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TZID:Asia/Kolkata
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DTSTART:20260101T000000
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BEGIN:VEVENT
DTSTART;TZID=Asia/Kolkata:20261008T120000
DTEND;TZID=Asia/Kolkata:20261008T130000
DTSTAMP:20261010T052227
CREATED:20261001T054452Z
LAST-MODIFIED:20261001T054452Z
UID:2667-1791460800-1791464400@homecse.iitd.ac.in
SUMMARY:Who Gets What? Fair Division of Indivisible Goods by Prof. Kurt Mehlhorn
DESCRIPTION:Abstract: \nA set of indivisible goods\, e.g.\, a car\, a house\, a toothbrush\, . . .  has to be split among a set of agents in a fair manner. Each agent has its own valuation function for sets of goods. What constitutes a fair allocation? When does a fair allocation exist? If it exists\, can we compute it efficiently? Can we approximate fair allocations? \nThere are three main notions of fairness: envy-based\, share-based\, and welfare-based. In the first part of the talk\, I will discuss all three notions. \nIn the second part\, I will concentrate on envy-freeness: Nobody should get more than I do. For indivisible goods\, envy-freeness cannot be achieved in general. Think of two persons and one good which both persons like. The good has to be given to one of the persons\, and the other person will envy. Envy-freeness up to any good (EFX) is a relaxation. One person may envy another person\, but upon removal of any good from the other person’s bundle\, the envy goes away. \nImagine the following hypothetical dialogue. Two brothers inherit the property of their parents. One says to the other. You are getting a house\, a car\, and a toothbrush. I envy you\, because I prefer what you get over what I get. But this is OK\, because\, if I discard the toothbrush\, I do not envy you anymore. I will mainly discuss two results: \n\nFor three agents and additive valuations\, an EFX-allocation always exists. A valuation is additive\, if the value of a bundle of items is the sum of the values of the items in the bundle (JACM ’24\, Operations Research ’24).\nFor general valuations\, EFX-allocations do not always exist. (arXiv ’26).\n\n 
URL:https://homecse.iitd.ac.in/event/who-gets-what-fair-division-of-indivisible-goods-by-prof-kurt-mehlhorn/
LOCATION:Bharti 501\, IIT Campus\, Hauz Khas\, New Delhi
CATEGORIES:Seminars
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=Asia/Kolkata:20260929T153000
DTEND;TZID=Asia/Kolkata:20260929T163000
DTSTAMP:20261010T052227
CREATED:20260930T052832Z
LAST-MODIFIED:20260930T052832Z
UID:2665-1790695800-1790699400@homecse.iitd.ac.in
SUMMARY:Trust in an Untrusted World: Private Access over Public Infrastructures by Prof. Divy Agrawal
DESCRIPTION:Abstract: \nWe are living in an era where our digital lives are increasingly interdependent\n\nand deeply interconnected. These connections rely on a vast\, layered ecosystem of\nactors—many of whose trustworthiness is uncertain or outright suspect. Over the past three\ndecades\, rapid advances in computing and communication technologies have brought\nunprecedented access and connectivity to billions of users. Yet this digitization comes at a cost:\nour interactions\, queries\, and data are increasingly vulnerable to privacy violations. Today\,\nthreats to privacy come not just from malicious individuals\, but also from powerful\ninstitutions—ranging from service providers to nation-states.\n\nIn this reality of an untrusted world\, we pose several foundational research questions: (i) Can\nwe design a scalable voice communication system that ensures absolute privacy? (ii) Can\nwe build an oblivious search engine over public document repositories? (iii) Can we develop\nscalable private query processing over shared or public databases? (iv) And in the age of\nlarge language models\, can we enable private inference for user queries? These are not just\nopen problems — they are essential challenges if we are to build trusted services over\nuntrusted infrastructures. In this talk\, I will present recent work that leverages Homomorphic\nEncryption to address some of these questions. We explore the inherent performance and\nscalability trade-offs in enabling private access\, search\, and inference. If nothing else\, our\nresults underscore a critical insight: ensuring privacy at scale is not impossible\, but it\ncomes at a high cost.
URL:https://homecse.iitd.ac.in/event/trust-in-an-untrusted-world-private-access-over-public-infrastructures-by-prof-divy-agrawal/
LOCATION:Bharti 501\, IIT Campus\, Hauz Khas\, New Delhi
CATEGORIES:Seminars
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=Asia/Kolkata:20260825T120000
DTEND;TZID=Asia/Kolkata:20260825T130000
DTSTAMP:20261010T052227
CREATED:20260818T060107Z
LAST-MODIFIED:20260818T060107Z
UID:2651-1787659200-1787662800@homecse.iitd.ac.in
SUMMARY:Scaling Up GPU Memory Management by Dr. Pratheek B
DESCRIPTION:Abstract:  \nThe 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 (in tens of GBs). As a result\, memory is often the primary performance bottleneck in many GPU applications.\n\nIn this talk\, we explore two key aspects of GPU memory management: memory oversubscription and address translation. Memory oversubscription enables GPU programs to work on datasets larger than the on-board GPU memory\, but can lead to severe slowdowns. Efficient address translation is important in GPUs\, as it lies in the critical path of memory accesses\, and thus impacts overall GPU performance.\n\nThis talk will primarily focus on the challenges posed by GPU memory oversubscription. GPU memory oversubscription enables GPU applications to work with datasets larger than the GPU memory capacity\, using the CPU memory as swap space. Unfortunately\, applications under GPU memory oversubscription often experience significant slowdowns. Our work\, ObservUVM\, improves GPU's eviction and prefetching policies by enabling observability into GPU’s memory accesses to pages resident on GPU memory — something current GPUs lack. We show that  current eviction and prefetching policies\, handled by the driver running on the CPU\, are limited in their ability to make informed decisions due to the lack of observability. ObservUVM enables observability into GPU’s memory accesses by repurposing existing hardware access counters\, enabling better-informed eviction and prefetching policies. ObservUVM improves UVM performance by around 33% across 14\napplications.\n\nWe will also touch upon a few other relevant problems in GPU memory management. We will discuss SUV\, our compiler-driven technique to perform automated data placement and migration in memory-oversubscribed GPUs. Then we will briefly discuss the impact of non-uniformity of Multi-Chip-Module (MCM) design on address\ntranslation in GPUs\, and on the impact of multi-tenancy on address translation in GPUs.\n\n\nBio:  \nPratheek is a Senior Software Engineer at NVIDIA\, working on improving GPU memory management. He obtained his Ph.D. from the Indian Institute of Science\, Bengaluru in 2026. His research revolves around improving GPU memory management\, focusing on address translation and data placement for large-memory workloads. His work spans the domains of GPU micro-architecture\, compiler techniques\, and operating systems.\nPreviously\, he had worked on reverse-engineering Nvidia GPUs.
