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DTSTART;TZID=America/New_York:20260304T160000
DTEND;TZID=America/New_York:20260304T170000
DTSTAMP:20260503T113337
CREATED:20260108T200326Z
LAST-MODIFIED:20260316T161023Z
UID:10003868-1772640000-1772643600@cmsa.fas.harvard.edu
SUMMARY:2026 Ding Shum Lecture: Sanjeev Arora\, Princeton
DESCRIPTION:2026 Ding Shum Lecture \nDate: March 4\, 2026 \nTime: 4:00 pm \nLocation: Harvard Science Center Hall D & via Zoom Webinar \nSpeaker: Sanjeev Arora\, Princeton \nTitle: How could a Superhuman AI mathematician come about? \n\nAbstract: Can AI systems exceed the capabilities of the human experts who provided their training data? The talk will examine the hypothesis of AI self‑improvement\, involving mechanisms such as synthetic data generation\, reinforcement learning\, and tool‑augmented reasoning with formal verification loops. \nI will also present recent work at Princeton\, including the Gödel Prover V2 for Lean‑based theorem proving and a new inference pipeline that achieved state‑of‑the‑art performance (at the time of evaluation) on IMO‑ProofBench (Advanced) at moderate inference costs ($20–$30 per problem). These will illustrate how AI systems are sometimes able to escape “cognitive wells”—local optima in a model’s reasoning capabilities. While providing evidence for the feasibility of self‑improvement\, they also highlight important hurdles and open questions. \n\n  \n\n \nSanjeev Arora is Charles C. Fitzmorris Professor of Computer Science and Director of Princeton Language and Intelligence\, a unit devoted to research and applications of large AI models. He got his Phd from UC Berkeley in 1994 and has been a faculty member at Princeton since then. He has been awarded the ACM Prize in Computing (2011)\, Fulkerson Prize in Discrete Mathematics (2012)\, Packard Fellowship\, Sloan Fellowship\, and the ACM Doctoral Dissertation Prize. He was a plenary speaker at the International Congress of Mathematicians in 2018 and is a member of the National Academy of Science and American Academy of Arts and Sciences. \n\n\n\n\n\nThis event is made possible by the generous funding of Ding Lei and Harry Shum.\n\n\n 
URL:https://cmsa.fas.harvard.edu/event/2026_dingshum/
LOCATION:Harvard Science Center\, 1 Oxford Street\, Cambridge\, MA\, 02138
CATEGORIES:Ding Shum Lecture,Event,Public Lecture,Special Lectures
ATTACH;FMTTYPE=image/jpeg:https://cmsa.fas.harvard.edu/media/Ding-Shum-2026_hall-d.jpg
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20250213T160000
DTEND;TZID=America/New_York:20250213T170000
DTSTAMP:20260503T113337
CREATED:20240708T151711Z
LAST-MODIFIED:20250328T150436Z
UID:10003396-1739462400-1739466000@cmsa.fas.harvard.edu
SUMMARY:2025 Ding Shum Lecture: Irit Dinur\, IAS: Expanders from local to global
DESCRIPTION:  \n \nOn February 13\, 2025 the CMSA hosted the sixth annual Ding Shum Lecture\, given by Irit Dinur\, Institute for Advanced Study. \nLocation: Harvard Science Center  Hall A & via Zoom Webinar \nSpeaker: Irit Dinur\, Institute for Advanced Study \n\n\n\nTitle: Expanders from local to global \nAbstract: Imagine a network—like a social network\, a transportation system\, or even a biological system—where every part of the network is robustly connected to the rest. Expander graphs are the mathematical idealization of such networks. They are structures where any small group of points (nodes) has many connections to the rest of the graph\, ensuring that no part is isolated and information (or influence) spreads efficiently throughout.\nWe will begin by surveying expander graphs\, their discovery and construction\, and some fascinating applications such as error-correcting codes\, pseudorandomness\, and probabilistically checkable proofs (PCPs)\, highlighting their role as a foundation for many breakthroughs in theoretical computer science. Then\, we will shift focus to an exciting new kind of expanders called high dimensional expanders (HDXs). While expanders are well-understood and widely applied\, HDXs remain enigmatic\, with potential that we are only starting to uncover. We will talk about a fascinating local to global feature that HDXs have\, and some applications. \n\n \n\n\n\n\nThis event is made possible by the generous funding of Ding Lei and Harry Shum.\n\n\n 
