protocols training and submit an attestation form to help ensure understanding of and compliance with the important requirements and protocols. A facility access sign-in form allowed TTIC to maintain safe capacity levels, assigned work spaces for those who came in, and served as a tool for contact tracing in the event of infections.

Throughout the pandemic, TTIC has emphasized providing a safe and supportive environment, trying to balance our mission and strong desire to push forward in our work with being flexible and accommodating to the many difficult and challenging situations we’ve all faced. The 2021-22 academic year will start with a vaccination requirement similar to that of the University of Chicago, and the institute anticipates moving towards more in-person operations as the Autumn Quarter 2021 progresses.

Persisting through a Global Pandemic

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Avrim Blum, Chief Academic Officer

Adam Bohlander, Director of IT

Chrissy Coleman, Administrative Director of Graduate Studies Jessica Jacobson, Chief Financial Officer

Amy Minick, Director of Human Resources

Matthew Turk, President

TTIC COVID-19 Response Group

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PhD ProgramFaculty and Staff

*3 students deferred matriculation from 2020 to 21 due to COVID-19

Institute Overview

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ProfessorsAssociate ProfessorsAssistant ProfessorsResearch Assistant ProfessorsAdjoint FacultyAdministrative Office Staff and ITPostdocs

82214

911

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Students enrolled 2020-21Master’s Degrees Awarded(in September 2020)PhD Degrees Awarded(in September 2020)Applicants for the 2020-21 Academic YearAnticipated Enrolling 2021-22

447

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227

5*

Awards and Honors

July 2020 - Steve HannekeProf. Steve Hanneke and his co-authors were awarded the Best Paper Award at the Conference on Learning Theory (COLT) 2020 for the paper “Proper Learning, Helly Number, and an Optimal SVM Bound.” The paper dealt with problems in PAC learning, classification, excess risk bounds and generalization error bounds.

November 2020 - Matthew TurkOn Thursday, November 19, 2020 President Matthew Turk gave a Keynote speech at the Institute of Electrical and Electronics Engineers (IEEE) Conference on Automatic Face and Gesture Recognition. His talk was titled “Is FG Enabling a Surveillance Dystopia?”

December 2020 - Chip Schaff and Takuma YonedaIn the fall of 2020, TTIC participated in the Max Planck Institute for Intelligent Systems’ Real Robot Challenge. Conducted remotely from August to December, participants wrote code that was run on robots that were all housed at Max Planck in Stuttgart, Germany. TTIC’s team won first place in the challenge, led by PhD students Charles Schaff and Takuma Yoneda.

January 2021 - Matthew TurkPresident Matthew Turk was named a 2020 Fellow of the Association for Computing Machinery (ACM), the world’s leading computing society. Dr. Turk was recognized for his “contributions to face recognition, computer vision, and multimodal interaction.” The distinction of Fellow is the ACM’s highest membership grade, awarded to less than 1% of members worldwide for outstanding accomplishments in computing.

January 2021 - Freda ShiThird-year PhD candidate Freda Shi was awarded a 2021 Google PhD Fellowship in Natural Language Processing. Her proposal centered around research that will be reflected in her PhD thesis, which will discuss learning language structures through grounding signals. The fellowship is a two-year award, but has the potential to be extended to three years.

March 2021 - Naren ManojSecond-year PhD student Naren Manoj was awarded a 2021 National Science Foundation (NSF) Graduate Research Fellowship. With only 2,000 fellows named each year, the program provides funding and other resources to a select group of scholars pursuing graduate research in STEM fields. The acceptance rate in 2019 was 16%, from a pool of 12,200 applicants.

May 2021 - Karen LivescuIn recognition of her “contributions to articulatory modeling, to speech representation learning, and to bridging the gaps between speech research, machine learning and natural language processing,” Prof. Karen Livescu has been elected a fellow of the International Speech Communication Association (ISCA). The ISCA honors a select number of members each year for significant contributions to their field.

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At the May 2021 meeting of the Board of Trustees, upon recommendation of the President, the Trustees approved Karen Livescu for promotion to full Professor. This promotion will be in effect as of October 2021.

Prof. Livescu received her B.A. in Physics from Princeton University in 1996 and spent the following year as a visiting student in Electrical Engineering at the Technion, Israel. She received her M.S. and Ph.D. in Electrical Engineering and Computer Science at the Massachusetts Institute of Technology, in 1999 and 2005. In 2005-2007 she was a postdoctoral lecturer in the Electrical Engineering and Computer Science department at the Massachusetts Institute of Technology.

Her research interests are in speech and language processing, recently focusing on speech recognition. She is particularly interested in statistical modeling techniques that can take advantage of both large stores of data and knowledge from linguistics and speech science.

Prof. Livescu has supervised 3 TTIC alumni: Hao Tang, who continued as a postdoc at MIT, and is now a lecturer at the University of Edinburgh; Taehwan Kim, who continued as a postdoc at CalTech, and is now at Amazon Alexa; and Bahador Nooraei, who continued to Groupon. She is currently advising 7 students in the PhD program, Ankita Pasad, Shane Settle, Bowen Shi, Freda Shi, Qingming Tang, Shubham Toshniwal, and David Yunis.

She has also served on a number of thesis committees, and advised several postdocs, research interns, and visiting students. Prof. Livescu has served as TTIC’s Student Support Coordinator since 2018, and as Colloquium Coordinator since 2014.

New Faculty: Research Assistant Professors

Faculty Promotion and Tenure

Filip Hanzely | PhD, King Abdullah University of Science and Technology

Mina Karzand | PhD, Massachusetts Institute of Technology

Audrey Sedal | PhD, University of Michigan

Bradly Stadie | PhD, University of California, Berkeley

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Algorithms and Complexity

Computational Biology

Arturs BackursAvrim BlumJulia ChuzhoySepideh MahabadiYury MakarychevThatchaphol SaranurakSaeed SeddighinMadhur Tulsiani

Michael YuJinbo Xu

Computer Vision and Computational Photography

Machine Learning

Greg ShakhnarovichMatthew Turk

Brian BullinsSteve HannekeFilip HanzelyMina KarzandDavid McAllesterNathan SrebroDanica Sutherland

RoboticsAudrey SedalBradly StadieMatthew Walter

Karen LivescuKevin GimpelMrinmaya SachanSam Wiseman

Speech and Language Technologies

Post-DocsHedyeh Beyhaghi | PhD - Cornell UniversityXiaoyang Jing | PhD - Fudan UniversityPritish Kamath | PhD - MITAli Vakilian | PhD - MITShirley Wu | PhD - University of Texas at AustinHongyang Zhang | PhD - Carnegie Mellon University

Faculty by Area

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In the spring of 2021, renovations of TTIC’s space at 6045 South Kenwood Avenue were completed by San Francisco-based architectural firm Gensler. The goal of the project was to reflect the heart of TTIC: research, education, and collaboration. With attention directed toward communal areas, the spaces adjacent to the atrium on the fifth and fourth floors, the new design of our space reinforces TTIC’s distinct identity, and provides a modern and thoughtful environment that promotes state-of-the-art work being conducted.

TTIC’s facility is now nearly 35,000 square feet, and features sweeping views of the Chicago skyline, Lake Michigan, the Midway Plaisance, and the collegiate gothic architecture of the University of Chicago. The fifth floor has a new and improved classroom, complete with a movable partition wall to create two smaller classrooms when needed. The classrooms are equipped with a full AV system with broadcasting capabilities. The focal point of the fourth floor is the main café, and the adjacent collaboration space. The new robotics lab is double the size of the old lab, and features glass walls to better highlight student work.

All existing private offices and storage rooms on both the fifth and fourth floors have been refreshed with coordinating colors, carpets, and wall bases. The new elements on these floors, including the student workstations, collaboration areas, café, and conference room, have been updated with coordinating finishings and furniture. Ceiling baffles were installed for acoustical control in key common areas. There are also sizable whiteboards throughout all common areas.

The newly acquired south side of the third floor has been transformed into a space for administrative staff offices and functions. All private office doors have been equipped with a sidelite frame for additional natural light, and the new large conference room has a fully integrated AV system. The space includes a small café and a significant amount of storage. The interior finishes and furnishings are coordinated with the décor on the fifth and fourth floors. Thank you to the members of TTIC’s Design Committee for your help and vital input in the renovation process. We look forward to sharing the newly renovated space with the entire TTIC community.

Newly Renovated Facility

Institute Progress

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TTIC common areas, pre-renovation

Renovation Design Committee

Jessica Jacobson, Chief Financial Officer

Matthew Turk, President

Yury Makarychev, Professor

Alicia McClarin, Administrative Assistant

© Patsy McEnroe | Design by Gensler

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In January 2021, Liz Clay began working in the newly created role of TTIC Communications Administrator. The institute aims to promote the exciting work and robust accomplishments being produced at TTIC, and we believe the best way to accomplish this is by telling compelling stories. Liz is chairing the website committee, focusing on website content curation, expanding TTIC’s social media footprint, conducting communications policy and procedure review and development, exploring outreach opportunities, drafting and designing print publications, and creating many new “highlight articles” and news items featuring faculty and students, their work, accolades, interests, and backgrounds.

The Office of Communications hopes to continue to support our students and faculty by increasing their visibility to the greater computer science community, and promoting the groundbreaking research being conducted on our campus.

Communicating the Story of TTIC

In May of 2021, Dr. Eric Grimson was named the new Board Chair of TTIC’s Board of Trustees. He succeeds Dr. Sadaoki Furui, who also served as the former President of TTIC. Dr. Grimson is the Chancellor for Academic Advancement at MIT CSAIL, has served on TTIC’s Board of Trustees since 2015, and is an advisor on the institute’s External Advisory Committee. TTIC is grateful to Dr. Grimson for his dedication and service and confident that under his leadership, the board will continue to strive for excellence and help TTIC grow as a world-class institution.

The Board expressed thanks Dr. Furui for his years of dedication to the Institute, as both President and Chair of the Board of Trustees. In May of 2021, the newly renovated conference room 501 was named in his honor. His friends and colleagues at TTIC wish him all the best, and will be forever grateful for his contributions to our academic community.

Board Chair Succession

Manager of Research Administration Rose Bradford was awarded the first annual Latrice Richards Outstanding Administrator Award at the 2020 diploma and awards ceremony. This award was created to recognize a member of the administrative staff who has gone above and beyond to make TTIC a great place to work and study.

Rose received this year’s award for her community outreach efforts, forging lasting relationships within our South Side community to increase access to STEM education for K-12 students in underserved schools. Rose’s dedication, enthusiasm and expertise elevate and serve the mission of TTIC and we look forward to her continued success. Award recipient names are engraved on a commemorative plaque at TTIC.

New Award Recognizes Administrator Achievements

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Sponsored ResearchIn FY 20-21, tenured and tenure-track faculty were awarded 8 grants totaling over $1.5M. The current grants portfolio includes:

• 17 National Science Foundation basic and collaborative research awards• 2 National Science Foundation Graduate Research Fellowship awards• 2 Google PhD Fellowships• 3 National Institute of Health awards• 2 Department of Defense awards• 2 Simons Foundation awards• Recent corporate awards from Google and Adobe.

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Research PhilosophyResearch is the heart and soul of activity at the Toyota Technological Institute at Chicago. The institute has an energetic and determined team of professors, visiting professors, assistant professors, research assistant professors, adjoint professors, and post-docs encompassing many areas of research interests, and from many countries and backgrounds, each bringing their own specialty to the Institute. With a generous budget, distinguished professors, and an environment that promotes learning and sharing, there are ample opportunities for collaborative research. Being on the campus of the University of Chicago, there is opportunity for close and cooperative research with not only the Computer Science Department, but with the departments of Mathematics, Statistics and the Booth Graduate School of Business. There are also many guests and visitors who come to TTIC to give talks, participate in workshops, and share their research findings, all heightening the feeling of enthusiasm that pulses through the Institute. The mission of TTIC includes “…achieving international impact through world-class research and education in fundamental computer science and information technology.” The research component of the mission is implemented through high quality research in high impact areas. Currently, there are active research programs in six areas: machine learning, algorithms and complexity, computer vision and computational photography, speech and language technologies, computational biology, and robotics. The areas are introduced below, and in some, TTIC’s strategy for achieving impact is also described. A key part of the strategy for achieving impact in all areas is to foster collaboration and communication between these areas.

Algorithms and Complexity

One of the central tasks in all areas of computer science is the writing of efficient software to perform required computation. In order to write such software, one must first design an efficient algorithm for the computational task at hand. The area of algorithms focuses on designing algorithms, and more generally developing powerful algorithmic tools, for solving fundamental computational problems that frequently occur in different areas of computer science. Complexity theory is the study of the power and limits of efficient computation. The central problem studied by complexity theorists is “Which computational problems can, and which cannot, be solved efficiently?” The study of algorithms and complexity is a part of a broader area called “theory of computer science,” or just “theory.” The area of theory works on developing theoretical foundations for computer science, which lead to a deeper understanding of computation in general, and specific computational tasks in particular, which include better algorithms and faster software. Below is a list of the work done at TTIC this year in the area of Algorithms and Complexity.

Institute Research

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Arturs BackursResearch Assistant Professorttic.edu/backurs

PUBLISHED/SUBMITTED PAPERSArturs Backurs, Amir Abboud, Karl Bringmann and Marvin Künnemann. “Impossibility Results for Grammar-Compressed Linear Algebra.” NeurIPS 2020.Arturs Backurs, Sepideh Mahabadi, Konstantin Makarychev and Yury Makarychev. “Two-sided Kirszbraun Theorem.” SoCG 2021.Arturs Backurs, Kyriakos Axiotis, Karl Bringmann, Ce Jin, Vasileios Nakos, Christos Tzamos and Hongxun Wu. “Fast and Simple Modular Subset Sum.” SOSA 2021.Arturs Backurs, Liam Roditty, Gilad Segal, Virginia Vassilevska Williams and Nicole Wein. “Towards Tight Approximation Bounds for Graph Diameter and Eccentricities.”SICOMP.

TALKS“Faster Kernel Matrix Algebra via Density Estimation.” TTIC, 2021.

INVOLVEMENTProgram Committees: STOC 2021, SODA 2021Conference Reviews: FOCS 2021, ITCS 2021, ICALP 2021, ESA 2021

RESEARCH FUNDING AWARDSNSF Small Grant CCF-2006806

Avrim BlumProfessor and Chief Academic Officerttic.edu/blum

PUBLISHED/SUBMITTED PAPERSAvrim Blum, Shelby Heinecke, and Lev Reyzin. “Communication-Aware Collaborative Learning”. AAAI 2021.Avrim Blum, Chen Dan, and Saeed Seddighin. “Learning Complexity of Simulated Annealing”. AISTATS 2021.Arturs Backurs, Avrim Blum, and Neha Gupta. “Active Local Learning”. COLT 2020.Avrim Blum and Han Shao. “Online Learning with Primary and Secondary Losses”. NeurIPS 2020.Avrim Blum, Travis Dick, Naren Manoj, and Hongyang Zhang. “Random Smoothing Might be Unable to Certify l Robustness for High-Dimensional Images”. Journal of Machine Learning Research, 2020. Avrim Blum (T. Roughgarden, Editor). “Approximation Stability and Proxy Objectives,” Chapter 6 in “Beyond the Worst-Case Analysis of Algorithms.” Cambridge University Press, 2021.