URL:https://homecse.iitd.ac.in/event/scaling-up-gpu-memory-management-by-dr-pratheek-b/
LOCATION:Bharti 501\, IIT Campus\, Hauz Khas\, New Delhi
CATEGORIES:Seminars
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=Asia/Kolkata:20260819T110000
DTEND;TZID=Asia/Kolkata:20260819T120000
DTSTAMP:20261010T052227
CREATED:20260813T065722Z
LAST-MODIFIED:20260813T065722Z
UID:2636-1787137200-1787140800@homecse.iitd.ac.in
SUMMARY:Foundations of Learning from Positive Samples by Dr. Anay Mehrotra
DESCRIPTION: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.\nIn this talk\, we will revisit this question for positive-only learning\, a setting where only positive examples are observed. This is a challenging problem arising in bioinformatics and causal inference. Classical results show that learning is impossible in general. However\, we will show that the hard instances are fragile: under a smoothed analysis\, which rules out pathological distributions\, efficient learning is possible. The same ideas also lead to faster and more general algorithms for estimation and regression from truncated data\, a foundational problem in statistics.\nThe talk is based on joint work with Shai Ben-David\, Yang Cai\, Constantine Caramanis\, Alkis Kalavasis\, Alex Kouridakis\, Jane H. Lee\, Katerina Mamali\, Farnam Mansouri\, and Manolis Zampetakis. \nSpeaker bio: Anay Mehrotra (https://anaymehrotra.com) is a Motwani Postdoctoral Fellow at Stanford working with Amin Saberi. He recently completed his PhD at Yale\, advised by Amin Karbasi and Manolis Zampetakis. His work has received the Best Paper Award at COLT and the Sri Binay Kumar Sinha Award from IIT Kanpur\, was selected for the TCS4All Rising Star session at STOC 2026\, and has been featured in WIRED.
URL:https://homecse.iitd.ac.in/event/foundations-of-learning-from-positive-samples-by-dr-anay-mehrotra/
LOCATION:Bharti 501\, IIT Campus\, Hauz Khas\, New Delhi
CATEGORIES:Seminars
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=Asia/Kolkata:20260814T140000
DTEND;TZID=Asia/Kolkata:20260814T150000
DTSTAMP:20261010T052227
CREATED:20260813T064616Z
LAST-MODIFIED:20260813T064616Z
UID:2628-1786716000-1786719600@homecse.iitd.ac.in
SUMMARY:Weight Enumerators and Magic State Distillation by Dr. Amolak Kalra
DESCRIPTION: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.\nI will then explain how the performance of a certain class of magic state distillation protocols can be determined by the weight enumerator of the quantum error-correcting code used for distillation. Using this connection\, I will show how one can derive new constraints on certain families of classical and quantum weight enumerators that are stronger than those previously known.\nThese stronger constraints lead to tighter distance bounds for quantum codes and\, rather surprisingly\, to a proof of the non-existence of certain families of classical codes.\nThis talk is based on joint work with Shiroman Prakash: https://arxiv.org/abs/2501.10163
URL:https://homecse.iitd.ac.in/event/weight-enumerators-and-magic-state-distillation-by-dr-amolak-kalra/
LOCATION:Bharti 501\, IIT Campus\, Hauz Khas\, New Delhi
CATEGORIES:Seminars
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=Asia/Kolkata:20260814T120000
DTEND;TZID=Asia/Kolkata:20260814T130000
DTSTAMP:20261010T052227
CREATED:20260813T064455Z
LAST-MODIFIED:20260813T064455Z
UID:2626-1786708800-1786712400@homecse.iitd.ac.in
SUMMARY:Two New Frontiers in Science: Autonomous AI Scientists and Lotteries for Acceptance Decisions by Prof Nihar Shah
DESCRIPTION:Abstract:\nWe will discuss two new frontiers in science. \n1) 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 experiments designed to mitigate confounding factors\, we uncover significant and concerning methodological flaws in their workflows. We also propose a method to detect these problems and provide policy recommendations for journals and conferences: Such methodological problems are not detectable from the produced paper alone but can be identified through analysis of the trace logs of the workflow executed by the AI scientists. \n2) Lotteries for Acceptance Decisions: Traditional decisions for accepting/rejecting papers or grant proposals involve expert reviews followed by discussions and human-specified decisions. More recently though\, citing drawbacks of such traditional approaches\, a number of funding agencies worldwide have moved towards a different decision model to make acceptance decisions. These agencies have incorporated “partial lotteries” into their decision-making\, where final decisions are randomized in a manner that still respects reviewers’ evaluations. We will first identify several problems in current implementations of such partial lotteries. We will then present a principled approach to designing improved partial lotteries with strong mathematical guarantees and empirical performance. \nThe talk will also contain a generous dose of minions. \n  \nBio: Nihar B. Shah is an Associate Professor in the Machine Learning and Computer Science departments at Carnegie Mellon University (CMU). His research focuses on the Evaluation of Science and the Science of Evaluation. His group develops computational tools with strong theoretical guarantees\, and designs and conducts controlled experiments for evidence-based policy design. His work has been used in the review of well over a hundred thousand papers and thousands of proposals\, across over 200 venues. He is a recipient of The Allen Newell Award for Research Excellence\, a Young Alumnus Medal from the Indian Institute of Science\, a JP Morgan faculty research award\, Google Research Scholar Award\, an NSF CAREER Award\, and the David J. Sakrison memorial prize from EECS Berkeley for a “truly outstanding and innovative PhD thesis.” Papers authored by him have won several Best Paper Awards.
URL:https://homecse.iitd.ac.in/event/two-new-frontiers-in-science-autonomous-ai-scientists-and-lotteries-for-acceptance-decisions-by-prof-nihar-shah/
LOCATION:Bharti 501\, IIT Campus\, Hauz Khas\, New Delhi
CATEGORIES:Seminars
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=Asia/Kolkata:20260813T120000
DTEND;TZID=Asia/Kolkata:20260813T130000
DTSTAMP:20261010T052227
CREATED:20260813T050927Z
LAST-MODIFIED:20260813T050927Z
UID:2622-1786622400-1786626000@homecse.iitd.ac.in
SUMMARY:Abstractions for expressive\, extensible\, and scalable root cause analysis by Dr. Vipul Harsh
DESCRIPTION: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 point-solution algorithms to a systems-first approach that enables rapid prototyping of diverse RCA techniques. I will introduce MoCE: a Mixture-of-Experts framework that provides high-level abstractions for failure diagnosis. MoCE lets developers express complex troubleshooting logic succinctly via its domain-specific operators while its underlying runtime system handles scalable telemetry processing. Finally\, I will briefly describe how these abstractions enable reliable\, autonomous agents for domain-specific data analytics tasks.\n\nBio: Vipul Harsh is a postdoctoral researcher at Conviva with Vyas Sekar and a visiting researcher at Carnegie Mellon University (CMU). His research interests lie at the intersection of networked systems\, AI\, and theory. His current research focuses on developing new abstractions and frameworks for building reliable and verifiable systems — from networked infrastructure to AI agents. He has also worked on datacenter topology\, distributed monitoring\, and parallel algorithms. His research has been published in top-tier CS conferences (SIGCOMM\, NSDI\, SPAA\, among others); his work on Murphy was adopted into VMware’s network management suite and DRing/Starfish have directly influenced Amazon AWS’s datacenter architecture. He completed his Ph.D. at UIUC\, where he worked with Brighten Godfrey\, and holds an undergraduate degree from IIT Bombay. His thesis was nominated by UIUC for the ACM SIGCOMM dissertation award.