URL:https://cmsa.fas.harvard.edu/event/2025_dingshum/
LOCATION:Harvard Science Center\, 1 Oxford Street\, Cambridge\, MA\, 02138
CATEGORIES:Ding Shum Lecture,Event,Public Lecture,Special Lectures
ATTACH;FMTTYPE=image/png:https://cmsa.fas.harvard.edu/media/DIngShum_21325.png
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20240328T163000
DTEND;TZID=America/New_York:20240328T173000
DTSTAMP:20260503T113337
CREATED:20240103T175709Z
LAST-MODIFIED:20250409T192237Z
UID:10001105-1711643400-1711647000@cmsa.fas.harvard.edu
SUMMARY:2024 Ding Shum Lecture: Yann LeCun: Objective-Driven AI: Towards AI systems that can learn\, remember\, reason\, and plan
DESCRIPTION:LECTURE SLIDES (pdf) \nOn March 28\, 2024\, the CMSA will host the fifth annual Ding Shum Lecture\, given by Yann LeCun. \nTime: 4:30–5:30 pm ET \nSpeaker: Yann Lecun\, New York University & META \nLocation: Harvard Science Center  Hall A & via Zoom Webinar \nTitle: Objective-Driven AI: Towards AI systems that can learn\, remember\, reason\, and plan \n\n\nAbstract:  \nHow could machines learn as efficiently as humans and animals? \nHow could machines learn how the world works and acquire common sense? \nHow could machines learn to reason and plan? \nCurrent AI architectures\, such as Auto-Regressive Large Language Models fall short. I will propose a modular cognitive architecture that may constitute a path towards answering these questions. The centerpiece of the architecture is a predictive world model that allows the system to predict the consequences of its actions and to plan a sequence of actions that optimize a set of objectives. The objectives include guardrails that guarantee the system’s controllability and safety. The world model employs a Hierarchical Joint Embedding Predictive Architecture (H-JEPA) trained with self-supervised learning. The JEPA learns abstract representations of the percepts that are simultaneously maximally informative and maximally predictable. The corresponding working paper is available here: https://openreview.net/forum?id=BZ5a1r-kVsf \n\n\n\n\n\n\n\n\n\n\nThis event is made possible by the generous funding of Ding Lei and Harry Shum. \n 
URL:https://cmsa.fas.harvard.edu/event/2024_dingshum/
LOCATION:Harvard Science Center\, 1 Oxford Street\, Cambridge\, MA\, 02138
CATEGORIES:Ding Shum Lecture,Event,Public Lecture,Special Lectures
ATTACH;FMTTYPE=image/jpeg:https://cmsa.fas.harvard.edu/media/Ding-Shum-2024_8.5x11.jpg
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20230321T170000
DTEND;TZID=America/New_York:20230321T180000
DTSTAMP:20260503T113337
CREATED:20230705T053409Z
LAST-MODIFIED:20250409T192224Z
UID:10000065-1679418000-1679421600@cmsa.fas.harvard.edu
SUMMARY:2023 Ding Shum Lecture
DESCRIPTION:On March 21\, 2023\, the CMSA hosted the fourth annual Ding Shum Lecture\, given by Cynthia Dwork (Harvard SEAS and Microsoft Research). \n\n\nTime: 5:00-6:00 pm ET \nLocation: Harvard University Science Center Hall D \nThis event was be held in person and via Zoom webinar. \n\n  \n\nTitle: Measuring Our Chances: Risk Prediction in This World and its Betters \nAbstract: Prediction algorithms score individuals\, assigning a number between zero and one that is often interpreted as an individual probability: a 0.7 “chance” that this child is in danger in the home; an 80% “probability” that this woman will succeed if hired; a 1/3 “likelihood” that they will graduate within 4 years of admission. But what do words like “chance\,” “probability\,” and “likelihood” actually mean for a non-repeatable activity like going to college? This is