TALKS“On learning in the presence of biased data and strategic behavior.” Georgia Tech ARC Colloquium Series 2021.

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INVOLVEMENTEditorial board: Journal of the ACMSteering Committee: FOCS, ITCS, FORC, ALTSponsorship Chair: ICML 2021Co-Chair: Scientific Advisory Board, Simons Institute for the Theory of ComputingConference Reviewing: ALT 2021, NeurIPS 2020, TEAC

RESEARCH FUNDING AWARDSDARPA, Theoretical Foundations for Highly Robust Learning Systems, $3,088,765, 2019 - 2023.NSF AF Small, Foundations for Collaborative and Information-Limited Machine Learning, $324,884, 2018 - 2021.NSF AitF, Algorithms and Mechanisms for Kidney Exchange, $266,540, 2017 - 2021. Simons Foundation, Simons Collaboration on the Theory of Algorithmic Fairness, $480,000, 2020 - 2024.

CLASSES/SEMINARSTTIC 31150/CMSC 31150: Mathematical Toolkit, Spring 2021.

MISCELLANEOUSAdvisor for: Kevin Stangl, Keziah Naggita (co-advised with Matt Walter), Han Shao, Naren Manoj (co-advised with Yury Makarychev), and Kavya Ravichandran (co-advised with Nati Srebro).Thesis Committee: Paul Golz (CMU), Gabriele Farina (CMU), Saba Ahmadi (UMD), Mahsa Derakhshan (UMD), Soheil Behnezhad (UMD), Falcon Dai (TTIC), Omar Montasser (TTIC).Hosted summer interns: 2020: Saba Ahmadi (UMD/NU), Soheil Behnezhad (UMD), Mahsa Derakhshan (UMD). 2021: Marina Knittel (UMD).

Julia ChuzhoyProfessorttic.edu/chuzhoy

PUBLISHED/SUBMITTED PAPERSJ. Chuzhoy. “Decremental All-Pairs Shortest Paths in Deterministic Near-Linear Time.” STOC 2021.J. Chuzhoy and T. Saranurak. “Deterministic Algorithms for Decremental Shortest Paths via Layered Core Decomposition.” SODA 2021. J. Chuzhoy, S. Mahabadi and Z. Tan. “Towards Better Approximation of Graph Crossing Number.” FOCS 2020.J. Chuzhoy, Y. Gao, J. Li, D. Nanongkai, R. Peng and T. Saranurak. “A Deterministic Algorithm for Balanced Cut with Applications to Dynamic Connectivity, Flows, and Beyond.” FOCS 2020.P. Chalermsook, J. Chuzhoy and T. Saranurak. “Pinning Down the Strong Wilber 1 Bound for Binary Search Trees.” APPROX 2020, Special Issue of Theory of Computing for APPROX/RANDOM 2020.J. Chuzhoy and Z. Tan. “Towards Tight(er) Bounds for the Excluded Grid Theorem.” J. Comb. Theory, Ser. B, Volume 146, 2021.

TALKSPlenary speaker, Mathematical Congress of the Americas 2021.

INVOLVEMENTEditorial Board: SICOMP (until December 2020)Steering Committee: SODA and ITCS

RESEARCH FUNDING AWARDSNSF grant “AF: Small: Graph Theory and Its Uses in Algorithms and Beyond”, $398,163, 2020 - 2023.

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Sepideh MahabadiResearch Assistant Professorttic.edu/mahabadi

PUBLISHED/SUBMITTED PAPERSJulia Chuzhoy, Sepideh Mahabadi, and Zihan Tan. “Towards Better Approximation of Graph Crossing Number.” FOCS 2020.Arturs Backurs, Sepideh Mahabadi, Konstantin Makarychev, and Yury Makarychev.“Two-sided Kirszbraun Theorem.” SoCG 2021.Martin Aumüller, Sariel Har-Peled, Sepideh Mahabadi, Rasmus Pagh, and Francesco Silvestri. “Sampling a Near Neighbor in High Dimensions--Who is the Fairest of Them All?” Special Issue of TODS, ACM SIGMOD Record 50, 2021.

TALKS“Diversity and Fairness in Data Summarization Algorithms.” ICPCU Alumni Lecture Series 2021. EPFL, IC School, 2021. Columbia University, CS Department, 2021. University of Michigan, CSE Division, 2021. Google Research, 2021. CMU, CS Department, 2021. University of Maryland, CS Department, 2021. Purdue University, CS Department, 2021.Georgia Institute of Technology, School of CS, 2021. MSR Redmond, MLO and Algorithms groups, 2021. University of Waterloo, CS Department, 2021.“Two-sided Kirszbraun Theorem.” SoCG 2021.“Non-Adaptive Adaptive Sampling in Turnstile Streams.” Michigan-Purdue Theory Seminar 2020.“Determinant Maximization over Large Data Sets.” Sub-linear Reading Group, MIT, 2020.

INVOLVEMENTProgram Committee: SODA 2021, STOC 2021, SoCG 2022, HALG 2022.Reviewer: PODS 2022

CLASSES/SEMINARSTTIC 41000: Special Topics: Algorithms for Massive Data, Spring 2021.

CLASSES/SEMINARSTTIC 31010/CMSC 37000: Algorithms, Winter 2021.

MISCELLANEOUSAdvisor: Zihan Tan (UChicago), Rachit Nimavat (TTIC)Internal Service: Sexual Harassment Policy Committee, Curriculum Czar

Yury MakarychevProfessorttic.edu/makarychev

PUBLISHED/SUBMITTED PAPERSArturs Backurs, Sepideh Mahabadi, Konstantin Makarychev, and Yury Makarychev. “Two-sided Kirszbraun Theorem.” SoCG 2021.Konstantin Makarychev, Yury Makarychev, Ilya Razenshteyn. “Performance of Johnson–Lindenstrauss Transform for k-Means and k-Medians Clustering.” Special Issue of SICOMP.

INVOLVEMENTExecutive Committee: IDEALReviewer: Algorithmica, SODA 2021Assisted organizers: STOC 2021Guest Editor: Journal of Computer and System Sciences

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RESEARCH FUNDING AWARDSNSF Small Award CCF-1718820, $449,986, 2017–2021.NSF Medium Award CCF-1955173, jointly with K. Makarychev (Northwestern). TTIC’s share is $475,645. 2020 - 2024.NSF HDR TRIPODS Award CCF-1934843, jointly with N. Srebro and our colleagues at Northwestern University and the University of Chicago. TTIC’s share is $511,610. 2019 - 2022.

CLASSES/SEMINARSTTIC 31100/CMSC 39010-1: Computational and Metric Geometry, Spring 2021. The course covers fundamental concepts, algorithms, and techniques in computational and metric geometry.

MISCELLANEOUSAdvisor: Naren Manoj (with Avrim Blum), Max Ovsiankin, Jafar Jafarov (UChicago)Internal Service: Co-Chair, Faculty Hiring Committee

Saeed SeddighinResearch Assistant Professorttic.edu/seddighin

PUBLISHED/SUBMITTED PAPERSTomasz Kociumaka and Saeed Seddighin. “Improved Dynamic Algorithms for Longest Increasing Subsequence.” STOC 2021.Michael Mitzenmacher and Saeed Seddighin. “Sublinear Time Algorithms for Longest Increasing Subsequence.” SODA 2021.Sina Dehghani, Hamed Saleh, Saeed Seddighin, and Shanghua Teng. “Computational Analyses of the Electoral College: Campaigning Is Hard But Approximately Manageable.” AAAI 2021.Avrim Blum, Chen Dan, and Saeed Seddighin. “Learning Complexity of Simulated Annealing.” AISTATS 2021.Mohammad Ghodsi, MohammadTaghi Hajiaghayi, Masoud Seddighin, Saeed Seddighin, and Hadi Yami. “Fair Allocation of Indivisible Goods: Improvements.” Mathematics of Operations Research, 2021.

TALKS“Sublinear Time Algorithms for Longest Increasing Subsequence.” SODA 2021.“Improved Dynamic Algorithms for Longest Increasing Subsequence.” ACM Symposium on Theory of Computing, 2021. “Dynamic LIS and Erd¨os-Szekeres Partitioning Problem.” TTIC Research Seminar, 2021.“Dynamic LIS and Erd¨os-Szekeres Partitioning Problem.” Rutgers Theory Seminar, 2021.“Dynamic LIS and Erd¨os-Szekeres Partitioning Problem.” Sharif UT Theory Seminar.“Solving Blotto and Beyond.” Isfahan UT Theory Seminar.

INVOLVEMENTProgram Committee: TTCS 2021, AAAI 2021Reviewer: STOC 2021, FOCS 2021, ICALP 2021, AAAI 2021, SODA 2021, SPAA 2021, EC 2021

HONORS/AWARDSAdobe Research AwardGoogle Research Award

RESEARCH FUNDING AWARDSGoogle Research Award, $30,000.Adobe Research Award, $10,000.

MISCELLANEOUSHosted Summer Interns: Hamed Saleh and Marina Knittel.

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Madhur TulsianiAssociate Professor and Director of Graduate Studiesttic.edu/tulsiani

PUBLISHED/SUBMITTED PAPERSDinur, Y. Filmus, P. Harsha, M. Tulsiani. “Explicit SoS lower bounds from high-dimensional expanders.” ITCS 2021.F. Jeronimo, S. Srivastava, M. Tulsiani. “Near-linear Time Decoding of Ta-Shma’s Codes via Splittable Regularity.” STOC 2021.C. Jones, A. Potechin, G. Rajendran, M. Tulsiani, J. Xu. “Sum-of-Squares Lower Bounds for Sparse Independent Set.” FOCS 2021.

TALKS“Constraint Satisfaction and High-dimensional expanders,” SoS+TCS Reading group, 2021.“Explicit SoS lower bounds from high-dimensional expanders,” ITCS talk, 2021.“Explicit optimization lower bounds from topological expansion”, Topology and Geometry online seminar, 2021.

INVOLVEMENTManaging Editor: Theory of Computing.Co-Organizer: “New Horizons in TCS” Summer School.Reviewer: FOCS, ICALP, JACM, Journal of Combinatorial Theory

CLASSES/SEMINARSTTIC 31200/CMSC 37220: Information and Coding Theory, Spring 2021.

MISCELLANEOUSAdvised: Fernando Granha Jeronimo (UChicago), Dylan Quintana (UChicago), Mrinalkanti Ghosh (TTIC), Goutham Rajendran (UChicago), Shashank Srivastava (TTIC), Tushant Mittal (UChicago), June Wu (UChicago).Thesis Committee: Christopher Jones (UChicago)

Computational Biology

Computational biology studies biological systems (e.g., cell, protein, DNA and RNA) through mathematical modeling and optimization. Machine learning methods (e.g., probabilistic graphical model and deep learning) and optimization techniques (e.g., linear programming and convex optimization) have significant applications in this field. Algorithm design and complexity analysis also play an important role, especially when we want to know if there is an efficient algorithm that can find an exact or approximate solution to a specific biological problem. Below is a list of the work done at TTIC this year in the area of Computational Biology.

Jinbo XuProfessorttic.edu/xu

PUBLISHED/SUBMITTED PAPERSFan Ding, Nan Jiang, Jianzhu Ma, Jian Peng, Jinbo Xu, Yexiang Xue. “PALM: Probabilistic Area Loss Minimization for Protein Sequence Alignment.” UAI 2021.Fan Ding, Jianzhu Ma, Jinbo Xu, Yexiang Xue. “XOR-CD: Linearly Convergent Constrained Structure Generation.” ICML 2021.Lupeng Kong, Fusong Ju, Wei-Mou Zheng, Jianwei Zhu, Shiwei Sun, Jinbo Xu* and Dongbo Bu*. “ProALIGN: Directly learning alignments for protein structure prediction via exploiting context-specific alignment motifs.” Paper presented at RECOMB 2021.Jin Li and Jinbo Xu*. “Study of Real-valued distance prediction for protein structure prediction by deep learning.” RECOMB 2021.

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Xiaoyang Jing and Jinbo Xu*. “Fast and effective protein model refinement by deep graph neural networks.” Nature Computational Science, 2021.Fandi Wu and Jinbo Xu*. “Deep Template-based Protein Structure Prediction.” PLoS Computational Biology, 2021.Jinbo Xu*, Matthew McPartlon and Jin Li. “Improved protein structure prediction by deep learning irrespective of co-evolution information.” Nature Machine Intelligence, 2021.Boqiao Lai, Sheng Qian, Hanwei Zhang, Siwei Zhang, Alena Kozlova, Jubao Duan, Jinbo Xu* and Xin He*. “Predicting Regulatory Effects of Noncoding Genetic Variants in the Context of Neurodevelopment via Deep Transfer Learning.” Genome Biology (under review).Marco Necci et al. “Critical Assessment of Protein Intrinsic Disorder Prediction.” Nature Methods, 2021.Xiaoyang Jing and Jinbo Xu*. “Improved protein model quality assessment by integrating sequential and pairwise features using deep learning.” Bioinformatics, 2020.

TALKS“Protein structure prediction by deep learning.” 2020.

INVOLVEMENTAssociate Editor: Journal of BioinformaticsArea chair: ISMB 2021Reviewer: Nature Methods, Nature Communications, IEEE/ACM TCBB, PLoS Computational Biology, Bioinformatics, BMC Bioinformatics, Journal of Computational Biology, PROTEINS and Proteome.PC member: RECOMB

RESEARCH FUNDING AWARDS Jinbo Xu. NIH/NIGMS 1R01GM089753-06A1. New Computational Methods for Data-Driven Protein Structure Prediction. 2020-2024. $330,000 per year.Juntao Guo and Jinbo Xu. NIH/NIGMS R15GM132846-01A1. Investigation of ssDNA binding proteins and protein-ssDNA interaction. 2021-2024. $30,000 per year.

Michael YuResearch Assistant Professorttic.edu/yu

PUBLISHED/SUBMITTED PAPERSMichael K. Yu, Emily C. Fogarty, A. Murat Eren. “Discovering plasmids in metagenomes based on genetic architecture”. bioRxiv, 2020.Andrea R. Watson, Jessika Füssel, Iva Veseli, Johanna Zaal DeLongchamp, Marisela Silva, Florian Trigodet, Karen Lolans, Alon Shaiber, Emily Fogarty, Joseph M. Runde, Christopher Quince, Michael K. Yu, Arda Söylev, Hilary G. Morrison, Sonny T.M. Lee, Dina Kao, David T. Rubin, Bana Jabri, Thomas Louie, A. Murat Eren. “Adaptive ecological processes and metabolic independence drive microbial colonization and resilience in the human gut”. bioRxiv, 2021.