URL:https://homecse.iitd.ac.in/event/abstractions-for-expressive-extensible-and-scalable-root-cause-analysis-by-dr-vipul-harsh/
CATEGORIES:Seminars
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=Asia/Kolkata:20260812T120000
DTEND;TZID=Asia/Kolkata:20260812T130000
DTSTAMP:20261010T052227
CREATED:20260813T051313Z
LAST-MODIFIED:20260813T051313Z
UID:2624-1786536000-1786539600@homecse.iitd.ac.in
SUMMARY:Building Trustworthy Intelligent Systems for Critical Infrastructures by Dr. Geetanjali
DESCRIPTION: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 talk presents a coherent research program dedicated to the development of Trustworthy Intelligent Systems through the integration of network security\, blockchain\, trust management\, intelligent decision-making\, and AI. \nIt traces the evolution of this research from blockchain-enabled secure communication and privacy-preserving healthcare frameworks to adaptive trust evaluation\, multi-criteria decision-making\, and decentralized trust management for the Industrial Internet of Things (IIoT). These contributions culminate in TrustNextGen\, a comprehensive framework for trustworthy next-generation IIoT\, providing a robust foundation for future advances in Trustworthy AI. The talk concludes by outlining a forward-looking research roadmap encompassing explainable AI\, federated learning\, edge intelligence\, digital twins\, and resilient autonomous systems. Together\, these directions aim to enable secure\, trustworthy\, and human-centric intelligent systems capable of safeguarding next-generation critical infrastructures while delivering broad societal and industrial impact. \nBio: Dr. Geetanjali Rathee is an Assistant Professor in the Department of Computer Science andEngineering at Netaji Subhas University of Technology (NSUT)\, Dwarka\, New Delhi. Prior tojoining NSUT\, she served as an Assistant Professor (Senior Grade) at Jaypee University\nofInformation Technology (JUIT)\, Waknaghat\, Himachal Pradesh\, for four years. She earned herB.Tech.\, M.Tech.\, and Ph.D. degrees in Computer\nScience and Engineering in 2011\, 2014\, and2017\, respectively.Dr.Rathee has made significant research contributions in the fields of\ncomputer science andnetwork security. She holds eight Indian patents and has authored approximately 15 papers inIEEE Transactions journals\n(including publications in journals with impact factors as high as9.1)\, over 40 SCI-indexed research papers\, around 10 Scopus-indexed\npublications\, and morethan 15 conference papers and book chapters at national and international forums.Her research has also been supported\nthrough competitive funding. She successfully completeda SERB-SURE (ANRF) sponsored project on data governance and security in\nhealthcare\, whileanother collaborative proposal with Sir Ganga Ram Hospital\, Delhi\, is currently under reviewunder the ICRM scheme. In\naddition\, Dr. Rathee is the author of the book Large-Scale DataStreaming\, Processing\, and Blockchain Security. She has delivered invited talks on blockchaintechnology and network security at several reputed universities and institutions across India.Her research technology\,resilient wireless mesh networks\, routing protocols\, computer networking\, and Industry 4.0.She also serves as a regular reviewer for several leading international journals\, including IEEETransactions on Vehicular Technology\, Wireless Networks\, Cluster Computing\, Journal ofAmbient Intelligence and Humanized Computing\, Transactions on Emerging Telecommunications Technologies\, and the International Journal of Communication Systems.
URL:https://homecse.iitd.ac.in/event/building-trustworthy-intelligent-systems-for-critical-infrastructures-by-dr-geetanjali/
CATEGORIES:Seminars
ORGANIZER;CN="Subodh Sharma":MAILTO:svs@cse.iitd.ac.in
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=Asia/Kolkata:20260810T120000
DTEND;TZID=Asia/Kolkata:20260810T130000
DTSTAMP:20261010T052227
CREATED:20260813T064721Z
LAST-MODIFIED:20260813T064721Z
UID:2630-1786363200-1786366800@homecse.iitd.ac.in
SUMMARY:Cryptographic proofs for privacy and integrity by Prof. Chaya Ganesh (IISc)
DESCRIPTION:Abstract: \nA 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. \n  \nIn this talk\, we will define computational integrity and show how one can verify the correctness of a computation much more efficiently than having to re-perform the computation. We will introduce the notion of succinct proof systems that allow a prover to convince a verifier about the correctness of computation such that verification is exponentially faster than the computation itself\, and zero-knowledge where the verifier learns nothing beyond the truth of the statement. We will see applications of zkSNARKs (Zero-knowledge Succinct Non-interactive ARguments of Knowledge)\, a kind of succinct arguments in decentralized systems like blockchain technology for both privacy and scalability issues\, and outline the design principle underlying zkSNARK constructions.
URL:https://homecse.iitd.ac.in/event/cryptographic-proofs-for-privacy-and-integrity-by-prof-chaya-ganesh-iisc/
LOCATION:Bharti 501\, IIT Campus\, Hauz Khas\, New Delhi
CATEGORIES:Seminars
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=Asia/Kolkata:20260805T110000
DTEND;TZID=Asia/Kolkata:20260805T120000
DTSTAMP:20261010T052227
CREATED:20260813T064916Z
LAST-MODIFIED:20260813T064916Z
UID:2632-1785927600-1785931200@homecse.iitd.ac.in
SUMMARY:Finding Equal Subset Sums in the Pigeonhole Regime by Dr. Pranjal Dutta
DESCRIPTION: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.\nIn this talk\, I will introduce the problem and discuss its connections to Subset Sum\, Equal Subset Sum\, and total search problems. First\, I will describe a simple birthday-paradox-based algorithm for the weak-pigeonhole regime\, and explain how combining it with Karmarkar–Karp differencing yields faster algorithms for dense instances. Second\, I will discuss a deterministic poly(n) · M^{o(1)}-time algorithm when M = 2^{o(n)}\, based on block merging and modular pruning. I will also discuss a conditional lower bound from lattice problems\, as well as an average-case poly(n) · M^{1/4}-time algorithm. This beats the best known algorithm which runs in poly(n)·M^{1/3} time (Jin-Wu\, ICALP 2024\, Jin-Williams-Zhang\, ESA 2025).\nBased on joint work with Deepak Bhati\, Antoine Joux\, Mahesh Sreekumar Rajasree\, and Karol Węgrzycki\, which got accepted in FOCS 2026. \nBio: Pranjal currently holds the Nanyang Assistant Professorship in the College of Computing and Data Science (CCDS) at Nanyang Technological University (NTU) Singapore. He spent Fall 2025 at Simons Institute as a Simons-Berkeley Fellow as well as Jane Street Research Fellow. Before joining NTU\, he was a postdoc at NUS Singapore\, hosted by Prof. Divesh Aggarwal. He obtained his PhD from CMI\, advised by Prof. Nitin Saxena and he was supported by Google PhD Fellowship. His PhD work won the ACM India Doctoral Dissertation Award 2023. He is broadly interested in Theoretical Computer Science\, with focus on algberaic flavoured algorithmic questions.
URL:https://homecse.iitd.ac.in/event/finding-equal-subset-sums-in-the-pigeonhole-regime-by-dr-pranjal-dutta/
LOCATION:Bharti 501\, IIT Campus\, Hauz Khas\, New Delhi
CATEGORIES:Seminars
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=Asia/Kolkata:20260727T110000
DTEND;TZID=Asia/Kolkata:20260727T120000
DTSTAMP:20261010T052227
CREATED:20260813T065116Z
LAST-MODIFIED:20260813T065116Z
UID:2634-1785150000-1785153600@homecse.iitd.ac.in
SUMMARY:The Mathematics (and Ethics) of Fair Resource Allocation by Dr. Siddhartha Banerjee (Cornell University)
DESCRIPTION: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 we mean? \nUnderstanding fairness in sequential decision-making is one of the most urgent (but also\, intellectually beautiful) topics today\, with deep connections to control theory\, economics\, optimization\, and normative philosophy. I will demonstrate this using two case studies: one where we know the exact numerical utility each agent gets from being allocated; and one where we do not\, but still want some rules as to which agents should have priority over others\, and why. Through these examples\, I will try to convince you of one big idea: as applied mathematicians\, our main task should be to characterize trade-offs between fairness and efficiency in different settings\, as a guide to policy-makers to help determine what is socially relevant. Luckily\, doing so turns out to be very intellectually rewarding — I will try and describe surprising connections between our problems and a host of other topics — physics\, information theory\, combinatorics\, computational complexity\, and many more! \nBio:  Sid Banerjee is an associate professor in the School of Operations Research at Cornell\, working on topics at the intersection of data-driven decision-making\, network algorithms and market design. His research is supported by grants from the NSF (including an NSF CAREER award)\, ARO\, and AFOSR\, and has received multiple awards including the Erlang Prize (2022)\, and best paper awards at ACM SIGMETRICS (2026) and the INFORMS Applied Probability Society (2021). He completed his PhD from the ECE Department at UT Austin\, and was a postdoctoral researcher in the Social Algorithms Lab at Stanford. He also served as a technical consultant with the research science group at Lyft from 2014-18.