a deep and unresolved problem in the philosophy of probability. Without a compelling mathematical definition we cannot specify what an (imagined) perfect risk prediction algorithm should produce\, nor even how an existing algorithm should be evaluated. Undaunted\, AI and machine learned algorithms churn these numbers out in droves\, sometimes with life-altering consequences. \nAn explosion of recent research deploys insights from the theory of pseudo-random numbers – sequences of 0’s and 1’s that “look random” but in fact have structure – to yield a tantalizing answer to the evaluation problem\, together with a supporting algorithmic framework with roots in the theory of algorithmic fairness. \nWe can aim even higher. Both (1) our qualifications\, health\, and skills\, which form the inputs to a prediction algorithm\, and (2) our chances of future success\, which are the desired outputs from the ideal risk prediction algorithm\, are products of our interactions with the real world. But the real world is systematically inequitable. How\, and when\, can we hope to approximate probabilities not in this world\, but in a better world\, one for which\, unfortunately\, we have no data at all? Surprisingly\, this novel question is inextricably bound with the very existence of nondeterminism. \n\n\nProfessor Cynthia Dwork is Gordon McKay Professor of Computer Science at the Harvard University John A. Paulson School of Engineering and Applied Sciences\, Affiliated Faculty at Harvard Law School\, and Distinguished Scientist at Microsoft. She uses theoretical computer science to place societal problems on a firm mathematical foundation. \nHer recent awards and honors include the 2020 ACM SIGACT and IEEE TCMF Knuth Prize\, the 2020 IEEE Hamming Medal\, and the 2017 Gödel Prize. \n\n\n\n\nTalk Chair: Horng-Tzer Yau (Harvard Mathematics & CMSA)\n\nModerator: Faidra Monachou (Harvard CMSA)\n\n\n\n\n\n\n\n\n\nThe 2020-2022 Ding Shum lectures were postponed due to Covid-19. \n\n\n\nThe 2019 Ding Shum Lecture featured Ronald Rivest on “Election Security.”\n\n\nThis event is made possible by the generous funding of Ding Lei and Harry Shum. \n\n\nWatch the Lecture on Youtube:
URL:https://cmsa.fas.harvard.edu/event/2023-ding-shum-lecture/
LOCATION:Harvard Science Center\, 1 Oxford Street\, Cambridge\, MA\, 02138
CATEGORIES:Ding Shum Lecture,Event,Special Lectures
ATTACH;FMTTYPE=image/jpeg:https://cmsa.fas.harvard.edu/media/Cynthia-Dwork.jpg
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BEGIN:VEVENT
DTSTART;TZID=America/New_York:20191022T121100
DTEND;TZID=America/New_York:20191022T121100
DTSTAMP:20260503T113337
CREATED:20230707T175654Z
LAST-MODIFIED:20250328T185250Z
UID:10000120-1571746260-1571746260@cmsa.fas.harvard.edu
SUMMARY:2019 Ding Shum Lecture
DESCRIPTION:  \nOn October 22\, 2019\, the CMSA held the third annual Ding Shum lecture. \nSpeaker: Ronald L. Rivest (MIT) \nTitle: Election Security \nRonald L. Rivest is an Institute Professor at the Massachusetts Institute of Technology. He is a member of the Electrical Engineering and Computer Science Department and the Computer Science and Artificial Intelligence Laboratory (CSAIL) and a founder of the Cryptography and Information Security research group within CSAIL. His research has been in the areas of algorithms\, machine learning\, cryptography\, and election security\, for which he has received multiple awards\, including: the ACM Turing Award (with Adleman and Shamir)\, the BBVA Frontiers of Knowledge Award\, National Inventor’s Hall of Fame membership\, and the Marconi Prize. \nProf. Rivest is also well-known as a co-author of the textbook “Introduction to Algorithms” (with Cormen\, Leiserson\, and Stein)\, and as a co-inventor of the RSA public-key cryptosystem (with Adleman and Shamir). He is a co-founder of RSA and of Verisign.He has served on the Technical Guidelines Development Committee (advisory to the Election Assistance