TALKS“Modeling biological systems using machine learning: applications in genotype-phenotype translation and plasmid discovery” Gladstone Institutes, 2021. “Modeling biological systems using machine learning: applications in genotype-phenotype translation and plasmid discovery.” University of Illinois Chicago, 2021. “Modeling biological systems using machine learning: applications in genotype-phenotype translation and plasmid discovery” Pacific Northwest Research Institute, 2021.

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Computer Vision and Computational Photography

Computer vision involves getting computers to extract useful information from pictures and videos. It has applications in robotics, surveillance, autonomous vehicles, and automobile collision avoidance.Historically, this is a central research area of computer science. Below is a list of the work done at TTIC this year in the area of Computer Vision and Computational Photography.

Greg ShakhnarovichProfessor and Director of Admissionsttic.edu/gregory

PUBLISHED/SUBMITTED PAPERSI. Vasiljevic, V. Guizilini, R. Ambrus, S. Pillai, W. Burgard, G. Shakhnarovich, A. Gaidon. “Neural Ray Surfaces for Self-Supervised Learning of Depth and Ego-motion,” 3DV 2021.P. Savarese, S. Kim, M. Maire, G. Shakhnarovich, D. McAllester. “Information-Theoretic Segmentation by Inpainting Error Maximization.” CVPR 2021.B. Shi, D. Brentari, G. Shakhnarovich, K. Livescu, “Fingerspelling Detection in American Sign Language.” CVPR 2021.S. Kim, N. Kolkin, J. Salavon, G. Shakhnarovich. “Deformable style transfer.” ECCV 2020.V. Guizilini, I. Vasiljevic, R. Ambrus, G. Shakhnarovich, A. Gaidon. “Full Surround Monodepth from Multiple Cameras.” arXiv 2104.00152.

TALKS“Towards general 3D perception in the real world.” Invited talk at Joint CS Seminar, TTI Nagoya, 2020.

INVOLVEMENTArea Chair: CVPR 2021, ECCV 2020, ICLR 2021, ICCV 2021Associate Editor: TPAMI

RESEARCH FUNDING AWARDS“Scalable Self-Supervised Learning for 3D Scene Understanding.” TRI University 2.0 program. $522,612 (for 3 years).Adobe corporate gift in support of research, $20,000.“Machine Learning to Improve Targeted Cancer Therapy.” University of Chicago CDAC award. $249,930 (for 2 years, as a co-PI; PI is S. Riesenfeld, UoC).

CLASSES/SEMINARSTTIC 31020: Introduction to Machine Learning, Autumn 2020.TTIC 31040: Introduction to Computer Vision, Spring 2021.Organized instruction for Introduction to Machine Learning at TTI Nagoya.

MISCELLANEOUSAdvisor: Nicholas Kolkin, Rouotan Luo, Steven Basart (UoC) desis (co-advised with Yali Amit)Internal Service: Co-chair, TTIC Committee on Diversity and Inclusion

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Matthew TurkPresidentttic.edu/turk

PUBLISHED/SUBMITTED PAPERSJedrzej Kozerawski, Victor Fragoso, Nikolaos Karianakis, Gaurav Mittal, Matthew Turk, and Mei Chen, “BLT: Balancing Long-Tailed Datasets with Adversarially-Perturbed Images.” Asian Conference on Computer Vision, 2020.

TALKS“Is FG Enabling a Surveillance Dystopia?” Keynote speech, IEEE Conference on Automatic Face and Gesture Recognition 2020.Invited speaker, Brandeis University, 2021.Invited speaker, ECCV Workshop on Fair Face Recognition and Analysis, 2020.

INVOLVEMENTEditorial board: TiiS, Vision and Image ComputingArea Chair: ICCVHonorary Chair: IEEE Workshop on Analysis and Modeling of Faces and Gestures, IEEE RFIWGeneral Chair: International Conference on Informatics, Electronics & VisionEditorial Board: International Journal of Computer Vision and Signal Processing

HONORS/AWARDSElected as a Fellow of the ACM, Class of 2020.

Machine Learning

Machine Learning generally refers to an engineering or design paradigm where systems are built based on automatic training from examples rather than detailed expert knowledge, much in the same way humans learn how to perform tasks and interact with the world. Most of modern Machine Learning is statistical in nature, and builds on statistical and probabilistic tools, as well as on algorithmic and computational developments. Especially in recent years, as training data is becoming plentiful, and massive computational and storage resources needed for handling the data are also becoming available, Machine Learning is playing a key role in many application areas. This includes classic artificial intelligence problems, such as computer vision, robotics, machine translation, question answering and dialogue systems. There are also a variety of “non-human” problems such as information retrieval, search, bioinformatics, and stock market prediction to be considered. Below is a list of the work done at TTIC this year in the area of Machine Learning.

Brian BullinsResearch Assistant Professorttic.edu/bullins

PUBLISHED/SUBMITTED PAPERSBullins, Brian. “Highly smooth minimization of non-smooth problems.” COLT 2020.Woodworth, Blake, Kumar Kshitij Patel, Sebastian U. Stich, Zhen Dai, Brian Bullins, H. Brendan McMahan, Ohad Shamir, and Nathan Srebro. “Is Local SGD Better than Minibatch SGD?” ICML 2020.Adil, Deeksha, Brian Bullins, Rasmus Kyng, and Sushant Sachdeva. “Almost-linear-time Weighted lp-norm Solvers in Slightly Dense Graphs via Sparsification.” ICALP 2021.Woodworth, Blake, Brian Bullins, Ohad Shamir, and Nathan Srebro. “The Min-Max Complexity of Distributed Stochastic Convex Optimization with Intermittent Communication.” COLT 2021.

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David McAllesterProfessorttic.edu/mcallester

PUBLISHED/SUBMITTED PAPERSP. Savarese, D. McAllester, S. Babu, M. MaireSign. “Domain Independent Dominance of Adaptive Methods.” CVPR 2021.P. Savarese, S.S.Y. Kim, M. Maire, G. Shakhnarovich, D. McAllester. “Information-Theoretic Segmentation by Inpainting Error Maximization.” CVPR 2021.

TALKS“MathZero, the Classification Problem, and Set-Theoretic Type Theory.” Princeton Institute for Advanced study Seminar on Theoretical Machine Learning.

CLASSES/SEMINARSTTIC 31230: Fundamentals of Deep Learning, Autumn 2021.

INVOLVEMENTArea Chair: ICLR 2021, NeuRIPS 2021

MISCELLANEOUSAdvisor: Pedro Savarese

TALKS“A Stochastic Newton Algorithm for Distributed Convex Optimization.” Research at TTIC Seminar, 2021.

INVOLVEMENTConference Reviews: NeurIPS 2020, ICLR 2021, ICML 2021

Nathan SrebroProfessorttic.edu/srebro

PUBLISHED/SUBMITTED PAPERSSuriya Gunasekar, Blake Woodworth, and Nathan Srebro. “Mirrorless Mirror Descent: A Natural Derivation of Mirror Descent.” AISTATS 2021.Pritish Kamath, Akilesh Tangella, Danica Sutherland, and Nathan Srebro. “Does Invariant Risk Minimization Capture Invariance?” AISTATS 2021.Omar Montasser, Steve Hanneke, and Nathan Srebro. “Reducing Adversarially Robust Learning to Non-Robust PAC Learning.” Paper presented at NeurIPS 2020.Edward Moroshko, Blake Woodworth, Suriya Gunasekar, Jason D Lee, Nathan Srebro, and Daniel Soudry. “Implicit Bias in Deep Linear Classification: Initialization Scale vs Training Accuracy.” NeurIPS 2020.Lijia Zhou, Danica Sutherland, and Nathan Srebro. “On Uniform Convergence and Low-Norm Interpolation Learning.” NeurIPS 2020.Blake Woodworth, Kumar Kshitij Patel, and Nathan Srebro. “Minibatch vs Local SGD for Heterogeneous Distributed Learning.” NeurIPS 2020.Pritish Kamath, Omar Montasser, and Nathan Srebro. “Approximate is good enough: Probabilistic variants of dimensional and margin complexity.” COLT 2020.Blake Woodworth, Suriya Gunasekar, Jason D Lee, Edward Moroshko, Pedro Savarese, Itay Golan, Daniel Soudry, and Nathan Srebro. “Kernel and rich regimes in overparametrized models.” COLT 2020.Omar Montasser, Surbhi Goel, Ilias Diakonikolas, and Nathan Srebro. “Efficiently learning adversarially robust halfspaces with noise.” Paper presented at COLT 2020.Hussein Mozannar, Mesrob Ohannessian, and Nathan Srebro. “Fair learning with private demographic data.” COLT 2020.Blake Woodworth, Kumar Kshitij Patel, Sebastian Stich, Zhen Dai, Brian Bullins, Brendan Mcmahan, Ohad Shamir, and Nathan Srebro. “Is local SGD better than minibatch SGD?” COLT 2020.

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Nandana Sengupta, Nathan Srebro, and James Evans. “Simple surveys: Response retrieval inspired by recommendation systems.” Social Science Computer Review, 2021.Chenxin Ma, Martin Jaggi, Frank E. Curtis, Nathan Srebro, and Martin Takac. “An accelerated communication-efficient primal-dual optimization framework for structured machine learning.” Optimization Methods and Software, 2021.

TALKS“Understanding Deep Learning through Optimization Bias.” C3DTI Colloquium Talk, 2021.“Explicit and Implicit Inductive Bias in Deep Learning.” Information Theory Workshop Invited Tutorial, 2021. “What, How and When can we Learn Adversarially Robustly?” Weizmann Institute, 2021. “Heterogeneous and Pluralistic Distributed Learning.” FLOW Seminar, 2020. “On Uniform Convergence and Interpolation Learning.” Simons Institute Deep Learning Reunion Workshop, 2020.“Complexity of Distributed and Federated Stochastic Optimization.” OPTML++ 2021.

RESEARCH FUNDING AWARDSNSF-Simons Research Collaborations on the Mathematical and Scientific Foundations of Deep Learning, $10 million.

CLASSES/SEMINARSReading Group on Machine Learning and OptimizationTTIC 31000: Research at TTIC, Autumn 2020. TTIC31120: Statistical and Computational Learning Theory, Autumn 2020. Taught concurrently at TTIC and EPFL.

INVOLVEMENTEditorial Board: JMLRSenior Area Chair: ICML 2021Organizer: IDEAL Special Quarter on Theory of Deep Learning

Danica J. SutherlandResearch Assistant Professorttic.edu/sutherland

PUBLISHED/SUBMITTED PAPERSLijia Zhou, Danica J. Sutherland, and Nathan Srebro. “On Uniform Convergence and Low-Norm Interpolation Learning.” NeurIPS 2020.Pritish Kamath, Akilesh Tangella, Danica J. Sutherland, and Nathan Srebro. “Does Invariant Risk Minimization Capture Invariance?” AISTATS 2021. Rémi Flamary, Nicolas Courty, Alexandre Gramfort, Mokhtar Z. Alaya, Aurélie Boisbunon, Stanislas Chambon, Laetitia Chapel, Adrien Corenflos, Kilian Fatras, Nemo Fournier, Léo Gautheron, Nathalie T.H. Gayraud, Hicham Janati, Alain Rakotomamonjy, Ievgen Redko, Antoine Rolet, Antony Schutz, Vivien Seguy, Danica J. Sutherland, Romain Tavenard, Alexander Tong, and Titouan Vayer. “POT: Python Optimal Transport.” JMLR, 2021.

TALKS“Can Uniform Convergence Explain Interpolation Learning?” Penn State Statistics colloquium, 2020.“Deep kernel-based distances between distributions.” Pacific Interdisciplinary Hub on Optimal Transport, 2021.

INVOLVEMENTArea Chair (or equivalent): NeurIPS 2020, AISTATS 2021, AAAI 2020.Reviewer: Journal of Machine Learning Research, IEEE Transactions on Information Theory, IEEE Transactions on Pattern Analysis and Machine Intelligence. MISCELLANEOUSThesis committees: Iryna Korshunova (Ghent University) Tong Che, Cambridge University)

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Robotics

Robotics can generally be defined as a field concerned with the development and realization of intelligent, physical agents that are able to perceive, plan, and act intentionally in an uncertain world. Robotics is a broad field that includes mechanical design, planning and control, perception, estimation, and human-robot interaction among others. At TTIC, robotics research currently focuses on developing advanced perception algorithms that endow robots with a rich awareness of, and the ability to act deliberately, within their surroundings. Researchers are particularly interested in algorithms that take multi-modal observations of a robot’s surroundings as input, notably image streams and natural language speech, and infer rich properties of the people, places, objects, and actions that comprise a robot’s environment. Integral to these technologies is their reliance on techniques from machine learning in developing probabilistic and statistical methods that are able to overcome the challenge of mitigating the uncertainty inherent in performing tasks effectively in real-world environments. These tasks include assistive technology for people living with physical and cognitive impairments, healthcare, logistics, manufacturing, and exploration. Below is a list of the work done at TTIC this year in the area of Robotics.

Audrey SedalResearch Assistant Professorttic.edu/sedal

PUBLISHED/SUBMITTED PAPERSA. Sedal and A. Wineman. “Material Memory Matters in Robotics.” Paper presented at RSS 2021. DO-SIM Workshop.A. Sedal and A. Wineman. “Nonlinear Viscoelastic Effects in Fiber-Reinforced Soft Actuators.” SES 2021. A. Sedal and A. Wineman. “Force reversal and energy dissipation in composite tubes through nonlinear viscoelasticity of component materials.” Proceedings of the Royal Society A, 2020.

TALKS“Soft Robotics and Morphological Intelligence.” SNL 2021. “Force reversal and energy dissipation in composite tubes through nonlinear viscoelasticity of component materials,” University of Manchester Mathematics of Waves and Materials Group, 2021.

CLASSES/SEMINARSTTIC 31000: Research at TTIC, Spring 2021. Co-Instructor with Saeed Seddighin.

INVOLVEMENTAssociate Editor: IEEE ICRA 2022Reviewer: Science Robotics, ASME Journal of Mechanisms and Robotics, IEEE Transactions on Mechatronics

Bradly StadieResearch Assistant Professorttic.edu/stadie

PUBLISHED/SUBMITTED PAPERSLunjun Zhang, Ge Yang, Bradly C Stadie. “World Model as a Graph: Learning Latent Landmarks for Planning.” Paper presented at ICML 2021.

INVOLVEMENTReviewer: ICML, ICLR, NeurIPS, NeurIPS Deep Learning WorkshopArea Chair: Meta Learn Workshop

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CLASSES/SEMINARSTTIC 31170: Planning, Learning, and Estimation for Robotics and Artificial Intelligence, Spring 2021. Co-instructor with Matthew Walter.