URL:https://homecse.iitd.ac.in/event/the-mathematics-and-ethics-of-fair-resource-allocation-by-dr-siddhartha-banerjee-cornell-university/
LOCATION:Bharti 501\, IIT Campus\, Hauz Khas\, New Delhi
CATEGORIES:Seminars
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=Asia/Kolkata:20260528T110000
DTEND;TZID=Asia/Kolkata:20260528T120000
DTSTAMP:20261010T052227
CREATED:20260515T060527Z
LAST-MODIFIED:20260515T060527Z
UID:2523-1779966000-1779969600@homecse.iitd.ac.in
SUMMARY:Faster Queries\, Smarter Execution: Factorization Meets Vectorization in Modern Data Systems
DESCRIPTION:Abstract:\n\nThis 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 efficiently at the hardware level.\n\nSpeaker Bio:\nSunny Yasser is a PhD student in Computer Engineering at Polytechnique Montréal\, in the Data and AI Systems (DAIS) Lab under Prof. Amine Mhedhbi. He is also associated with MILA\, the premier AI research lab started by Prof. Yoshua Bengio. His research centers on high-performance query processing\, with an emphasis on compression-aware execution on modern hardware.  His broader work explores low-level system optimizations for scalable analytical and AI-driven data workloads. He has published papers at VLDB and SIGMOD previously.
URL:https://homecse.iitd.ac.in/event/faster-queries-smarter-execution-factorization-meets-vectorization-in-modern-data-systems/
LOCATION:Bharti 501\, IIT Campus\, Hauz Khas\, New Delhi
CATEGORIES:Seminars
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=Asia/Kolkata:20260520T160000
DTEND;TZID=Asia/Kolkata:20260520T170000
DTSTAMP:20261010T052227
CREATED:20260513T060719Z
LAST-MODIFIED:20260513T060719Z
UID:2520-1779292800-1779296400@homecse.iitd.ac.in
SUMMARY:Algorithmic Behaviours in In-Context Learning by Dr. Aditya Gangrade
DESCRIPTION:Venue: Bharti-501/ MS Teams \nAbstract: In-Context Learning (ICL) is a remarkable phenomenon whereby transformer-based LLMs can use data contained within their prompts to adapt their responses\, without changing their weights. This suggests that such models encode learning mechanisms. The recent literature has used statistical learning problems as a test-bed to investigate ICL\, and established that ICL can be realised for a wide range of function classes. However\, the mechanisms these models use to learn are poorly characterised. \nI will describe work on extracting and analysing learning algorithms embedded in the weights of transformers trained to perform ICL in two settings: linear-activation transformers for linear regression\, and softmax-activation transformers for linear classification. Through the former\, I will illustrate a high-level ‘simplify and validate’ strategy that allows extraction\, and through the latter\, I will describe a symmetry-driven strategy for evoking structure in these weights. In these settings\, we recover concrete iterative procedures that use existing ideas (Newton-Schulz; mean-shift methods) in new ways that are distinct from gradient descent. Further\, we show that transformers trained on variations of these problems implement modified versions of the same dynamics. This suggests that such models recover certain `stable’ algorithmic motifs\, and adapt them in response to problem structure. \nBased on work done jointly with Patrick Lutz\, Themistoklis Haris\, Arjun Chandra\, Hadi Daneshmand\, and Venkatesh Saligrama. \nBio:  Aditya Gangrade is a research scientist at the ECE department at Boston University. He obtained his Ph. D. in Systems Engineering from Boston University\, and previously held postdoctoral positions at Carnegie Mellon University and the University of Michigan. His research interests span theoretical and methodological aspects of machine learning\, with recent focus on safety in sequential decision making\, and on in-context learning phenomena.
URL:https://homecse.iitd.ac.in/event/algorithmic-behaviours-in-in-context-learning-by-dr-aditya-gangrade/
LOCATION:Bharti 501\, IIT Campus\, Hauz Khas\, New Delhi
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=Asia/Kolkata:20260506T120000
DTEND;TZID=Asia/Kolkata:20260506T130000
DTSTAMP:20261010T052227
CREATED:20260503T085759Z
LAST-MODIFIED:20260503T085759Z
UID:2517-1778068800-1778072400@homecse.iitd.ac.in
SUMMARY:Learning Hierarchical Control via Feasible Subgoal Prediction by Utsav Singh
DESCRIPTION:Venue: SIT001 \nAbstract: Solving long-horizon tasks remains a central challenge in robotics because agents must explore efficiently\, assign credit over long time scales\, and act under sparse supervision. To address this\, hierarchical reinforcement learning (HRL) offers a promising alternative to flat reinforcement learning (RL) by enabling a high-level policy to propose subgoals and a low-level policy to execute them. However\, in practice\, HRL suffers from a fundamental issue: the higher level can propose subgoals that are infeasible for the lower level to achieve\, leading to training instability and sub-optimal performance. In this talk\, I will present my research around a central idea: hierarchy is effective only when its high-level decisions are grounded in the capabilities of the lower-level policies. I will discuss methods for training high-level policies to predict feasible subgoals by leveraging expert demonstrations\, preference-based feedback\, and visually grounded reward synthesis. Across challenging navigation and manipulation tasks\, these approaches improve training stability\, mitigate non-stationarity\, and enable agents to solve complex sparse-reward tasks in both simulation and real-world robotic settings. More broadly\, this work argues that building intelligent robotic agents that can solve long-horizon tasks is not just a matter of better planning\, but of ensuring that high-level decisions remain aligned with what lower-level policies can actually achieve. \n  \nBio: Utsav Singh received his Ph.D. from the Department of Computer Science and Engineering at the Indian Institute of Technology (IIT) Kanpur\, advised by Dr. Vinay P. Namboodiri and Dr. Sunil E. Simon. He will be joining the University of Central Florida as a Postdoctoral Researcher in June 2026\, working with Dr. Mubarak Shah and Dr. Amrit Singh Bedi. Prior to his Ph.D.\, he received his M.Tech. from IIT Kanpur. His research interests span hierarchical reinforcement learning (HRL)\, embodied intelligence\, and bilevel optimization\, with a current focus on enabling language-guided robotic control and improving reasoning in large language models (LLMs) via bilevel actor-critic frameworks. His research has been published at leading venues including ICML\, NeurIPS\, ICLR\, and AAAI.
