Seminars

Abstract: Neuroscience and AI share a bottleneck: while one can build (artificial) or record (biological) complex networks, we struggle to explain their functional circuitry; i.e., how they compute. In this… Read More »Neural Circuit Discovery via Representation and Dynamics by Savik Kinger

Abstract: This talk explores a novel approach to speeding up complex database queries by combining two powerful ideas: factorization and vectorized execution. Instead of processing large intermediate results (which can… Read More »Faster Queries, Smarter Execution: Factorization Meets Vectorization in Modern Data Systems

Abstract: In many settings, a finite supply of some public resource is allocated among people over time, without using money: a computing cluster among researchers, food among food-banks, medical supplies between… Read More »The Mathematics (and Ethics) of Fair Resource Allocation by Dr. Siddhartha Banerjee (Cornell University)

Abstract: The Pigeonhole Equal Subset Sum problem (PESS), introduced by Papadimitriou (1994), asks: given n positive integers bounded by M with total sum less than 2^n − 1, find two… Read More »Finding Equal Subset Sums in the Pigeonhole Regime by Dr. Pranjal Dutta

Abstract: A common denominator of conventional financial systems, trusted execution environments (like SGX), blockchain technology, and ZK-rollups is the promise of computational integrity -- doing the right computation on potentially secret… Read More »Cryptographic proofs for privacy and integrity by Prof. Chaya Ganesh (IISc)

Abstract: The convergence of artificial intelligence (AI), the Internet of Things (IoT), cloud-edge computing, and autonomous cyber-physical systems is transforming critical infrastructures across healthcare, manufacturing, transportation, and smart cities. As… Read More »Building Trustworthy Intelligent Systems for Critical Infrastructures by Dr. Geetanjali

Abstract: Modern Internet-scale services must identify and mitigate customer-impacting incidents quickly. Despite the development of many Root Cause Analysis (RCA) algorithms—including recent LLM-assisted solutions—existing approaches struggle with the "long tail"… Read More »Abstractions for expressive, extensible, and scalable root cause analysis by Dr. Vipul Harsh

Abstract: We will discuss two new frontiers in science. 1) Autonomous AI Scientists: Research conducted by autonomous AI scientist systems is rapidly increasing in prevalence. These systems execute the entire research… Read More »Two New Frontiers in Science: Autonomous AI Scientists and Lotteries for Acceptance Decisions by Prof Nihar Shah

In this talk I will start by describing a protocol called magic state distillation, which was first introduced by Bravyi and Kitaev in 2005. This protocol uses quantum error-correcting codes… Read More »Weight Enumerators and Magic State Distillation by Dr. Amolak Kalra

Abstract: What can be learned from data? Traditional answers to this question assume an idealized data-generating process where test and training distributions are symmetric, which is rarely the case in… Read More »Foundations of Learning from Positive Samples by Dr. Anay Mehrotra