MISCELLANEOUSInternal Service: Website Committee, Sexual Harassment Policy Committee

Matthew WalterAssistant Professorttic.edu/walter

PUBLISHED/SUBMITTED PAPERSF. Z. Dai and M. R. Walter. “Loop estimator for discounted values in Markov reward processes.” Proceedings of the National Conference on Artificial Intelligence, 2021.C. Schaff and M. R. Walter. “Residual policy learning for shared autonomy.” Proceedings of Robotics: Science and Systems, 2020.J. Tani, A.F. Daniele, G. Bernasconi, A. Camus, A. Petrov, A. Courchesne, B. Mehta, R. Suri, T. Zaluska, M. R. Walter, E. Frazzoli1, L. Paull, and A. Censi. “Integrated benchmarking and design for reproducible and accessible evaluation of robotic agents.” Proceedings of the IEEE/RSJ International Conference on Intelligent Robots and Systems, 2020.N. Funk, C. Schaff, R. Madan, T. Yoneda, J. U. De Jesus, J. Watson, E. K. Gordon, F. Widmaier, S. Bauer, S. S. Srinivasa, T. Bhattacharjee, M. R. Walter, and J. Peters. “Benchmarking structured policies and policy optimization for real-world dexterous object manipulation.” IEEE Robotics and Automation Letters, 2021.T. M. Howard, E. Stump, J. Fink, J. Arkin, R. Paul, D. Park, S. Roy, D. Barber, R. Bendell, K. Schmeckpeper, J. Tian, J. Oh, M. Wigness, L. Quang, B. Rothrock, J. Nash, M. R. Walter, F. Jentsch, and N. Roy. “An intelligence architecture for grounded language communication with field robots.” Field Robotics, 2021.M. R. Walter, S. Patki, A. F. Daniele, E. Fahnestock, F. Duvallet, S. Hemachandra, J. Oh, A. Stentz, N. Roy, and T. M. Howard, “Language understanding for field and service robots in a priori unknown environments,” Field Robotics, 2021.

INVOLVEMENTAssociate Editor: IEEE RA-L, ACM THRIArea Chair: ICLR, NeurIPS, CoRL, ICMLSteering Committee: Northeast Robotics ColloquiumPanelist: NSF Graduate Research Fellowship ProgramContributor: AI Driving Olympics (AI-DO), NeurIPS, 2020Board Member: Duckietown FoundationSenior Program Committee: AAAIProgram Committee: EMNLP, AISTATSReviewer: IEEE Transactions on Robotics, International Journal of Robotics Research, Field Robotics Journal, Conference on Robot Vision, Association for Computational Linguistics (ACL), IEEE/RSJ IROS, IEEE ICRA, ACM/IEEE HRI, ICCVCo-Organizer: CVPR 2021 Workshop on Frontiers of Monocular 3D Perception, IROS 2020 Workshop on Benchmarking Progress in Autonomous Driving

CLASSES/SEMINARS TTIC 31170: Robot Learning and Estimation, Spring 2021.Robotics Reading Group

MISCELLANEOUSQualifying Exam Committee Chair: Naren Manoj (TTIC), Vadim Grinberg (TTIC)Thesis Committee Member: Jacob Arkin (University of Rochester) Igor Vasilijevic (TTIC)Thesis Examiner: James Mount (Queensland University of Technology)Mentor: Jesus Duran (Chicago Public Schools)TTIC Young Researcher Seminar Series CzarSenior Member: IEEE

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Speech and Language Technologies

This area is concerned with getting computers to analyze and extract information from spoken language, as well as to generate spoken audio. At TTIC, current speech research focuses mainly on the analysis side. For example, speech recognition is the problem of transcribing the words being spoken in an audio signal, such as that recorded from a microphone. Speech processing heavily relies on techniques from machine learning and statistics, as well as ideas from linguistics and speech science, and shares algorithms with computer vision and computational biology. This area has applications such as automated telephone information centers, dictation systems, machine translation, archiving and search of spoken documents, assistance for the visually or hearing-impaired, and other human-computer interface systems. Below is a list of the work done at TTIC this year in the area of Speech and Language Technologies.

Kevin GimpelAssistant Professorttic.edu/gimpel

PUBLISHED/SUBMITTED PAPERSShubham Toshniwal, Allyson Ettinger, Kevin Gimpel, Karen Livescu. “PeTra: A Sparsely Supervised Memory Model for People Tracking.” ACL 2020. Lifu Tu, Richard Yuanzhe Pang, Sam Wiseman, Kevin Gimpel. “ENGINE: Energy-Based Inference Networks for Non-Autoregressive Machine Translation.” ACL 2020. Lifu Tu*, Tianyu Liu*, Kevin Gimpel. “An Exploration of Arbitrary-Order Sequence Labeling via Energy-Based Inference Networks.” EMNLP 2020. Xiaoan Ding*, Tianyu Liu*, Baobao Chang, Zhifang Sui, Kevin Gimpel. “Discriminatively-Tuned Generative Classifiers for Robust Natural Language Inference.” EMNLP 2020.Shubham Toshniwal, Sam Wiseman, Allyson Ettinger, Karen Livescu, Kevin Gimpel. “Learning to Ignore: Long Document Coreference with Bounded Memory Neural Networks.” EMNLP 2020. Haoyue Shi, Karen Livescu, Kevin Gimpel. “On the Role of Supervision in Unsupervised Constituency Parsing.” EMNLP 2020. Xiaoan Ding, Kevin Gimpel. “FlowPrior: Learning Expressive Priors for Latent Variable Sentence Models.” NAACL 2021.Shubham Toshniwal, Haoyue Shi, Bowen Shi, Lingyu Gao, Karen Livescu, Kevin Gimpel. “A Cross-Task Analysis of Text Span Representations.” 5th Workshop on Representation Learning for NLP.Mingda Chen, Kevin Gimpel. “Learning Probabilistic Sentence Representations from Paraphrases.” 5th Workshop on Representation Learning for NLP.Lingyu Gao, Kevin Gimpel, Arnar Jensson. “Distractor Analysis and Selection for Multiple-Choice Cloze Questions for Second-Language Learners.” 15th Workshop on Innovative Use of NLP for Building Educational Applications.Lifu Tu, Richard Yuanzhe Pang, Kevin Gimpel. “Improving Joint Training of Inference Networks and Structured Prediction Energy Networks.” 4th Workshop on Structured Prediction for NLP.Mingda Chen*, Zewei Chu*, Karl Stratos, Kevin Gimpel. “Mining Knowledge for Natural Language Inference from Wikipedia Categories.” Findings of EMNLP 2020.

TALKS“Learning to do Structured Inference in Natural Language Processing.” Seminar Series in Cognitive Computing at Baidu Research, 2020.

INVOLVEMENTSenior Area Chair: Machine Learning for NLP: Language Modeling and Sequence to Sequence Models, NAACL 2021.Journal reviewing: TACL

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Karen LivescuAssociate Professorttic.edu/livescu

PUBLISHED/SUBMITTED PAPERSB. Shi, D. Brentari, G. Shakhnarovich, and K. Livescu. “Fingerspelling detection in American Sign Language.” CVPR 2021.H. Shi, K. Livescu, and K. Gimpel. “Substructure substitution: Structured data augmentation for NLP.” Findings of ACL 2021.Y. Hu, S. Settle, and K. Livescu. “Acoustic span embeddings for multilingual query-by-example search.” SLT 2021.B. Shi, S. Settle, and K. Livescu. “Whole-word segmental speech recognition with acoustic word embeddings.” SLT 2021.H. Shi, K. Livescu, and K. Gimpel. “On the role of supervision in unsupervised constituency parsing.” EMNLP 2020.S. Toshniwal, S. Wiseman, A. Ettinger, K. Livescu, and K. Gimpel. “Learning to ignore: Long document coreference with bounded memory neural networks.” EMNLP 2020Y. Hu, S. Settle, and K. Livescu. “Multilingual jointly trained acoustic and written word embeddings.” Interspeech 2020.S. Jin, S. Wiseman, K. Stratos and K. Livescu. “Discrete latent variable representations for low-resource text classification.” ACL 2020.S. Toshniwal, A. Ettinger, K. Gimpel, and K. Livescu. “PeTra: A sparsely supervised memory model for people tracking.” ACL 2020.P. Peng, H. Kamper, and K. Livescu. “A correspondence variational autoencoder for unsupervised acoustic word embeddings.” NeurIPS 2020 Workshop on Self-Supervised Learning for Speech and Audio Processing.S. Toshniwal, H. Shi, B. Shi, L. Gao, K. Livescu, and K. Gimpel. “A cross-task analysis of text span representations.” ACL RepL4NLP 2020.

TALKS“Learning to embed spoken words.” UVA ECE Colloquium, 2021.“Learning to embed spoken words across tasks and languages.” U. T. Austin Forum for AI, 2020.“Embeddings for spoken words.” Amazon AWS, 2020.

INVOLVEMENTAssociate Editor: IEEE TPAMI, IEEE OJ-SPArea Chair: Interspeech 2021, EMNLP 2020Co-organizer: NeurIPS Workshop on Self-Supervised Learning for Speech and Audio ProcessingStanding Review Committee Member: Transactions of the ACLReviewer: CVPR 2021, Computer Speech and Language

HONORS/AWARDSISCA Fellow, IEEE SPS Distinguished Lecturer

Conference reviewing: EMNLP 2020 (outstanding reviewer), EACL 2021, ICLR 2021 (outstanding reviewer award), CoNLL 2020, AACL 2020, *SEM 2020. Workshop reviewing: W-NUT 2020, SPNLP 2020, Eval4NLP 2020.

CLASSES/SEMINARSTTIC 31190: Natural Language Processing, Fall 2020

MISCELLANEOUSAdvisor: Mingda Chen (TTIC), Lingyu Gao (TTIC), Freda Shi (TTIC, co-advised with K. Livescu), Shubham Toshniwal (TTIC, co-advised with K. Livescu), Lifu Tu (TTIC), Davis Yoshida (TTIC), Xiaoan Ding (University of Chicago)PhD dissertation committees: Qingming Tang (TTIC), Ruotian Luo (TTIC)PhD dissertation committee chair/co-chair: Lifu Tu (TTIC), Shubham Toshniwal (TTIC)Internal Service: Website Committee, Faculty Coordinator of Visiting Student Program, Hiring Committee (Co-chair), Sexual Harassment Policy Committee

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RESEARCH FUNDING AWARDSNIH R01MD015064-01A1 sub-award: \Primed to (re)act: Can changes in procedural language prevent adverse events between police and minority male youth?” 2021.

CLASSES/SEMINARSTTIC 31220: Unsupervised learning and data analysis, Winter 2021.

MISCELLANEOUSAdvisor: Ankita Pasad (TTIC), Shane Settle (TTIC), Bowen Shi TTIC), Freda (Haoyue) Shi, co-advised with Kevin Gimpel (TTIC), Qingming Tang (TTIC), Shubham Toshniwal, co-advised with Kevin Gimpel (TTIC), David Yunis (TTIC), Ju-Chieh Chou (GSAL), Puyuan (Jason) Peng (University of Chicago), Yushi Hu (Visiting TTIC Student)Thesis Committees: Lifu Tu (TTIC), Marcely Zanon Boito (Universite Grenoble Alpes)Internal Service: Colloquium Coordinator, Student Support Coordinator, Student Workshop Faculty AdvisorUniversity of Chicago Service: CDAC Steering Committee Member

© Patsy McEnroe | Design by Gensler

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Visiting and Adjoint Faculty

Robert NowakAdjoint Professor, TTICProfessor, University of Wisconsin-MadisonPhD - University of Wisconsin-Madison

Alexander RazborovResident Adjoint Professor, TTICProfessor, University of ChicagoPhD - Steklov Mathematical Institute

Yutaka SasakiAdjoint Professor, TTICProfessor, TTI-JapanPhD - University of Tsukuba

Norimichi UkitaAdjoint Professor, TTICProfessor, TTI-JapanPhD - Kyoto University

Stephen WrightAdjoint Professor, TTICProfessor, University of Wisconsin-MadisonPhD - University of Queensland

David ChiangAdjoint Professor, TTICAssociate Professor, University of Notre DamePhD - University of Pennsylvania

Eden ChlamtacVisiting Professor, TTICAssistant Professor, Ben Gurion UniversityPhD - Princeton University

David ForsythAdjoint Professor, TTICProfessor, University of Illinois at Urbana-ChampaignPhD - Balliol College, Oxford

Sanjeev KhannAdjoint Professor, TTICProfessor, University of PennsylvaniaPhD - Stanford University

Richard LiptonAdjoint Professor, TTICProfessor and Frederick G. Storey Chair (Emeritus)Georgia Institute of TechnologyPhD - Carnegie Mellon University

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Courtesy Faculty

Michael Maire Assistant ProfessorDepartment of Computer ScienceUniversity of ChicagoPhD - University of California, Berkeley

Aaron PotechinAssistant ProfessorUniversity of ChicagoPhD - MIT

Janos Simon Professor and Director of Graduate StudiesDepartment of Computer ScienceUniversity of ChicagoPhD - Cornell University

Chenhao TanAssistant ProfessorUniversity of ChicagoPhD - Cornell University

Rebecca Willett ProfessorDepartments of Statistics and Computer ScienceUniversity of ChicagoPhD - Rice University

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László Babai George and Elizabeth Yovovich ProfessorDepartments of Computer Science and MathematicsUniversity of ChicagoPhD - Hungarian Academy of Sciences, Budapest

Allyson EttingerAssistant ProfessorUniversity of ChicagoPhD - University of Maryland, College Park

Michael Franklin Liew Family Chair of Computer ScienceUniversity of ChicagoPhD - University of Wisconsin

Mladen Kolar Associate Professor of Econometrics and Statistics Booth School of BusinessUniversity of ChicagoPhD - Carnegie Mellon University

Risi Kondor Assistant ProfessorDepartments of Computer Science and Statistics University of ChicagoPhD - Columbia University

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TTIC wishes to congratulate TTIJ in celebrating its 40th anniversary, as well as the 25th anniversary of its doctoral program.

Professor Matt Walter is teaching a MOOC (Massive Online Open Course) on Duckietown, a project teaching fundamental lessons in robotics and AI. He is teaching the course along with his colleagues Liam Paull (University of Montreal Professor), Andrea Censi (Senior Researcher at ETH Zurich), Jacopo Tani (Senior Researcher at ETH Zurich), Stefanie Tellex (Brown University Professor), Nick Wang (National Chiao Tung University, Taiwan Professor), and Kirill Krinkin (Head of The Department of Software Engineering and Computer Applications at St. Petersburg Electrotechnical University).

PhD candidate Freda Shi collaborated with Sida Wang (Facebook Research Scientist) and Luke Zettlemoyer (U Washington Professor, Facebook Research Scientist) on lexicon induction from monolingual corpora during her internship at Facebook. She also collaborated with Jiayuan Mao (MIT Ph.D. student), Jiajun Wu (Stanford Assistant Professor), Roger Levy (MIT Professor) and Josh Tenenbaum (MIT Professor) on a grounded grammar induction project.

President Matthew Turk has been working with PhD student Jedrzej Kozerawski (UC Santa Barbara) on meta-learning for few-shot image classification. He also collaborated with Prof. E. R. (Roy) Davies (University of London) on a book entitled “Advanced Methods and Deep Learning in Computer Vision.”