URL:https://homecse.iitd.ac.in/event/learning-hierarchical-control-via-feasible-subgoal-prediction-by-utsav-singh/
LOCATION:SIT 001\, Amar Nath and Shashi Khosla School of Information Technology\, IIT Delhi\, Hauz Khas\, New Delhi 110016\, India\, Delhi\, Delhi\, 110016\, India
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=Asia/Kolkata:20260430T163000
DTEND;TZID=Asia/Kolkata:20260430T173000
DTSTAMP:20261010T052227
CREATED:20260421T084518Z
LAST-MODIFIED:20260421T084518Z
UID:2507-1777566600-1777570200@homecse.iitd.ac.in
SUMMARY:Physical reasoning in Minds\, Brains\, and Machines by Dr. Pramod RT
DESCRIPTION:Online joining: https://teams.microsoft.com/meet/44299089959938?p=5wwN132pf54i4sVnyN \nAbstract: Successful engagement with the physical world involves perceiving the underlying structure\, predicting how things unfold\, and planning actions accordingly. This rich understanding and reasoning about our physical environment\, or ‘intuitive physics’\, develops early in infancy and is a core component of human cognition. While it seems easy for us to understand and interact in unfamiliar situations\, current machine learning systems are still far from achieving human-like generalizable performance. In this talk\, I will present: i) a set of non-invasive neuroimaging (functional Magnetic Resonance Imaging or fMRI) studies characterizing the brain basis of physical reasoning in humans\, ii) the first single-neuron level evidence for physical reasoning in the human brain using intracranial recordings\, and iii) ongoing work benchmarking latest AI models on various physical reasoning tasks. Together\, the results and methodology will help in not only understanding the neural mechanisms of physical reasoning but also discovering ways to bridge the reasoning gap between humans and AI. \nBio: RT Pramod is a computational cognitive neuroscientist at Massachusetts Institute of Technology whose research explores the neural and computational basis of physical scene understanding and reasoning. His work combines behavioral experiments\, computational modeling and neuroimaging to understand how humans perceive\, predict and plan in the world. Pramod obtained his Ph.D. from the Indian Institute of Science (IISc) working on compositional representations underlying visual object perception. His interests span visual cognition\, world models\, and the intersection of biological and machine intelligence.
URL:https://homecse.iitd.ac.in/event/physical-reasoning-in-minds-brains-and-machines-by-dr-pramod-rt/
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=Asia/Kolkata:20260424T120000
DTEND;TZID=Asia/Kolkata:20260424T130000
DTSTAMP:20261010T052227
CREATED:20260415T080647Z
LAST-MODIFIED:20260415T080647Z
UID:2495-1777032000-1777035600@homecse.iitd.ac.in
SUMMARY:Learning assessment-aware brain representations from multimodal neuroimaging data by Dr. Ishaan Batta
DESCRIPTION:Venue: SIT001 \nOnline joining: https://teams.microsoft.com/meet/48853006918605?p=788lqF84K1ykLq1rtg \nAbstract: Standard supervised learning on neuroimaging data optimizes for diagnostic prediction while yielding feature-level importance scores that lack network-level\, assessment-specific interpretability required for biomarker discovery; while unsupervised methods reduce data dimensions leading to loss of assessment-specific information. This talk presents frameworks developed towards addressing these gaps via biologically interpretable methodologies for neuroimaging data analysis. First\, a multimodal active subspace analysis framework to compute multiple salient directions that define the gradient space of a prediction function learned on brain features\, followed by repeated analysis to extract consistent and robust assessment-oriented subspace centers: compact multimodal representations of co-varying brain regions and functional connections maximally associated with a target clinical assessment. Second\, an interpretable deep learning framework\, constrained source-based salience\, that embeds active subspace learning and spatially constrained ICA directly into the saliency space of trained deep learning architectures\, producing network-level full-brain visualizations anchored around spatial brain templates. Lastly\, it will include some of the ongoing work on a conditional graph variational autoencoder that encodes static functional network connectivity in the brain into a structured latent space conditioned on demographic and cognitive variables\, enabling condition-specific reconstruction and identification of discriminative patterns of biological sex and fluid intelligence. Collectively\, these frameworks establish a principled methodology for learning brain representations that are simultaneously predictive\, network-interpretable\, and account for clinical observations. \nBio: Ishaan Batta is a postdoctoral research associate at the tri-institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS)\, GSU/GAtech/Emory\, Atlanta\, USA. His research lies at the intersection of machine learning and neuroscience\, with a focus on developing interpretable representation learning methods for high-dimensional multimodal neuroimaging data to uncover biologically meaningful signatures of brain disorders and cognitive function.\nIshaan completed his Ph.D. in Electrical and Computer Engineering at the Georgia Institute of Technology (Georgia Tech)\, USA in 2023\, advised by Dr. Vince Calhoun. His doctoral and postdoctoral work has introduced a suite of novel frameworks spanning active subspace learning\, deep learning-based interpretation\, and conditional generative modeling\, aimed at ensuring both predictive performance as well as neurobiological interpretability in brain imaging studies. Prior to his graduate studies\, Ishaan received a dual degree (B.Tech. and M.Tech.) in Computer Science and Engineering from the Indian Institute of Technology Delhi (IIT Delhi) in 2017
URL:https://homecse.iitd.ac.in/event/learning-assessment-aware-brain-representations-from-multimodal-neuroimaging-data-by-dr-ishaan-batta/
LOCATION:SIT 001\, Amar Nath and Shashi Khosla School of Information Technology\, IIT Delhi\, Hauz Khas\, New Delhi 110016\, India\, Delhi\, Delhi\, 110016\, India
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=Asia/Kolkata:20260420T153000
DTEND;TZID=Asia/Kolkata:20260420T163000
DTSTAMP:20261010T052227
CREATED:20260418T084932Z
LAST-MODIFIED:20260418T084932Z
UID:2505-1776699000-1776702600@homecse.iitd.ac.in
SUMMARY:Quantum Computing: Towards Advantage by Dhinakaran Vinayagamurthy
DESCRIPTION:Venue: Bharti501 \nAbstract: This talk will provide a perspective on where we are at IBM Quantum in building useful quantum computers. There are two main pillars: developing a quantum computing platform that scales beyond classical computers\, and discovering algorithms that leverage the strengths of this platform to deliver state-of-the-art methods for solving hard problems. The talk will also provide an overview of how tools available in Qiskit can be leveraged for research and development. \nBio: Dhinakaran Vinayagamurthy is a Researcher in the newly formed Quantum Computing research group at the IBM Research India lab in Bangalore\, and the Engagement Manager for the IIT Madras-IBM Quantum partnership. My research interests are in quantum error mitigation\, cryptography and security. At IBM\, I have worked on projects around blockchain interoperability\, encrypted databases\, IBM Blockchain Transparent Supply and privacy-preserving machine learning. I am also an IBM Quantum Senior Ambassador.
URL:https://homecse.iitd.ac.in/event/quantum-computing-towards-advantage-by-dhinakaran-vinayagamurthy/
LOCATION:Bharti 501\, IIT Campus\, Hauz Khas\, New Delhi
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=Asia/Kolkata:20260416T120000
DTEND;TZID=Asia/Kolkata:20260416T130000
DTSTAMP:20261010T052227
CREATED:20260415T080141Z
LAST-MODIFIED:20260415T080141Z
UID:2491-1776340800-1776344400@homecse.iitd.ac.in
SUMMARY:The Landscape of Exact Round Complexity in Secure Multi-Party Computation by Prof. Arpita Patra
DESCRIPTION:Venue: Bharti501\nAbstract: Secure Multi-Party Computation (MPC) is a central problem in cryptography\, often regarded as its holy grail. It enables a group of mutually distrusting data owners to jointly compute a function over their private inputs\, while revealing nothing beyond what is inherently implied by the output itself.\nRound complexity is one of the most fundamental efficiency measures in MPC\, capturing the minimal interaction required for secure computation. In this talk\, I will present a high-level overview of the evolution of research on round complexity in MPC and place my own contributions within this evolving landscape.\n\nBio: Arpita Patra is a Professor of Computer Science at the Indian Institute of Science (IISc). Her research focuses on cryptography\, with particular emphasis on the theoretical foundations and practical implementations of secure computation protocols. She has authored over 100 publications\, and her work has been recognized through numerous honors\, including the Prof. S. K. Chatterjee Award for Outstanding Woman Researcher/Industry Leader (IISc\, 2023)\, the Google Privacy Research Faculty Award (2023)\, the J.P. Morgan Chase Faculty Award (2022)\, the SONY Faculty Innovation Award (2021)\, the Google Research Award (2020)\, the NASI Young Scientist Platinum Jubilee Award (2018)\, the SERB Women Excellence Award (2016)\, and the INAE Young Engineer Award (2016). She is affiliated with leading scientific academies\, including the Indian Academy of Sciences (IAS)\, the Indian National Academy of Engineering (INAE)\, and The World Academy of Sciences (TWAS). She has coauthored two academic textbooks: Secure Multiparty Computation against Passive Adversaries (Springer\, 2023) and Fault Tolerant Distributed Consensus in Synchronous Networks (Springer\, 2025).
