Seminars

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 be slow and memory-intensive), the method represents data in a compact, factorized form that avoids redundancy. It also applies vectorized processing techniques to process data… 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 hospitals, funding between non-profit projects, fellowships among admitted students, etc. The underlying aim is often to try and be ‘fair’ in these allocations…but what exactly do… 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 distinct subsets with the same sum. A solution is guaranteed by the pigeonhole principle, yet finding one efficiently has been a longstanding challenge. In this… 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 inputs, even when there is no trust.   In this talk, we will define computational integrity and show how one can verify the correctness of a… 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 these technologies become increasingly interconnected, ensuring that they operate in a secure, reliable, explainable, and privacy-preserving manner has emerged as a fundamental research challenge. This… 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" of novel failure modes and the sheer scale of telemetry. In this talk, I argue that the path forward requires a paradigm shift: from developing… 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 process autonomously, with little or no human intervention. While papers generated may appear appealing, we investigate whether these systems follow rigorous research methodologies. Using novel… 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 and noisy magic states to perform universal fault-tolerant quantum computation. I will then explain how the performance of a certain class of magic state distillation… 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 applications. In this talk, we will revisit this question for positive-only learning, a setting where only positive examples are observed. This is a challenging problem… Read More »Foundations of Learning from Positive Samples by Dr. Anay Mehrotra

Abstract: The volume of data generated worldwide is growing at an unprecedented rate, and GPUs have emerged as the primary compute engine for processing this data. While GPUs offer massive compute power — reaching thousands of TFLOPS — they are constrained by the relatively low memory bandwidth (around a few TB/s) and limited memory capacity… Read More »Scaling Up GPU Memory Management by Dr. Pratheek B