PhD Candidate Igor Vasiljevic collaborated with TTIC Professor Greg Shakhnarovich, Adrien Gaidon (Head of Machine Learning Research at Toyota Research Institute), Vitor Guizilini (TRI Research Scientist), Rares Ambrus (TRI Research Scientist), Sudeep Pillai (TRI ML-Engineering Lead), and Wolfram Burgard (University of Freiburg Professor) on research regarding neural ray surfaces for self-supervised learning.

Professor Nathan Srebro is a collaborator in one of two new awards through the Mathematical and Scientific Foundations of Deep Learning (MoDL) program. The NSF Directorates for Mathematical and Physical Sciences (MPS), Computer and Information Science and Engineering (CISE), Engineering (ENG), and the Simons Foundation Division of Mathematics and Physical Sciences have partnered to sponsor the new research collaborations through MoDL focused on challenging questions in Mathematical and Scientific Foundations of Deep Learning.

Collaboration and Cooperation

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Talks and Seminars

Talks and seminars are an important part of any academic institution. They are both a way for researchers to promote their research, and to keep abreast of recent developments. They allow students to be exposed to ideas and researchers that may play a role in shaping their academic views, research direction, or even career. Talks and seminars play an important role in establishing the level of intellectual activity and influx of innovative ideas at an institution: research is more likely to be productive in an active environment with significant interaction between researchers. The table below lists seminars given at TTIC, many of which are given by speakers from other universities and research institutions, as part of the TTIC Colloquium: a forum for talks by invited speakers on work of current relevance and broad interest to the computer science community. Other talks may be a part of the Research at TTIC series: a weekly seminar series presenting research currently underway at the Institute. Every week a different TTIC faculty member will present their research. The lectures are intended both for students seeking research topics and advisers, and for the general TTIC and University of Chicago communities interested in hearing what their colleagues are currently involved in. The Young Researcher Seminar Series features talks by PhD students and postdocs whose research is of broad interest to the computer science community. The series provides an opportunity for early-career researchers to present recent and promising work and to meet with students and faculty at TTIC and nearby universities. Some speakers may be part of research Reading Groups: people presenting papers that are of interest to a particular group, such as the theory group or the programming languages group. Most seminars are advertised outside of TTIC and are intended to be for a broad audience in computer science. In the spring quarter there are a large number of recruiting seminars which are talks given by candidates for faculty positions. The TTIC Event Calendar can be accessed from the main website: www.ttic.edu

2018-2019

2017-2018

2016-2017 79 Speakers

89 Speakers

123 Speakers

2019-2020 123 Speakers

2020-2021 90 Speakers

Speaker Institute Title DateTakeshi Onishi Toyota Technological

Institute at ChicagoThesis Defense: Relation/Entity-Centric Reading Comprehension

8/4/2020

Hai Wang Toyota Technological Institute at Chicago

Thesis Defense: Knowledge Efficient Deep Learning for Natural Language Processing

8/19/2020

Babak Hassibi California Institute of Technology

IDEAL Seminar: The Blind Men and the Elephant: The Mysteries of Deep Learning

10/1/2020

Avrim Blum Toyota Technological Institute at Chicago

On Learning in the Presence of Biased Data and Strategic Behavior

10/2/2020

Julia Gaudio Massachusetts Institute of Technology

IDEAL Seminar: Regression Under Sparsity 10/6/2020

Jason Lee Princeton University IDEAL Seminar: Beyond Linearization in Deep Learning: Hierarchical Learning and the Benefit of Representation

10/8/2020

David McAllester Toyota Technological Institute at Chicago

Status Report on MathZero --- The Quest for an AlphaZero of Mathematics

10/9/2020

Daniel Hsu Columbia University IDEAL Seminar: Contrastive Learning, Multi-view Redundancy, and Linear Models

10/13/2020

Quanquan Gu University of California - Los Angeles

IDEAL Seminar: Learning Wide Neural Networks: Polylogarithmic Over-parameterization and A Mean Field Perspective

10/15/2020

Matthew Turk Toyota Technological Institute at Chicago

Beyond Fairness in Face Recognition 10/16/2020

Anca Dragan University of California - Berkeley

Colloquium: Getting Human-Robot Interaction Strategies to Emerge from First Principles

10/19/2020

Nati Srebro Toyota Technological Institute at Chicago

Toward Understanding Deep Learning, and Whether It’s All Just a Big Bad Kernel

10/23/2020

Francis Bach INRIA IDEAL Seminar: On the Convergence of Gradient Descent for Wide Two-Layer Neural Networks

10/29/2020

Steve Hanneke Toyota Technological Institute at Chicago

The Sample Complexity of PAC Learning: Optimal Learning, Proper Learning, and Compression Schemes

10/30/2020

Jeannette Bohg Stanford University Colloquium: Scaffolding, Abstraction and Learning from Demonstration - A Route to Robot Learning

11/2/2020

Sudarshan Babu Toyota Technological Institute at Chicago

Student Talk: HyperNetworks and their Application to Meta-Learning

11/3/2020

Shengjie Lin Toyota Technological Institute at Chicago

Student Talk: View Planning for Robot Vision: Approaches and Applications

11/4/2020

Matus Telgarsky University of Illinois at Urbana-Champaign

IDEAL Seminar: The Dual of the Margin: Improved Analyses and Rates of Gradient Descent’s Implicit Bias

11/5/2020

Audrey Sedal Toyota Technological Institute at Chicago

Soft Robot Design and Embodied Intelligence 11/6/2020

Surbhi Goel Microsoft Research - New York City

IDEAL Seminar: Computational Complexity of Learning Neural Networks over Gaussian Marginals

11/10/2020

Emmanuel Abbe École Polytechnique Fédérale de Lausanne

IDEAL Seminar: Learning Monomials and Separation between Deep Learning and SQ

11/12/2020

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Thatchaphol Saranurak

Toyota Technological Institute at Chicago

Improved Algorithms for Vertex Connectivity 11/13/2020

Leila Wehbe Carnegie Mellon University

From Language Models to Human Brains and Back Again

11/16/2020

Rayadurgam Srikant

University of Illinois at Urbana-Champaign

IDEAL Seminar: The Role of Explicit Regularization in Overparameterized Neural Networks

11/19/2020

Danica Sutherland

Toyota Technological Institute at Chicago

Trying to Understand and Improve Deep Learning: When can Uniform Convergence and Invariant Risk Minimization Help?

11/20/2020

Oriol Vinyals Google DeepMind Colloquium: Model-free vs Model-based Reinforcement Learning

11/30/2020

Edgar Dobriban University of Pennsylvania

IDEAL Seminar: On the Statistical Foundations of Adversarially Robust Learning

12/1/2020

Andrea Montanari

Stanford University IDEAL Seminar: The Generalization Error of Random Feature and Neural Tangent Models

12/3/2020

Andrew Gordon Wilson

New York University How Do We Build Models That Learn and Generalize?

12/14/2020

Frederic Koehler Massachusetts Institute of Technology

Learning Some Ill-Conditioned Gaussian Graphical Models

1/8/2021

Sitan Chen Massachusetts Institute of Technology

Learning When Gradient Descent Fails 1/11/2021

Soheil Behnezhad

University of Maryland Recent Advances in Large-Scale Graph Algorithms

1/13/21

Wenhu Chen University of California at Santa Barbara

Knowledge-Grounded Natural Language Processing

1/14/21

Mina Karzand Toyota Technological Institute at Chicago

Online Learning of Structured Matrices 1/15/21

David Rosen Massachusetts Institute of Technology

Provably Sound Perception for Reliable Autonomy

1/20/21

Najoung Kim Johns Hopkins University

What Aspects of Meaning are Missing from Current Natural Language Understanding Systems?

1/21/21

Bradly Stadie Toyota Technological Institute at Chicago

Thoughts on “The Bitter Lesson.” Scaling Learning and Search in Robotics.

1/22/21

Percy Liang Stanford University Surprises in the Quest for Robust Machine Learning

1/25/21

Tolga Birdal Stanford University Non-Euclidean Machine Learning for 3D Computer Vision

1/26/21

Melanie Weber Princeton University Geometric Methods for Machine Learning and Optimization

1/27/21

Huda Khayrallah Johns Hopkins University

Machine Translation for All: Improving Machine Translation in Low Resource, Domain Mismatch, and Low Resource Settings

1/28/21

Saeed Seddighin Toyota Technological Institute at Chicago

Dynamic Longest Increasing Subsequence and the Erd\”{o}s-Szekeres Partitioning Problem

1/29/21

Emily Denton Google Rethinking machine learning data 2/1/21Mahsa Derakhshan

University of Maryland Algorithms for Markets: Matching and Pricing 2/3/21

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Luiz Chamon University of Pennsylvania

Learning under Requirements 2/4/21

Greg Shakhnarovich

Toyota Technological Institute at Chicago

Making Do With Few Pixels: Results in Learning Super-Resolution

2/5/21

Pedro Morgado University of California, San Diego

Learning to See and Hear from Audio-Visual Co-occurrence

2/8/21

Stephen Mussmann

Stanford University Bridging Theory and Practice in Active Learning 2/11/21

Ian Abraham Carnegie Mellon University

Runtime Active Learning for Reactive Robotics 2/12/21

Anton Bankevich University of California, San Diego

Interpretability and Higher-order Generalization in Deep Learning: Integrated models of Genomics, Evolution and the Brain

2/15/21

Jonathan Warrell Yale University Interpretability and Higher-order Generalization in Deep Learning: Integrated models of Genomics, Evolution and the Brain

2/16/21

Amirali Aghazadeh

University of California, Berkeley

Inferring Biological Functions with Explainable Algorithms

2/17/21

Maarten Sap University of Washington

Positive AI with Social Commonsense Models 2/18/21

Michael Yu Toyota Technological Institute at Chicago

Modeling the Language of Microbial Genomes 2/19/21

Daniel Fried University of California, Berkeley

Learning Grounded Pragmatic Communication 2/22/21

Mariya Toneva Carnegie Mellon University

Data-Driven Transfer of Insight between Brains and AI Systems

2/23/21

Hongyuan Mei Johns Hopkins University

Probabilistic Modeling for Event Sequences 2/24/21

Swabha Swayamdipta

Allen Institute for AI Addressing Biases for Robust, Generalizable AI 2/25/21

Nicole Wein Massachusetts Institute of Technology

Approximating the Diameter of a Graph 2/26/21

Dylan Foster Massachusetts Institute of Technology

Bridging Learning and Decision Making 3/1/21

Kartik Goyal Carnegie Mellon University

Revisiting Training and Decoding in Neural Sequence Models

3/3/21

Raymond Yeh University of Illinois at Urbana-Champaign

Extracting Structures from Data: The Black-Box, the Manual and the Discovered

3/4/21

Kevin Gimpel Toyota Technological Institute at Chicago

Natural Language Processing Beyond 512 Tokens

3/5/21

Yi Zhang Princeton University Advancing Deep Learning by Integrating Theory and Empirics

3/8/21

Aditi Raghunathan

Stanford University Rethinking the Role of Data in Robust Machine Learning

3/10/21

Kira Goldner Columbia University Mechanism Design for Social Good 3/11/21Sam Wiseman Toyota Technological

Institute at ChicagoNLP Structured Prediction with Nearest Neighbors

3/12/21

Bryan Wilder Harvard University AI for Population Health: Melding Data and Algorithms on Networks

3/15/21

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Ahmed Abbas The Jackson Laboratory (JAX)

Integrating Hi-C and FISHdata for Modeling of the 3D Organization of Chromosomes

3/17/21

Lingxiao Wang University of California, Los Angeles

Towards Efficient and Effective Privacy-Preserving Machine Learning

3/18/21

Jonathan Frankle

Massachusetts Institute of Technology

The Lottery Ticket Hypothesis: On Sparse, Trainable Neural Networks

3/19/21

Siddharth Bhandari

Tata Institute of Fundamental Research, India

Sandwich to Sample Exactly 3/24/21

Wei Hu Princeton University Opening the Black Box: Towards Theoretical Understanding of Deep Learning

3/29/21

Filip Hanzely Toyota Technological Institute at Chicago

Personalized Federated Learning: A Unified Framework and Universal Optimization Techniques

4/9/21

Pietro Perona California Institute of Technology

Distinguished Lecture: A Sense for Number and Quantity as an Emergent Property of a Manipulating Agent

4/15/21

Matt Walter Toyota Technological Institute at Chicago

Research at TTIC: No Title 4/16/21

Arturs Backurs Toyota Technological Institute at Chicago

Research at TTIC: Faster Kernel Matrix Algebra via Density Estimation

4/23/21

Brian Bullins Toyota Technological Institute at Chicago

A Stochastic Newton Algorithm for Distributed Convex Optimization

4/30/21

Maria Chudnovsky

Princeton University Distinguished Lecture: Induced Subgraphs and Tree Decompositions

5/5/21

Julia Chuzhoy Toyota Technological Institute at Chicago

Decremental All-Pairs Shortest Paths in Deterministic Near-Linear Time

5/7/21

Ron Dror Stanford University Learning and Simulating Atomic-Level Biomolecular Structure

5/10/21

Blake Woodworth

Toyota Technological Institute at Chicago

Thesis Defense: The Minimax Complexity of Distributed Optimization

5/13/21

Yury Makarychev

Toyota Technological Institute at Chicago

Kirszbraun theorem, its generalizations and applications

5/14/21

Derek Reiman University of Illinois at Chicago

Deep Learning Frameworks for Multi-omics Analyses of the Microbiome in Disease Studies

5/14/21

Karen Livescu Toyota Technological Institute at Chicago

Pre-training speech models, from the shallow end to the deep end

5/21/21

Subhash Khot New York University Hardness of Approximation: From the PCP Theorem to the 2-to-2 Games Theorem

5/24/21

Lifu Tu Toyota Technological Institute at Chicago

Thesis Defense: Learning Energy-Based Approximate Inference Networks for Structured Applications in NLP

5/27/21

Madhur Tulsiani Toyota Technological Institute at Chicago

Constraint Satisfaction and High-Dimensional Expansion: Algorithms and Lower Bounds

6/4/21

Chenhao Tan The University of Chicago

Characterizing the “Value” of Information 6/7/21

Daniela Rus Massachusetts Institute of Technology

Distinguished Lecture: Learning Risk and Social Behavior in Mixed Human-Autonomous Vehicles Systems

6/14/21

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Workshops

Women in Theoretical Machine Learning Symposium

[Friday, April 9, 2021] TTIC co-sponsored a symposium on women in theoretical machine learning along with the IAS School of Mathematics and the Institute for Mathematical and Statistical Innovation (IMSI).

The goals of this symposium were to encourage collaborations, tighten relationships, and strengthen connections for female researchers in theoretical machine learning, computer science, applied mathematics, and statistical inference.

This event offered an opportunity to enhance professional skills in a friendly environment. Participation was encouraged from graduate students, postdoctoral researchers, professors, and researchers from the industry. As the symposium was held virtually, worldwide participants were welcome.