URL:https://homecse.iitd.ac.in/event/the-landscape-of-exact-round-complexity-in-secure-multi-party-computation-by-prof-arpita-patra/
LOCATION:Bharti 501\, IIT Campus\, Hauz Khas\, New Delhi
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=Asia/Kolkata:20260416T100000
DTEND;TZID=Asia/Kolkata:20260416T110000
DTSTAMP:20261010T052227
CREATED:20260415T081035Z
LAST-MODIFIED:20260415T081035Z
UID:2497-1776333600-1776337200@homecse.iitd.ac.in
SUMMARY:Challenges in scaling memory bandwidth in modern SoCs by Nithya Bashyam
DESCRIPTION:Venue: Bharti501\nAbstract: Memory bandwidth scaling has emerged as a critical bottleneck as compute capabilities continue to grow faster than DRAM performance. This talk examines why higher memory frequencies do not directly translate to higher delivered bandwidth\, focusing on bandwidth efficiency rather than peak bandwidth. It discusses key constraints arising from DRAM organization\, timing parameters\, power\, reliability and security\, and highlights the roles of memory controllers\, SoC architecture\, and workload behavior. The talk provides a framework to understand the architectural trade‑offs involved in sustaining memory bandwidth scaling in modern SoCs.\n\n\nBiography: A Principal Engineer in Client SoC Performance Architecture at Intel\, Nithya Bashyam holds an M.Tech. in Electronics Design from CEDT\, IISc. He joined Intel in 2004\, initially working on front‑end design for two years\, before moving in 2006 to the CPU SoC Architecture team. He contributed to the industry’s first CPU with integrated graphics and memory controller on Sandy Bridge\, released in 2011\, followed by work on a few generations of server CPUs. Since Skylake (2015)\, he has focused on client CPUs\, working across the core‑to‑memory path including cache\, ring\, memory controllers\, and aspects of the I/O subsystem. He has led memory subsystem performance for several generations of client SoCs and led SoC performance for Lunar Lake (2024) and Panther Lake (2025).
URL:https://homecse.iitd.ac.in/event/challenges-in-scaling-memory-bandwidth-in-modern-socs-by-nithya-bashyam/
LOCATION:Bharti 501\, IIT Campus\, Hauz Khas\, New Delhi
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=Asia/Kolkata:20260415T160000
DTEND;TZID=Asia/Kolkata:20260415T170000
DTSTAMP:20261010T052227
CREATED:20260415T080415Z
LAST-MODIFIED:20260415T080415Z
UID:2493-1776268800-1776272400@homecse.iitd.ac.in
SUMMARY:State of Confidential Computing by Dr. Kapil Vaswani
DESCRIPTION:Venue: Bharti501 \nAbstract: Over the last decade\, confidential computing has emerged as an advanced security and privacy primitive that can fundamentally change the nature of trust that we place in digital services. In this talk\, we will take a journey through the evolution of confidential computing and understand its current state and open problems that will need to be solved. Towards the end\, I will introduce SPARC\, a new research center working at the intersection of AI and security\, and our view of the opportunities that confidential computing creates for building the next generation of AI systems. \nBio: Kapil Vaswani is the founder of SPARC. He is a security researcher with 18 years of experience in research spanning computer architecture\, programming languages\, systems\, hardware security\, and AI security. At Microsoft Research\, he led research on problems in confidential computing\, privacy-preserving AI\, database security\, supply chain security\, and formally verified hardware. His research has been published in many top tier conferences such as POPL\, PLDI\, ASPLOS\, MICRO\, OSDI\, Oakland and USENIX Security. His work directly led to several products at Microsoft including SQL Server Always Encrypted\, Azure Confidential GPU VMs\, Azure AI Confidential Inferencing\, Azure Confidential Clean Rooms\, and Ad Selection API.  In his capacity as a volunteer with iSPIRT\, he has led the development of the DEPA Inferencing and Training Framework. ​Kapil Vaswani has a master’s and PhD from the Indian Institute of Science\, Bangalore.
URL:https://homecse.iitd.ac.in/event/state-of-confidential-computing-by-dr-kapil-vaswani/
LOCATION:Bharti 501\, IIT Campus\, Hauz Khas\, New Delhi
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=Asia/Kolkata:20260413T120000
DTEND;TZID=Asia/Kolkata:20260413T130000
DTSTAMP:20261010T052227
CREATED:20260407T073939Z
LAST-MODIFIED:20260407T073939Z
UID:2483-1776081600-1776085200@homecse.iitd.ac.in
SUMMARY:Advancing Safe Multimodal Intelligence by Dr. Pritam Sarkar
DESCRIPTION:Venue: Bharti-501/ MS Teams \nAbstract: Building artificial intelligence with meaningful real-world impact requires models that can understand and interact in both the virtual and physical world. This talk outlines four key capabilities necessary to achieve this: foundational world knowledge\, alignment with human values and expectations\, reasoning ability\, and the capacity to self-improve. The talk begins with an overview of our past contributions toward these capabilities\, with a particular focus on building foundational world knowledge in multimodal models and improving their alignment with human values and expectations. The next part of the talk delves into a fundamental challenge in current alignment approaches: the alignment tax. While existing methods aim to make models safer and more reliable\, they often degrade general capabilities—reducing response diversity and making models overly cautious or less useful. To address this\, we introduce Refined Regularized Preference Optimization (RRPO)\, a fine-grained alignment method. By penalizing only specific error tokens rather than entire responses\, RRPO mitigates harmful behaviors while simultaneously improving performance across diverse vision tasks. Finally\, the talk concludes with an overview of ongoing work on enabling stronger visual reasoning and outlines a research vision for developing machines that can adapt and improve over time\, fostering long-term usefulness and human–AI trust. \n\n \n \nBio: Pritam Sarkar is a Distinguished Postdoctoral Fellow at the Vector Institute and a Postdoctoral Research Fellow at the University of British Columbia\, where he works on multimodal AI\, especially with video\, image\, audio\, and language. He completed his PhD in September 2025 at Queen’s University\, Canada and during this time\, he was an intern at Google\, USA. His research has been recognized at leading venues including NeurIPS\, ICLR\, and AAAI\, with multiple Oral and Spotlight presentations. He received the IEEE Research Excellence Award in 2023 for his work on self-supervised learning. He actively serves the research community as an Area Chair and a Reviewer for leading conferences and journals such as NeurIPS\, CVPR\, and PAMI\, and is a strong proponent of open-source research. He is interested in developing safe and generalizable multimodal intelligence through algorithms that learn effectively with minimal human supervision. Find more: https://pritamsarkar.com/  \nHead Shot: https://pritamsarkar.com/assets/my_images/pp_square.jpg 
URL:https://homecse.iitd.ac.in/event/advancing-safe-multimodal-intelligence-by-dr-pritam-sarkar/
LOCATION:Bharti 501\, IIT Campus\, Hauz Khas\, New Delhi
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=Asia/Kolkata:20260406T110000
DTEND;TZID=Asia/Kolkata:20260406T120000
DTSTAMP:20261010T052227
CREATED:20260404T150052Z
LAST-MODIFIED:20260404T150052Z
UID:2480-1775473200-1775476800@homecse.iitd.ac.in
SUMMARY:Machine Learning under Adversaries: How Structure in Data Helps by Ambar Pal
DESCRIPTION:Venue: SIT001 \nAbstract: This talk overviews recent results in the theoretical foundations of adversarially robust machine learning. Modern ML classifiers can fail spectacularly when subject to specially crafted input-perturbations\, called adversarial examples. On the other hand\, humans are quite robust for several tasks involving vision. Motivated by this contrast\, in the first part of this talk we will take a deeper dive into the question of when exactly adversarial examples can be avoided. We will see that a key property of the data distribution — localization on small volume subsets of the input space — characterizes whether any robust classifier exists. In the second part of this talk\, we will empirically instantiate these results for a few localized data distributions\, and demonstrate that utilizing such structure in data leads to practical classifiers that enjoy better provable robustness guarantees in several regimes. This talk is based on work at NeurIPS ’23\, ’20 and TMLR ’23\, ’24. \n  \nBio: Ambar Pal is a scientist at Amazon Responsible AI. He received his PhD in Computer Science from the Johns Hopkins University. His current research is in the foundations of robust machine learning where he develops the theory and practice of tools that closely utilize structure in data for robust machine learning. His research has been awarded the CPAL rising star award and fellowships from JHU and Amazon.