Commission)\, in charge of the Security subcommittee. He is a member of the CalTech/MIT Voting Technology Project\, on the Board of Verified Voting\, and an advisor to the Electronic Privacy Information Center. Additionally\, he has served on the Technical Guidelines Development Committee (advisory to the Election Assistance Commission)\, as a member of the CalTech/MIT Voting Technology Project\, and as an advisor to the Electronic Privacy Information Center. \n  \n \nLast year featured Eric Maskin\, who spoke on “How to Improve Presidential Elections: the Mathematics of Voting.” The first Ding Shum lecture took place on October 10\, 2017\, featuring Leslie Valiant on “Learning as a Theory of Everything.” \nThis event is made possible by the generous funding of Ding Lei and Harry Shum.\n 
URL:https://cmsa.fas.harvard.edu/event/2019-ding-shum-lecture/
LOCATION:CMSA\, 20 Garden Street\, Cambridge\, MA\, 02138\, United States
CATEGORIES:Ding Shum Lecture,Event,Public Lecture,Special Lectures
ATTACH;FMTTYPE=image/png:https://cmsa.fas.harvard.edu/media/DingShum-2019-1.png
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20181024T150000
DTEND;TZID=America/New_York:20181024T160000
DTSTAMP:20260503T113337
CREATED:20230715T085247Z
LAST-MODIFIED:20250328T150854Z
UID:10000101-1540393200-1540396800@cmsa.fas.harvard.edu
SUMMARY:2018 Ding Shum Lecture
DESCRIPTION:  \n \nOn October 24\, 2018\, the CMSA hosted the second annual Ding Shum lecture. This event was made possible by the generous funding of Ding Lei and Harry Shum. Last year featured Leslie Valiant\, who spoke on “learning as a Theory of Everything.” \nThis year will feature Eric Maskin\, who will speak on “How to Improve Presidential Elections: the Mathematics of Voting.” This lecture will take place from 5:00-6:00pm in Science Center\, Hall D.  \nPictures of the event can be found here.
URL:https://cmsa.fas.harvard.edu/event/2018-ding-shum-lecture/
LOCATION:Harvard Science Center\, 1 Oxford Street\, Cambridge\, MA\, 02138
CATEGORIES:Ding Shum Lecture,Event,Public Lecture,Special Lectures
ATTACH;FMTTYPE=image/png:https://cmsa.fas.harvard.edu/media/Ding-Shum-lecture-2018.png
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20171010T170000
DTEND;TZID=America/New_York:20171010T180000
DTSTAMP:20260503T113337
CREATED:20230717T173349Z
LAST-MODIFIED:20250328T150724Z
UID:10000038-1507654800-1507658400@cmsa.fas.harvard.edu
SUMMARY:2017 Ding Shum Lecture
DESCRIPTION:Leslie Valiant will be giving the inaugural talk of the Ding Shum Lectures on Tuesday\, October 10 at 5:00 pm in Science Center Hall D\, Cambridge\, MA. \nLearning as a Theory of Everything \nAbstract: We start from the hypothesis that all the information that resides in living organisms was initially acquired either through learning by an individual or through evolution. Then any unified theory of evolution and learning should be able to characterize the capabilities that humans and other living organisms can possess or acquire. Characterizing these capabilities would tell us about the nature of humans\, and would also inform us about feasible targets for automation. With this purpose we review some background in the mathematical theory of learning. We go on to explain how Darwinian evolution can be formulated as a form of learning. We observe that our current mathematical understanding of learning is incomplete in certain important directions\, and conclude by indicating one direction in which further progress would likely enable broader phenomena of intelligence and cognition to be realized than is possible at present. \n 
URL:https://cmsa.fas.harvard.edu/event/2017-ding-shum-lecture/
LOCATION:Harvard Science Center\, 1 Oxford Street\, Cambridge\, MA\, 02138
CATEGORIES:Ding Shum Lecture,Event,Public Lecture,Special Lectures
ATTACH;FMTTYPE=image/png:https://cmsa.fas.harvard.edu/media/Ding-Shum-lecture-3.png
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