Speakers included Jelena Diakonikolas (University of Wisconsin - Madison), Stefanie Jegelka (Massachusetts Institute of Technology), Po-Ling Loh (University of Wisconsin - Madison), Caroline Uhler (Massachusetts Institute of Technology), and Rachel Ward (The University of Texas at Austin). The event is being organized by Xiaoxia Wu (TTIC and University of Chicago) and Mina Karzand (TTIC), and advised by Rebecca Willett (University of Chicago).

The Women in Theoretical Machine Learning Symposium preceded IMSI’s workshop on The Multifaceted Complexity of Machine Learning, which took place (virtually) from April 12-16, 2021.

The Multifaceted Complexity of Machine Learning

[April 12-16, 2021] Modern machine learning methods, coupled with new optimization and statistical inference strategies, have demonstrated an unprecedented potential to solve challenging problems in computer vision, natural language processing, healthcare, agriculture, and other application areas. However, foundational understanding regarding how and when certain methods are adequate to use and most effective in solving tasks of interest is still emerging.

A central question at the heart of this endeavor is to understand the different facets of the complexity of machine learning tasks. These include sample complexity, computational complexity, Kolmogorov complexity, oracle complexity, memory complexity, model complexity, and the stationarity of the learning problem. This workshop will focus on developing a better understanding of these different types of complexity within machine learning, how they can be jointly leveraged to understand the solvability of learning problems, and fundamental trade-offs among them.

Organizers: Avrim Blum (TTIC), Olgica Milenkovic (Electrical and Computer Engineering, University of Illinois at Urbana-Champaign), Lev Reyzin (Mathematics, University of Illinois at Chicago), Matus Telgarsky (Computer Science, University of Illinois at Urbana-Champaign), Rebecca Willett (Statistics and Computer Science, University of Chicago)

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New Horizons in Theoretical Computer Science Summer School

[May 31-June 4 2021] New Horizons in Theoretical Computer Science was a week-long online summer school which exposed undergraduates to exciting research in the area of theoretical computer science and its applications. The school contained several mini-courses from top researchers in the field.

The course was free of charge, and welcomed applications from undergraduates majoring in computer science or related fields. Applications were particularly encouraged from students who are members of groups that are currently underrepresented in the field of theoretical computer science.

Organizers: Madhur Tulsiani (TTIC), Boaz Barak (Harvard University), Shuchi Chawla (University of Texas at Austin), the Committee for the Advancement of Theoretical Computer Science (CATCS) of the ACM Special Interest Group on Algorithms and Computation Theory (SIGACT)

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Distinguished Lecture Series 2021

Daniela RusMonday, June 14, 2021Director of the MIT Computer Science and Artificial Intelligence Laboratory, Massachusetts Institute of TechnologyAndrew and Erna Viterbi Professor in the Department of Electrical Engineering and Computer Science, Massachusetts Institute of TechnologyTalk Title: "Learning Risk and Social Behavior in Mixed Human-Autonomous Vehicles Systems”

Pietro PeronaThursday, April 15, 2021Allen E. Puckett Professor of Electrical Engineering and Computation and Neural Systems, California Institute of TechnologyDirector of the National Science Foundation Engineering Research Center in Neuromorphic Systems Engineering, California Institute of TechnologyTalk Title: “A sense for number and quantity as an emergent property of a manipulating agent”

Maria ChudnovskyWednesday, May 5, 2021Professor, Department of Mathematics, Princeton UniversityTalk Title: “Induced Subgraphs and Tree Decompositions”

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© Patsy McEnroe | Design by Gensler

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Education

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The TTIC PhD Program is designed to prepare students for modern academic or research careers in computer science. To complete the program, a student must make an original and significant contribution to the field of computer science, conducting high-level, responsible, and original research that culminates in a doctoral thesis which can be successfully defended in a public forum and published. In addition to the thesis, there are course, experiential, and examination requirements to complete the program. The main component of the program is the process by which the student learns to do quality research and becomes a part of the academic community. As part of the associated partnership between TTIC and the University of Chicago, students of TTIC can take and receive credit for courses through the University of Chicago, and University of Chicago students can take advantage of classes that TTIC offers as well. Students of both institutions have taken full advan-tage of this opportunity. TTIC students also have full access to the University of Chicago library system, athletic facilities, the student health center, and transportation on campus. TTIC students enjoy the benefits and great rewards of an intimate learning, study, and research setting, exposure to state-of-the-art re-search, opportunities in the greater computer science community, and still maintain the traditional shared experiences that come with a large university.

The PhD Program

A global pandemic and remote operations did not deter TTIC from celebrating the accomplishments of its students. TTIC awarded 3 doctoral diplomas at the first-ever virtual diploma ceremony in September 2020 to: Takeshi Onishi, who studied under Professor David McAllester, with research interests in machine learning and natural language processing. Takeshi is currently employed by Toyota Motor Corporation.

Siqi Sun, who studied under Professor Jinbo Xu, with research interests in machine learning, mathematical modeling, and its application in bioinformatics. Siqi is currently employed as a Researcher at Microsoft.

Hai Wang, who studied under Professor David McAllester, with research interests in large-scale machine learning, deep learning and optimization. Hai is currently employed by JD.com at Mountain View.

TTIC expects four PhD Candidates to be eligible for doctoral degrees in the September 2021 diplomaceremony. Students Freda Shi, Ziwei Xie, Jiading Fang, Takuma Yoneda, Xiao Luo, Ben Lai, and Shashank Srivastava successfully fulfilled all requirements to complete the Master’s portion of the PhD Program, and received master’s diplomas from the institute at the September 2020 virtual diploma ceremony at the start of the academic year.

The remote format of the ceremony allowed TTIC to secure some meaningful speakers for the occasion. Trustee Dr. Charles Isbell, also Professor and Dean at Georgia Institute of Technology College of Computing gave the convocation opening address. TTIC Alumnus Dr. Behnam Neyshabur gave a second address.

Graduates, Diplomas, and Awards

Ankita Pasad was awarded the 2020 Outstanding Teaching Assistant Award at the ceremony, for her exceptional dedication to the course TTIC 31110 Speech Technologies (taught by Professor Karen Livescu in Spring Quarter 2020). The annual award was created in 2019 to recognize outstanding performance of teaching assistants (TAs) of courses at the Toyota Technological Institute at Chicago. Students enrolled in TTIC courses may nominate the course TA(s) for the award throughout the academic year. An award committee reviews nominations and selects a winner. A TA Award plaque displays the names of award recipients.

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Quality CurriculumTTIC instructors serve the TTIC student population in their courses, and under the TTIC-University of Chicago Agreement, University students may enroll in TTIC’s courses and receive credit through the University, and vice-versa. TTIC views this as part of serving the Education Mission of the Institute. The amount of University students who register for TTIC courses continues to increase.

TTIC instructors are proud to offer a quality curriculum and rigorous courses to institute PhD students and the students from the University who take part. The increase in course enrollments, and with that, course delivery demands, was a consideration in TTIC’s recently completed facility renovation and expansion.

TTIC Students

UChicago Students

Full financial support is offered to all enrolled students in good academic standing, in residence, and making progress in the program, guaranteed for up to five years.

The tuition for an academic year is $30,000. All students at TTIC may expect to receive financial support that covers tuition, health services, health insurance and student services fees, a new student equipment allowance, and a stipend paid for research assistance, provided they remain full-time and in good academic standing.

Financial Support for Students

Enrollment Numbers for TTIC Courses

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TTIC moved from fully in-person to fully remote operation in March 2020 due to the escalating public health risk inflicted by the COVID-19 global pandemic, and mitigation restrictions from the City of Chicago and State of Illinois. For the safety of the TTIC community, operations remained remote to begin the 2020-21 academic year.

Returning students and faculty demonstrated a dedication and commitment to pursuing study and research. Students moved forward with courses, qualifying exams, thesis proposals and remote internships throughout the academic year. Faculty found ways to encourage students whether it was via online meetings and course instruction, or advising sessions outdoors at a picnic table on campus.

Four ambitious new students matriculated to the PhD program in autumn 2020 and began their studies remotely. These students navigated a new PhD program, connected with new advisors, instructors and the TTIC community and met the challenges of this time. We look forward to welcoming them in-person in the coming year, along with the additional three students who were accepted for autumn 2020 matriculation but had to defer enrollment to autumn 2021 due to travel restrictions, high infection rates and closed embassies around the world.

Remote course delivery did not deter Research Assistant Profesor Dr. Sepideh Mahabadi from moving ahead with her new course in the spring quarter 2021, Special Topics: Algorithms for Massive Data. Students found this new course valuable and course evaluation comments included: “the course helped me find research directions that I want to pursue this summer and maybe later in life too. Also, the topics were modern.” and, “The material was really interesting. The lectures were illuminating.” We are grateful to Dr. Mahabadi for choosing to move ahead with this quality course despite the added challenge of remote format.

In the spring of 2021, vaccines became generally accessible to most adults in the U.S. and it was after this that TTIC’s COVID-19 Response Group arranged protocols and safety measures which allowed members of the TTIC community increased access to the facilities and the capability to opt-in to some in-person activities.

TTIC’s PhD program is an in-person, in-residence program, and the institute aims to return to that format as soon as it is safe to do so, with our eyes on autumn 2021. TTIC continues to be encouraged by the persistence with which students continue to produce great work, make progress, and move towards degree completion. We are grateful to the faculty and staff who support the institute’s mission and these students.

Academics in the COVID-19 Pandemic

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In Spring Quarter 2021, Research Assistant Prof. Sepideh Mahabadi taught a new course, “Special Topics: Algorithms for Massive Data.” This course covered the theoretical aspects of computation over massive data.

While classical algorithms can be too slow, or require too much space on big data, in this course students focused on designing algorithms that are specifically tailored for large datasets. Moreover, they learned about different computational models that capture various aspects of computation over massive data, such as streaming algorithms and sub-linear time algorithms.

The course will also offer some of the algorithmic techniques and tools for solving problems over massive data, such as sampling, sketching, dimensionality reduction, and computing efficient summaries of the data (such as core-sets).

“I decided to teach this course to provide an opportunity for interested students to become familiar with the area of algorithms for big data, use the techniques developed in the course in other areas, be able to read recent papers in the area, and ideally start conducting research in this area,” said Mahabadi.

New Courses

In Spring Quarter 2021, Prof. Greg Shakhnarovich taught “Introduction to Computer Vision,” an original course serving as an introduction to the principles and practice of computer vision, providing an in-depth survey of topics involved in major tasks in moden vision, and offering hands-on experience in implementing some of these principles.

Topics covered by this course included image formation, representation, and compression, representation and perception of color, filtering and edge detection, image features, detectors, and interest point operators, model fitting, RANSAC and Hough transform, stereo and multi-view geometry, and camera calibration among other areas.

Students gained familiarity with models for image formation and image analysis, major methods for image processing, alignment, matching, knowledge of principles and methods of 3D reconstruction from images and videos, and the ability to build and diagnose implementations of these methods. They also obtained an understanding of modern methods for object and scene categorization from images, and ability to build and diagnose implementations of such methods.

Special Topics: Algorithms for Massive DataInstructor: Sepideh Mahabadi

Introduction to Computer VisionInstructor: Greg Shakhnarovich

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Student Publications, Posters, and AbstractsPedro Savarese, David McAllester, Sudarshan Babu, and Michael Maire. “Domain-Independent Dominance of Adaptive Methods.” Paper presented at the Conference on Computer Vision and Pattern Recognition (CVPR), June 2021.

Mingda Chen, Zewei Chu, Karl Stratos, and Kevin Gimpel. “Mining Knowledge for Natural Language Inference from Wikipedia Categories.” Paper presented at the Conference on Empirical Methods in Natural Language Processing and 9th International Joint Conference on Natural Language Processing (EMNLP), November 2020.

Mingda Chen, Sam Wiseman, and Kevin Gimpel. “Exemplar-Controllable Paraphrasing and Translation using Bitext.” Preprint, submitted October 2020. arXiv:2010.05856.

Mingda Chen, Zewei Chu, Sam Wiseman, and Kevin Gimpel. “SummScreen: A Dataset for Abstractive Screenplay Summarization.” Preprint, submitted April 2021. arXiv:2104.07091.

Weili Zheng, Geeth Kavya Minama Reddy, Falcon Dai, Ayushi Chandramani, David Brang, Scott Hunter, Michael H. Kohrman, Sandra Rose, Marvin Rossi, James Tao, et al. “Chasing Language Through the Brain: Successive Parallel Networks.” Clinical Neurophysiology 132, no.1 (January 2021):80-93. Doi:j.clinph.2020.10.007.

Falcon Dai and Matthew R. Walter. “Loop Estimator for Discounted Values in Markov Reward Processes.” Paper presented at the Association for the Advancement of Aritificial Intelligence (AAAI), February 2021.

Jacopo Tani, Andrea F. Daniele, Gianmarco Bernasconi, Amaury Camus, Aleksandar Petrov, Anthony Courchesne, Bhairav Mehta, Rohit Suri, Tomasz Zaluska, Matthew R. Walter, et al. “Integrated Benchmarking and Design for Reproducible and Accessible Evaluation of Robotic Agents.” Paper presented at the IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS 2020), October 2020.

Matthew R. Walter, Siddharth Patki, Andrea F Daniele, Ethan Fahnestock, Felix Duvallet, Sachithra Hemachandra, Jean Oh, Anthony Stentz, Nicholas Roy, and Thomas M. Howard. “Language Understanding for Field and Service Robots in a Priori Unknown Environments.” Preprint, submitted May 2021. arXiv:2105.10396.

Lingyu Gao, Kevin Gimpel, and Arnar Jensson. “Distractor Analysis and Selection for Multiple-Choice Cloze Questions for Second-Language Learners.” Paper presented at the Workshop on Innovative Use of NLP for Building Educational Applications, July 2020.

Shubham Toshniwal, Freda (Haoyue) Shi, Bowen Shi, Lingyu Gao, Karen Livescu, and Kevin Gimpel. “A Cross-Task Analysis of Text Span Representations.” Paper presented at the Workshop on Representation Learning for NLP, July 2020.

Buddhima Gamlath and Vadim Grinberg. “Approximating Star Cover Problems.” Paper presented at the International Conference on Approximation Algorithms for Combinatorial Optimization Problems (APPROX), August 2020.

Ben Lai, Sheng Qian, Hanwen Zhang, Siwei Zhang, Alena Kozlova, Jubao Duan, Xin He, and Jinbo Xu. “Predicting regulatory functions of genetic variants in the context of neurodevelopment via deep transfer learning.” Talk given at the American Society of Human Genetics Annual Meeting, October 2020.

Jie Yuan, Ben Lai, and Itsik Pe’er. “Reversing Transcriptome-Wide Association Studies to improve expression Quantitative Trait Loci associations.” Preprint, submitted December 2020. doi:10.1101/2020.12.27.424460.

Gene Li, Pritish Kamath, Dylan J. Foster, and Nathan Srebro. “Eluder Dimension and Generalized Rank.” Preprint, submitted April 2021. arXiv:2104.06970.