URL:https://homecse.iitd.ac.in/event/machine-learning-under-adversaries-how-structure-in-data-helps-by-ambar-pal/
LOCATION:SIT 001\, Amar Nath and Shashi Khosla School of Information Technology\, IIT Delhi\, Hauz Khas\, New Delhi 110016\, India\, Delhi\, Delhi\, 110016\, India
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=Asia/Kolkata:20260316T120000
DTEND;TZID=Asia/Kolkata:20260316T130000
DTSTAMP:20261010T052227
CREATED:20260312T134932Z
LAST-MODIFIED:20260312T134932Z
UID:2437-1773662400-1773666000@homecse.iitd.ac.in
SUMMARY:Logical explorations for security theory by Prof. Vaishnavi Sundararajan
DESCRIPTION:Venue: Bharti 501 \nAbstract: Logics and proof theories play a large role in the formal study and analysis of systems\, especially for formal verification. The exact shape of the syntax and proof rules involved depend heavily on the systems being modelled and verified. In this talk\, we will introduce a logical syntax for communicated messages and an associated proof system originally used in the verification of cryptographic protocols\, dating back to a very robust model from 1983\, which captures even the operational aspects of today’s internet. We will show how to extend thissystem to be able to better handle protocols that involve the communication of certificates\, and investigate some of the various logical and algorithmic questions that manifest during this endeavour.
URL:https://homecse.iitd.ac.in/event/logical-explorations-for-security-theory-by-prof-vaishnavi-sundararajan/
LOCATION:Bharti 501\, IIT Campus\, Hauz Khas\, New Delhi
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=Asia/Kolkata:20260313T120000
DTEND;TZID=Asia/Kolkata:20260313T130000
DTSTAMP:20261010T052227
CREATED:20260309T161051Z
LAST-MODIFIED:20260309T161051Z
UID:2434-1773403200-1773406800@homecse.iitd.ac.in
SUMMARY:Genteel-Negotiator: LLM-enhanced mixture-of-expert-based reinforcement learning approach for polite negotiation dialogue by Dr. Mauajama Firdaus
DESCRIPTION:Venue: SIT001 \nAbstract : Developing intelligent negotiation dialogue systems that promote fair and sustainable outcomes is crucial for advancing automated negotiation for social good. Since effective negotiation requires balancing cooperation and competition while maintaining respect\, we propose GENTEEL-NEGOTIATOR\, a polite negotiation dialogue system for tourism and e-commerce domains. We introduce NEGOCHAT\, a tourism negotiation dataset\, and enrich it along with the Integrative Negotiation Dataset (IND) using diverse negotiation strategies. Built on an LLM-enhanced Mixture-of-Experts reinforcement learning framework\, the model incorporates dedicated experts for negotiation\, politeness\, and coherence\, guided by a reward function capturing strategy alignment\, politeness\, coherence\, and engagingness. Extensive automatic and human evaluations demonstrate its effectiveness in generating polite\, coherent\, and goal-oriented negotiation responses. \n\nBio: Dr. Mauajama Firdaus is an Assistant Professor in the Department of Computer Science and Engineering at the Indian Institute of Technology (ISM) Dhanbad. She completed her Ph.D. in Computer Science & Engineering from IIT Patna and pursued postdoctoral research at the University of Alberta\, Canada. Her research expertise lies in Natural Language Processing\, Multimodal and Multilingual AI\, Dialogue Systems\, Explainable AI\, and Affective Computing. Her work focuses on building empathetic\, polite\, and emotion-aware conversational AI systems\, with applications in social good\, mental health\, customer care\, and multilingual settings. She has published extensively in top-tier journals and conferences including IEEE\, ACM\, AAAI\, ACL\, EMNLP\, and Information Fusion\, and holds a US patent in spoken language understanding. She also serves as Associate Editor for reputed international journals published by Elsevier.
URL:https://homecse.iitd.ac.in/event/genteel-negotiator-llm-enhanced-mixture-of-expert-based-reinforcement-learning-approach-for-polite-negotiation-dialogue-by-dr-mauajama-firdaus/
LOCATION:SIT 001\, Amar Nath and Shashi Khosla School of Information Technology\, IIT Delhi\, Hauz Khas\, New Delhi 110016\, India\, Delhi\, Delhi\, 110016\, India
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=Asia/Kolkata:20260310T090000
DTEND;TZID=Asia/Kolkata:20260310T100000
DTSTAMP:20261010T052227
CREATED:20260302T102353Z
LAST-MODIFIED:20260302T102353Z
UID:2429-1773133200-1773136800@homecse.iitd.ac.in
SUMMARY:Integration of Structured Reasoning and Data-driven Learning for Acting and Planning by Dr. Sunandita Patra
DESCRIPTION:Teams Link: MS Teams\nAbstract: The talk will focus on enabling autonomous actors\, such as digital agents or robots\, to take deliberative actions towards achieving their long horizon goals\, in the face of uncertainty and dynamic events. Today\, dynamic events or failures often require human intervention\, system restarts\, retraining\, or redesign\, for example\, when robots get stuck in dead ends or digital agents collapse under unanticipated events. Existing methods either rely on rule-based reasoning\, where it is extremely difficult for human experts to compile and maintain a complete set of rules\, or black-box machine learning models that need to be trained extensively for individual tasks. Neither approach is well suited when actors are operating in a dynamically changing environment. This work aims to overcome these limitations by creating integrated planning and learning algorithms that are practical to be executed in real-world environments (spanning finance\, robotics and cybersecurity) by incorporating within a single framework: (a) deliberative acting\, (b) online planning\, and (c) data-driven learning from the actor’s experiences.\n\n\nBy integrating structured reasoning with data-driven learning\, the goal of this research is to push towards the next generation of autonomous systems\, general-purpose agentic AI that can plan and act deliberately across a set of diverse tasks and domains.\n\n \nBio: Sunandita Patra is a Research Lead at J. P. Morgan AI Research\, Chicago\, USA. Her research interests include the integration of acting\, planning\, and machine learning\, focusing on finance\, cybersecurity\, and robotics domains. She completed her PhD and PostDoc in Computer Science at the University of Maryland\, College Park\, and holds BTech and MTech degrees in Computer Science and Engineering from IIT Kharagpur.  Her work received the Best Student Paper Honorable Mention Award at ICAPS 2020\, and the Best Student Paper Finalist Award (Top 3) at ECMR 2025. For more details\, please visit https://sunanditapatra.wixsite.com/camp
URL:https://homecse.iitd.ac.in/event/integration-of-structured-reasoning-and-data-driven-learning-for-acting-and-planning-by-dr-sunandita-patra/
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=Asia/Kolkata:20260306T120000
DTEND;TZID=Asia/Kolkata:20260306T130000
DTSTAMP:20261010T052227
CREATED:20260303T103646Z
LAST-MODIFIED:20260303T103646Z
UID:2432-1772798400-1772802000@homecse.iitd.ac.in
SUMMARY:Neural Circuit Discovery via Representation and Dynamics by Savik Kinger
DESCRIPTION: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 talk I use whole-brain recordings from C. elegans\, a canonical neurobiological system\, as a concrete testbed for “circuit interpretability.” I then introduce two complementary inference approaches for turning high-dimensional activity data into mechanistic structure. Approach 1 treats circuit discovery as a representation problem: learn time-varying functional structure and uncover recurring\, stimulus-dependent modules rather than a single static connectivity map. Approach 2 treats circuit discovery as a dynamics problem: go beyond correlation to estimate directed\, time-lagged influence—i.e.\, which units appear to drive others and over what delays—using modern score-based generative modeling ideas. I will show how these ML methods produce testable hypotheses for biologists and\, potentially\, offer new avenues for understanding complex networks in AI. \nBio: Savik Kinger is a PhD candidate in Computer Science at Yale University\, advised by Steven Zucker. His research focuses on developing methods to analyze biological and artificial neural networks\, integrating ideas from machine learning\, dynamical systems\, and causal inference. He received Bachelor’s degrees in Math and Computer Science from Columbia University. He is supported by a Nathan Hale fellowship.