Jiahao Li, Changhao Zhang, Ziyao Xu, Hangning Zhou, and Chi Zhang. “Iterative Distance-Aware Similarity Matrix Convolution with MutualSupervised Point Elimination for Efficient Point Cloud Registration.” Paper presented at the European Conference on Computer Vision (ECCV), August 2020.

Kunal Nagpal, Davis Foote, Fraser Tan, Yun Liu, Po-Hsuan Cameron Chen, Dave Steiner, Naren Manoj, Niels Olson, Jenny L. Smith, Arash Mohtashamian, et al. “A Deep Learning System with Subspecialist-Level Accuracy for Gleason Grading Prostate Biopsies.” JAMA Oncology 6, no. 9:1372-1380. doi:10.1001/jamaoncol.2020.2485.

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Omar Montasser, Steve Hanneke, and Nathan Srebro. “Adversarially Robust Learning with Unknown Perturbation Sets.” Paper presented at the Conference on Learning Theory (COLT), August 2021.

Pedro Savarese, Sunnie S. Y. Kim, Michael Maire, Greg Shakhnarovich, and David McAllester. “Information-Theoreic Unsupervised Segmentation with Inpainting Error Maximization.” Paper presented at the Conference on Computer Vision and Pattern Recognition (CVPR), June 2021.

Yao Lu, Soren Pirk, Jan Dlabal, Anthony Brohan, Ankita Pasad, Zhao Chen, Vincent Casser, Anelia Angelova, and Ariel Gordon. “Taskology: Utilizing Task Relations at Scale.” Poster presented at the Conference on Computer Vision and Pattern Recognition (CVPR), June 2021.

Blake Woodworth, Kumar Kshitij Patel, Sebastian U Stich, Zhen Dai, Brian Bullins, H Brendan McMahan, Ohad Shamir, and Nathan Srebro. “Is Local SGD Better than Minibatch SGD?” Poster presented at the International Conference on Machine Learning (ICML), virtual, July 2020.

Blake Woodworth, Kumar Kshitij Patel, and Nathan Srebro. “Minibatch vs Local SGD for Heterogeneous Distributed Learning.” Poster presented at the Conference on Neural Information Processing Systems (NeurIPS), December 2020.

Niklas Funk, Charles Schaff, Rishabh Madan, Takuma Yoneda, Julen Urain De Jesus, Joe Watson, Ethan K Gordon, Felix Widmaier, Stefan Bauer, Siddhartha S Srinivasa, et al. “Benchmarking Structured Policies and Policy Optimization for Real-World Dexterous Object Manipulation.” Preprint, submitted May 2021. arXiv:2105.02087.

Takuma Yoneda, Charles Schaff, Takahiro Maeda, and Matthew Walter. “Grasp and Motion Planning for Dexterous Manipulation for the Real Robot Challenge.” Preprint, submitted January 2021. arXiv:2101.02842.

Yushi Hu, Shane Settle, and Karen Livescu. “Multilingual Jointly Trained Acoustic and Written Word Embeddings.” Paper presented at the Annual Conference of the International Speech Communication Association (Interspeech), October 2020.

Yushi Hu, Shane Settle, and Karen Livescu. “Acoustically grounded word embeddings for improved acoustics-to-word speech recognition.” Paper presented at the IEEE Spoken Language Technology Workshop (SLT), January 2021.

Bowen Shi, Shane Settle, and Karen Livescu. “Whole-Word Segmental Speech Recognition with Acoustic Word Embeddings.” Paper presented at the IEEE Spoken Language Technology Workshop (SLT), January 2021.

Rémy Degenne, Han Shao, and Wouter M. Koolen. “Structure adaptive algorithms for stochastic bandits.” Paper presented at the International Conference on Machine Learning (ICML), July 2020.

Avrim Blum and Han Shao. “Online learning with preliminary and secondary losses.” Paper presented at the Conference on Neural Information Processing Systems (NeurIPS), December 2020.

Chieh-Chi Kao, Bowen Shi, Ming Sun, and Chao Wang. “A Joint Framework for Audio Tagging and Weakly Supervised Acoustic Event Detection Using DenseNet with Global Average Pooling.” Paper presented at the Annual Conference of the International Speech Communication Association (Interspeech), October 2020.

Bowen Shi, Diane Brentari, Greg Shakhnarovich, and Karen Livescu. “Fingerspelling detection in American sign language.” Paper presented at the Conference on Computer Vision and Pattern Recognition (CVPR), June 2021.

Freda (Haoyue) Shi, Karen Livescu, and Kevin Gimpel. “On the Role of Supervision in Unsupervised Constituency Parsing.” Paper presented at the Conference on Empirical Methods in Natural Language Processing and 9th International Joint Conference on Natural Language Processing (EMNLP), November 2020.

Keerthiram Murugesan, Mattia Atzeni, Pavan Kapanipathi, Pushkar Shukla, Sadhana Kumaravel, Gerald Tesauro, Kartik Talamadupula, Mrinmaya Sachan, and Murray Campbell. “Text-based RL Agents with Commonsense Knowledge: New Challenges, Environments and Baselines.” Paper presented at the Conference on Artificial Intelligence (AAAI), February 2021.

Fernando Granha Jeronimo, Dylan Quintana, Shashank Srivastava, and Madhur Tulsiani. “Unique Decoding of Explicit β-biased codes near Gilbert-Varshamov Bound.” Paper presented at the IEEE Symposium on Foundations of Computer Science (FOCS), November 2020.

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Mathew Rogers, Ritabrata Ray, and Shashank Srivastava. “Fractional Cross Intersecting Families.” Graphs and Combinatorics 37, no.3:1-14. doi:10.1007/s00373-020-02257-7.

Fernando Granha Jeronimo, Shashank Srivastava, and Madhur Tulsiani. “Near-Linear Time Decoding of Ta-Shma’s Codes via Splittable Regularity.” Paper presented at the ACM Symposium on Theory of Computing (STOC), June 2021.

Shubham Toshniwal, Sam Wiseman, Karen Livescu, and Kevin Gimpel. “Learning Chess Blindfolded: Evaluating Language Models on State Tracking.” Preprint, submitted on February 26, 2021. arXiv:2102.13249.

Shubham Toshniwal, Sam Wiseman, Allyson Ettinger, Karen Livescu, and Kevin Gimpel. “Learning to Ignore: Long Document Coreference with Bounded Memory Neural Networks.” Talk given at the Conference on Empirical Methods in Natural Language Processing and 9th International Joint Conference on Natural Language Processing (EMNLP), November 2020.

Igor Vasiljevic, Vitor Guizilini, Rares Ambrus, Sudeep Pillai, Wolfram Burgard, Greg Shakhnarovich, and Adrien Gaidon. “Neural Ray Surfaces for Self-Supervised Learning of Depth and Ego-motion.” Paper presented at the International Conference on 3D Vision (3DV), virtual, November 2020.

Takuma Yoneda, Matthew Walter, and Jason Naradowsky. “Pow-Wow: A Dataset and Study on Collaborative Communication in Pommerman.” Poster presented at the International Conference on Machine Learning (ICML), July 2020.

Davis Yoshida, Allyson Ettinger, and Kevin Gimpel. “Adding Recurrence to Pretrained Transformers for Improved Efficiency and Context Size.” Preprint, submitted August 2020. arXiv:2008.07027.

In the fall of 2004, TTIC matriculated its first three students. The 2020-21 academic year began with 43 students, 4 who enrolled as first time new students for Autumn 2020.

Student Admissions and Student Body Growth

AdmissionsYear

Total Applicants

Applicants Admitted

Enrolled next Fall

2021 227 17 5 planned

2020 229 26 4

2019 225 27 9

2018 207 26 8

2017 135 23 8

2016 123 23 4

2015 90 22 9

2014 83 6 2

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Notes from theChief Financial Officer

Fiscal year 2020-2021 mirrored the previous fiscal year – remote operations while progressing our growth related to infrastructure and financial assets. Like the previous fiscal year, TTIC did not experience adverse fiscal repercussions from another year at the mercy of the global pandemic.

In the fall of 2020, we completed renovation of space leased from University of Chicago, including the newly leased 5,000 sq feet. The renovation project was completed under budget, and the overall feedback from the community has been positive. As a result of the renovation, TTIC has updated classrooms, conference rooms, and collaboration spaces with upgraded AV capabilities. There is enough workspace to meet the future planned growth in addition to a remodeled café space. The Robotics Lab also significantly increased in size and was outfitted to support the needs

of the Robotics faculty and students.

In November 2020, the Board of Trustees adopted a new Investment Policy Statement (IPS). The IPS outlines the investment policy, strategy, and asset allocation necessary to meet TTIC’s operating needs as it continues to grow while maintaining a tolerable level of risk. The implementation of the new asset allocation began in early 2021, and it mostly complete by the end of the fiscal year. As a result of the asset allocation restructuring, TTIC’s investment portfolio is currently managed by two different investment managers, University of Chicago and Aon Investments, each managing approximately half of the investable assets.

TTIC’s investment portfolio experienced extraordinary capital growth in this fiscal year, specifically related to TTIC’s investments University of Chicago’s Total Return Investment Pool (TRIP). All asset classes performed well but alternative investments residing in the TRIP fund performed exceptionally well.

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30% of TTIC’s operating revenue is derived from external grants. TTIC’s research was able to continue remotely, so there was no impact on grant revenue due to COVID-19. Except for tuition paid by UChicago, the remainder of TTIC’s operating revenue is the distribution of investment return, which to date, has not been significantly impacted by COVID-19. Overall, operating revenue was close to budget at just over $11 mil.

Regarding operating expense, TTIC continues to operate remotely so there is less spending on travel and events-related expenses. TTIC continued paying a monthly telecommunication stipend of $100 for the entire community to facilitate remote work. Overall, TTIC ended the fiscal year with an operating surplus of over $700,000.

TTIC is fortunate to be in a strong financial position with approximately $75 mil in unrestricted financial assets. Not only is TTIC poised to successfully navigate the challenges of the global pandemic, but we are also in a strong financial position to continue our growth plan.

In conclusion, I would like to thank the TTIC administrative staff for their hard work, commitment, and teamwork. I am looking forward to returning from remote operations to a newly renovated campus – and celebrating with the entire TTIC Community!

Operating Results

Jessica JacobsonChief Financial Officer

Financial Reports

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Toyota Technological Institute at Chicago

Statement of Financial PositionJune 30, 2021 and 2020

2021 2020

Assets

Current AssetsCash and cash equivalents $ 1,481,352 $ 13,901,797Receivables:

Miscellaneous receivable 526,684 85,165Grants receivable 926,273 737,034Due from TTI (Note 9) 1,646 1,299Interest receivable 43,426 1,169,499Investment distribution receivable 5,462,834 4,089,694

Prepaid expenses and other current assets 34,898 330,690

Investments 287,565,842 243,833,782

Furniture and Equipment - Net (Note 4) 6,128,092 6,830,440

Total assets $ 302,171,047 $ 270,979,400

Liabilities and Net Assets

Current LiabilitiesAccounts payable $ 326,826 $ 125,888Accrued expenses 820,480 917,640Accrued lease liability (Note 7) 288,057 284,191

Total liabilities 1,435,363 1,327,719

Net Assets (Note 5)Without donor restrictions 74,717,884 67,932,936

With donor restrictions 226,017,800 201,718,745

Total net assets 300,735,684 269,651,681

Total liabilities and net assets $ 302,171,047 $ 270,979,400

See notes to financial statements. 3

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Toyota Technological Institute at Chicago

Statement of Activities and Changes in Net AssetsYears Ended June 30, 2021 and 2020

2021 2020Without Donor

RestrictionsWith DonorRestrictions Total

Without DonorRestrictions

With DonorRestrictions Total

Revenue, Gains, and Other SupportStudent tuition and fees:

Student tuition and fees $ 1,355,414 $ - $ 1,355,414 $ 1,357,088 $ - $ 1,357,088Scholarships (1,320,000) - (1,320,000) (1,290,000) - (1,290,000)

Total net student tuitionand fees 35,414 - 35,414 67,088 - 67,088

Federal grants and contracts 3,625,811 - 3,625,811 4,374,202 - 4,374,202Other interest 14,213 - 14,213 93,657 - 93,657Net realized and unrealized gains on

investments 7,822,800 27,931,091 35,753,891 1,807,887 5,822,489 7,630,376Investment income (loss) - Net of

investment fees 363,713 2,480,314 2,844,027 (245,637) 4,552,517 4,306,880Net assets released from restrictions 6,112,350 (6,112,350) - 7,244,282 (7,244,282) -

Total revenue, gains,and other support 17,974,301 24,299,055 42,273,356 13,341,479 3,130,724 16,472,203

ExpensesEducation and research expenses -

Instruction 8,863,900 - 8,863,900 8,226,644 - 8,226,644Management and general expenses -

Institutional support 2,325,453 - 2,325,453 2,453,925 - 2,453,925

Total expenses 11,189,353 - 11,189,353 10,680,569 - 10,680,569

Increase in Net Assets 6,784,948 24,299,055 31,084,003 2,660,910 3,130,724 5,791,634

Net Assets - Beginning of year 67,932,936 201,718,745 269,651,681 65,272,026 198,588,021 263,860,047

Net Assets - End of year $ 74,717,884 $ 226,017,800 $ 300,735,684 $ 67,932,936 $ 201,718,745 $ 269,651,681

See notes to financial statements. 4

Interns and Visiting Scholars

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TTIC maintains a steady number of interns and visiting scholars who engage in study and research on the premises. Summer 2020 had eighteen visiting scholars from other institutions in the U.S. and abroad who came to the Institute to work on research projects in collaboration with TTIC faculty and students. Due to pandemic travel restrictions and closed campus, TTIC faculty could only offer remote engagement for interns over the summer 2020.

Short-term visiting scholars bring interest, energy, and enthusiasm to our academic community, and allow TTIC students access to a broad range of specialties that outside researchers bring with them, along with ideas and culture brought from the visitors’ home institutions. TTIC hopes to be back to the usual intern/ visiting scholar program in the summer of 2021.

Ahmadi, SabaUniversity of Maryland Faculty Host: Avrim Blum

Behnezhad, SoheilUniversity of Maryland Faculty Host: Avrim Blum

Carlson, CharlesUniversity of Colorado, Boulder Faculty Host: Yury Makharychev

Derakhshan, MahsaUniversity of Maryland Faculty Host: Avrim Blum

Ge, RuiquanHangzhou Dianzi University of Technology Faculty Host: Jinbo Xu

Granha, Fernando JeronimoUniversity of Chicago Faculty Host: Madhur Tulsiani

Hou, YifanThe Chinese University of Hong Kong Faculty Host: Mrinmaya Sachan

Jafarov, JafarUniversity of ChicagoFaculty Host: Yury Makharychev

Kim, Suhyoung (Sunnie)Princeton UniversityFaculty Host: Greg Shakhnarovich

Li, Jin JunUniversity of ChicagoFaculty Host: Jinbo Xu

Liu, QingyuanUniversity of MichiganFaculty Host: Jinbo Xu

Liu, Zhuoran (Oliver)Northwestern UniversityFaculty Host: Sam Wiseman

MacKeith, ArthurUniversity of ChicagoFaculty Host: Matt Walter

Qing, Yutong (Tony)Faculty Host: Audrey Sedal

Rogers, Marianna Faculty Host: Audrey Sedal

Saleh, HamedUniversity of Maryland Faculty Host: Saeed Seddighin

Schondorf, EthanFaculty Hosts: Audrey Sedal and Matt Walter

Wu, FandiInstitute of Computing TechnologyChinese Academy of Sciences Faculty Host: Jinbo Xu

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Community Outreach

Addressing Violence Against People of Asian DescentOn March 18, 2021 President Turk released a statement to the TTIC community regarding the recent surge in violence against people of Asian descent. Amplifying the words of the University of Chicago’s Provost Ka Yee C. Lee, and Dean of Students Michele Rasmussen, President Turk shared the following via email:

TTIC stands firmly with UChicago in opposing all bias, racism, and acts of hate and violence. The surge in anti-Asian rhetoric, discrimination, and violence in this country is extremely disturbing. The murders in the Atlanta area were a shock to us all.

To those in the TTIC community who are Asian or of Asian descent, you are integral to TTIC, as our colleagues, students, teachers, leaders, and friends. We value and appreciate you. TTIC stands in solidarity with you.

To all, please consider taking advantage of campus resources if you have experienced bias, feel threatened in any way, or want to seek or offer support. Additionally, don’t hesitate to contact HR Director Amy Minick or myself if you’d like to discuss any issues related to your safety or well-being at TTIC or our commitments to equity and inclusion.

Institute Donation to India in CrisisPhilanthropic giving is something very close to the hearts of many TTIC community members This year when pandemic emergency hit India with alarming impact, the TTIC community chose to help communities in India that have been adversely affected by the ongoing global pandemic. The institute has always had deep academic and social ties to Indian nationals and friends and in this case, the ability to offer aid in this crisis was personal.

The TTIC community collectively donated $1,330 to the Indian Red Cross Society, and the Institute matched with a gift of $780. The total donation was $2,110. $125 was also donated to MilaapWeHopeWeCare’s campaign to support a community in India that was hit especially hard by the pandemic.

The new Diversity, Equity, and Inclusion (DEI) Committee at TTIC was created in August 2020, and has already become very active. The committee consistently seeks out new opportunities to educate our students, faculty, and staff and make TTIC a more inclusive and welcoming place.

In August 2020, the DEI committee conducted a Community Discussion on Diversity survey to gauge attitudes of the TTIC community. In September 2020, they held the TTIC Community Discussion on Diversity, and most attendees thought that TTIC should make attempts to increase diversity.

In October 2020, the TTIC Diversity Values Statement was published, and the faculty hiring and student recruitment processes were reviewed. Among the potential issues identified were the need for improved recruiting and outreach to underrepresented populations, and the potential for unchecked bias affecting the process.

In January 2021, co-chairs Professor Greg Shakhnarovich and Manager of Research Administration Rose Bradford met with University of Chicago leadership to discuss how to better take advantage of their diversity resources.

The DEI committee is composed of students, faculty, and staff members, and welcomes feedback and participation from all members of the TTIC community.

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Promoting Diversity, Equity, and Inclusion

Diversity, Equity, and Inclusion Committee

Rose Bradford, Manager of Research Administration (Co-Chair)

Greg Shakhnarovich, Professor (Co-Chair)

Erica Cocom, Student Services and Admissions Administrator

Lingyu Gao, Student

Sepideh Mahabadi, Research Assistant Professor

David Yunis, Student

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Governance

Board of TrusteesKavita BalaDean, Ann S. Bowers College of Computing and Information Science, Cornell UniversityProfessor, Cornell UniversityCo-Founder, GrokStyleFaculty Fellow, Atkinson Center for a Sustainable FutureTrustee since May 2021

Robert BarnettPartner, Williams & Connolly LLPRanked Number One, Washingtonian Magazine’s list of “Washington’s Best Lawyers.”Executive Committee Member, Williams & Connelly LLPSenior Counsel, Board of Trustees of the John F. Kennedy Center for the Performing Arts. (President-appointed member.)Trustee since April 2006

Juan de PabloVice President for National Laboratories, Science Strategy, Innovation, and Global Initiatives, University of ChicagoLiew Family Professor in Molecular Engineering, University of ChicagoSenior Scientist, Argonne National LaboratoryMember, National Academy of Engineering (NAE)Trustee since May 2021

Sadaoki Furui Chair, Board of Trustees, Toyota Technological Institute at ChicagoFormer President, Toyota Technological Institute at ChicagoProfessor Emeritus, Tokyo Institute of TechnologyFormer Director of University Library, Tokyo Institute of TechnologyFormer Dean of Graduate School of Information Science and Engineering, Tokyo Institute of TechnologyFormer Director of Furui Research Laboratory, NTT Human Interface Laboratories, JapanFormer Director of Speech and Acoustics Laboratory, NTT Human Interface Laboratories, JapanTrustee since April 2013, Chair from July 2019 - May 2021

Eric Grimson Chancellor for Academic Advancement, Massachusetts Institute of TechnologyBernard Gordon Chair of Medical Engineering at MITLecturer on Radiology at Harvard Medical School and at Brigham and Women's HospitalFormer Education Officer for the Dept. of Electrical Engineering and Computer Science at MIT; Associate Department Head; Head of the Depart. of Electrical Engineering and Computer Science.Trustee since July 2015, Chair from May 2021

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Alexis Herman Chair and Chief Executive Officer, New Ventures, LLCAppointed by President Jimmy Carter, became the youngest director of the Women's Bureau in the history of the Labor DepartmentUS 23rd Secretary of Labor and first African American to lead the US Department of LaborFormer member of the National Economic CouncilServes on the boards of: Cummins Inc., Entergy Inc., MGM Mirage, Coca-Cola CompanyFormer chairwoman of the Coca-Cola Company’s Human Resources Task ForceBoard member of the Clinton Bush Haiti FundTrustee since October 2012

Kazuo Hotate President, Toyota Technological Institute (Nagoya, Japan)Member, Science Council of Japan (SCJ)Member, Institute of Electrical and Electronics Engineers (IEEE)Member, Japan Society of Applied Physics (JSAP)Trustee since May 2021

Charles Isbell, Jr. Professor, Executive Associate Dean, College of Computing, Georgia Institute of TechnologyOversaw Georgia Tech’s rollout of online Computer Science Master’s degree, studied by Harvard economists and published in the New York Times, as a whole new way of thinking about the cost of higher educationWork has been featured in the New York Times and the Washington PostTrustee since April 2018

Noboru Kikuchi President, Toyota Central R&D Labs, Inc.Professor Emeritus, Mechanical Engineering, University of MichiganRoger L. McCarthy Professor Emeritus of Mechanical Engineering, University of MichiganA Member of National Academy of Engineering, USADesign and System Engineering Achievement Award, The Japan Society of Mechanical EngineersComputational Mechanics Achievements Award, The Japan Society of Mechanical EngineersExcellence in Research Award, Dept of Mechanical Engineering and Applied Mechanics, The University of MichiganDistinguished Research Award, College of Engineering, University of MichiganTrustee since May 2019

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Yoshihiko MasudaChairman of the Board of Trustees, Toyota School FoundationAdvisor, Toyota Central R&D Labs, Inc. and Toyota MotorChairman of Toyota Central R&D Labs, Inc., 2014-2017Member of Representatives, Society of Automotive Engineers of Japan, Inc., 2009-2011Member of Councils, Toyota School Foundation, 2011- currentMember of Japan Techno-Economics Society Board of Trustees, 2017- currentRecipient of Society of Automotive Engineers (SAE) Fuel and Lubricant Paper Award (1997) and JSAE Technological Contribution Award (2017)Trustee since October 2017

Angela Olinto Dean of the Physical Sciences Division, University of ChicagoAlbert A. Michelson Distinguished Service Professor, Department of Astronomy and Astrophysics; Enrico Fermi Institute; and the College, University of ChicagoPrincipal Investigator of the POEMMA (Probe of Extreme Multi-Messenger Astrophysics) space missionMember of the Pierre Auger ObservatoryFellow, American Physical Society and the American Association for the Advancement of ScienceReceived the Chaire d’Excellence Award of the French Agence Nationale de Recherche, 2006Received the Llewellyn John and Harriet Manchester Quantrell Award for Excellence in Undergrad Teaching, 2001Received the Faculty Award for Excellence in Graduate Teaching, University of Chicago, 2015Trustee since October 2018

Nuria Oliver Chief Scientific Advisor, Vodafone InstituteChief Data Scientist, DataPop AllianceIndependent Director, Board of Directors, BankiaCommissioner for AI and COVID-19, Presidency of ValenciaFounder, ELLIS Alicante FoundationTrustee since November 2020

Mari Ostendorf Endowed Professor of System Design Methodologies and Associate Vice Provost for Research, University of WashingtonHas worked for AT&T Bell Laboratories, BBN Laboratories and Boston UniversityAdjunct Professor in Linguistics and Computer Science and Engineering and served as Associate Dean for Research and Graduate Studies in the College of Engineering, 2009-2012Scottish Informatics and Computer Science Alliance Distinguished Visiting FellowAustralia Fulbright Scholar at Macquarie UniversityHas had 260 publications and recipient of two paper awards, the 2010 IEEE HP Harriett B. Rigas Award, and the 2018 IEEE James L. Flanagan Speech and Audio Processing AwardServed as Editor of IEEE Transactions on Audio, Speech and Language Processing and Computer Speech and Language, as VP Publications on IEEE Signal Processing Society, and served as a member of the IEEE Periodicals Review and Advisory CommitteeFellow of IEEE and ISCA and a 2013-2014 IEEE Signal Processing Society Distinguished LecturerTrustee since October 2017

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Hiroyuki Sakaki Former President, Toyota Technological InstituteAppointed as an associate professor in 1973 at the Institute of Industrial Science, University of Tokyo, promoted to full professor in 1987, and engaged in R&D and education in the area of semiconductor electronics.Professor Emeritus in 2007 Appointed as Vice President of Toyota Technological Institute (Nagoya, Japan) in 2007 and promoted to President in 2010Awarded the National Recognition as a Person of Cultural Merit, Japan Academy Award, Leo Esaki Award, Heinrich Welker Award, Medal of Purple Ribbon from the Emperor of Japan, IEEE David Sarnoff Award, Fujiwara Prize, Japan IBM Science Award, and the Hattori-Hoko AwardTrustee since October 2010

Ivan SamsteinVice President and Chief Financial Officer, University of ChicagoFormer Director of public finance department, Bank of America Merrill Lynch, 2004-2011; Assistant Vice President of public finance, Moody’s Investors Service, 1999-2004Former Chief Financial Officer for Cook County, 2012-2016Had primary responsibility for budget, capital and debt structure for second-largest county government and associated health system in the countryDesigned and led several transformative projects in financial operations, technology, program-based budgeting and performance metric-driven managementLeads integrated strategic financial planning and oversight for the execution of the University’s work in financial analysis and functions, information technology and human resourcesTrustee since April 2018

Balaji SrinivasanExecutive Vice President for Science, Innovation, and Strategy, University of Chicago Deputy Provost, University of Chicago Chief International Officer, University of Chicago Former managing director of Och Ziff Capital Management (India and Hong Kong) Former executive director of Goldman Sachs Hong Kong Former associate director of Jardine Fleming (Singapore, Hong Kong, and Mumbai) Trustee since July 2019

Masatami TakimotoFormer Chair, Board of Trustees, Toyota Technological Institute at ChicagoFormer Chair, Board of Directors & the Board of Trustees, Toyota School FoundationSpecial Advisor, Toyota Central R&D Labs., INC.Former Executive Vice President, Toyota Motor CorporationTrustee since October 2011

Matthew TurkPresident, Toyota Technological Institute at ChicagoProfessor Emeritus, University of California, Santa BarbaraFormer professor and dept. Chair, Department of Computer Science and Media Arts and Technology, UC Santa BarbaraCo-founder, Vision Technology Group at Microsoft ResearchFellow of the IEEE and the IAPRFormer Fulbright-Nokia Distinguished Chair in Information and Communications TechnologiesTrustee since July 2019

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Tatsuro Toyoda | Chair Emeritus

Trustee Departures: Dr. Sadaoki Furui (April 2013 - May 2021) Dr. Balaji Srinivasan (July 2019 - November 2020)Dr. Masatami Takimoto (October 2011- November 2020)

Trustee Appointments: Dr. Nuria Oliver (November 2020) Dr. Kavita Bala (May 2021)Dr. Juan de Pablo (May 2021)Dr. Kazuo Hotate (May 2021)

New Board Chair: Dr. Eric Grimson (Appointed May 2021)

Mark Hogan (Advisor to the Board)Director, Toyota Motor CorporationPresident, Dewey Investments, LLC

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Adam Bohlander, Director of Information Technology Rose Bradford, Manager of Research AdministrationLiz Clay, Communications AdministratorErica Cocom, Student Services and Admissions Administrator Chrissy Coleman, Administrative Director of Graduate Studies and Publications, Accreditation Liaison Officer, Deputy Title IX Coordinator Jessica Jacobson, Chief Financial Officer and Director of OperationsDeree Kobets, Controller Mary Marre, Administrative Assistant Alicia McClarin, Office ManagerAmy Minick, Director of Human Resources and International Affairs, Title IX Coordinator

LeadershipMatthew Turk, President Jessica Jacobson, Chief Financial Officer Chrissy M. Coleman, Secretary of the InstituteAvrim Blum, Chief Academic Officer

Administration

TTIC is committed to providing a respectful and positive environment for all members of its community, free from all forms of discrimination and harassment.

Non-Discrimination Statement

External Advisory Committee Eric Grimson, Chancellor for Academic Advancement and Professor of Computer Science and Engineering, Massachusetts Institute of Technology Takeo Kanade, UA and Helen Whitaker University Professor, Robotics Institute, Carnegie Mellon University Richard Karp, Professor Emeritus, Electrical Engineering and Computer Science, University of California, Berkeley Éva Tardos, Jacob Gould Schurman Professor of Computer Science, Cornell University

The professionals at the Higher Learning Commission

Toyota Motor Corporation

Toyota Technological Institute (Nagoya, Japan)

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Special Thanks

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University of Chicago greater community Booth School of Business Department of Computer Science Department of Mathematics Department of Statistics English Language Institute Faculty and Administration of the Division of Physical Sciences Office of the Bursar Office of International Affairs Office of Investments Office of the Provost Office of the Registrar Staff of the Regenstein and Eckhart LibrariesStudent Wellness Services Facilities Services University Research Administration University IT Services The professionals at the 6045 S. Kenwood Avenue building

6045 S. Kenwood Ave.Chicago, IL 60637

www.ttic.edu

@ttic_connect

@ttic_connect

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