URL:https://homecse.iitd.ac.in/event/neural-circuit-discovery-via-representation-and-dynamics-by-savik-kinger/
LOCATION:SIT 113\, Amar Nath and Shashi Khosla School of Information Technology\, Indian Institute of Technology\, Delhi\, Hauz Khas\, New Delhi\, Delhi\, 110016\, India
CATEGORIES:Seminars
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=Asia/Kolkata:20260219T110000
DTEND;TZID=Asia/Kolkata:20260219T120000
DTSTAMP:20261010T052227
CREATED:20260217T085749Z
LAST-MODIFIED:20260217T085749Z
UID:2426-1771498800-1771502400@homecse.iitd.ac.in
SUMMARY:TESSERA: Programming Petabytes of Earth Observations using Foundation Models by Prof. Anil Madhavapeddy
DESCRIPTION:Venue. Bharti 501\n\nAbstract. We present TESSERA\, a pixel-wise foundation model for multi-modal (Sentinel-1/2) earth observation time series that learns robust\, label-efficient embeddings.  Our goal with TESSERA is to make manipulating global satellite intelligence as easy as LLMs did for natural language! Towards this we release global\, annual\, 10m\, pixel-wise embeddings together with open weights and code and lightweight adaptation heads\, providing practical tooling for large-scale retrieval and inference at planetary scale.  As with any good foundation model\, there are a staggering array of downstream tasks which can benefit. TESSERA embeddings deliver state-of-the-art accuracy with high label efficiency across diverse classification\, segmentation\, and regression tasks.\n\nIn this talk\, I’ll take you through an array of problems our users are applying it to\, ranging from the ecological to the urban to the temporal. By the end of the talk\, we aim to have you identify a seemingly impossible spatial problem that is now within range to solve yourself using our easy-to-install Python package\, geotessera. Bring your favourite coding agents!\n\nSpeaker: Anil Madhavapeddy\, Professor of Planetary Computing\, University of Cambridge
URL:https://homecse.iitd.ac.in/event/tessera-programming-petabytes-of-earth-observations-using-foundation-models-by-prof-anil-madhavapeddy/
LOCATION:Bharti 501\, IIT Campus\, Hauz Khas\, New Delhi
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BEGIN:VEVENT
DTSTART;TZID=Asia/Kolkata:20260212T120000
DTEND;TZID=Asia/Kolkata:20260212T130000
DTSTAMP:20261010T052227
CREATED:20260204T131611Z
LAST-MODIFIED:20260204T131643Z
UID:2416-1770897600-1770901200@homecse.iitd.ac.in
SUMMARY:Abstractions for expressive\, extensible\, and scalable root cause analysis by Vipul Harsh
DESCRIPTION:Venue: Bharti501 \nAbstract: 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 point-solution algorithms to a systems-first approach. I will introduce MoCE: a Mixture-of-Experts (MoE) framework that provides high-level abstractions for failure diagnosis. This framework enables developers to express complex troubleshooting logic succinctly using MoCE’s domain specific operators while providing the underlying systems support for scalable telemetry processing. Finally\, I will briefly describe how these abstractions empower reliable\, autonomous agents to perform interactive diagnosis and discuss ongoing and promising future work based on these ideas. \nBio: Vipul Harsh is a postdoctoral researcher at Conviva with Vyas Sekar and Hui Zhang and a visiting researcher at Carnegie Mellon University (CMU). His research lies at the intersection of networked systems\, AI\, and theory. His works span design of reliable agents for interactive troubleshooting\, systems for failure diagnosis in networked systems\, datacenter topology\, distributed monitoring\, and parallel algorithms. His research has been published in top-tier CS conferences (SIGCOMM\, NSDI\, SPAA among others) and his projects have been adopted into real-world products. He completed his Ph.D. from UIUC where he worked with Brighten Godfrey and holds an undergraduate degree from IIT Bombay. His thesis was nominated by UIUC for the ACM SIGCOMM dissertation award.
URL:https://homecse.iitd.ac.in/event/2416/
LOCATION:Bharti 501\, IIT Campus\, Hauz Khas\, New Delhi
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=Asia/Kolkata:20260206T160000
DTEND;TZID=Asia/Kolkata:20260206T170000
DTSTAMP:20261010T052227
CREATED:20260206T095221Z
LAST-MODIFIED:20260206T095221Z
UID:2419-1770393600-1770397200@homecse.iitd.ac.in
SUMMARY:Molecular Machine Learning for Chemical Catalysis by Dr. Sukriti Singh
DESCRIPTION:Venue: SIT001\nAbstract: The development of new reaction methodology could become a tedious task demanding both time and resources. The application of machine learning (ML) approaches for reaction optimization and prediction can make a significant impact on efficient exploration of the high-dimensional chemical space. But the direct adaptation of ML as used in well-developed domains\, is unlikely to succeed in reaction discovery. Some of the challenges stem from ineffective featurization of the molecular space\, unavailability of quality data and its distribution. Given these backgrounds\, rendering ML tools conducive for reactions is an exciting as well as challenging endeavor at the same time.\nIn this talk\, I will present molecular machine learning strategies specifically designed for small-data reaction discovery\, typically involving only hundreds to a few thousand data points. I will first discuss feature engineering approaches based on quantum-chemically derived physical organic descriptors\, illustrated through catalytic asymmetric hydrogenation of imines and alkenes for predicting enantioselectivity. I will then introduce feature learning methods that learn molecular representations directly from data and demonstrate their effectiveness in predicting reaction yield and enantioselectivity across diverse catalytic transformations.\n\nTo address data scarcity\, I will describe a transfer learning framework in which a chemical language model is trained on large number of molecules and fine-tuned on a focused library of desired reactions. Finally\, I will briefly introduce a meta-learning workflow that leverages literature-derived reaction data to identify shared reaction features\, enabling accurate outcome prediction with only a few experimental examples. Overall\, this talk will highlight how technically sound deployment of molecular machine learning tools can guide reaction development and help us get closer to sustainable practices by reducing the number of heuristic and empirical steps.\n\nBio: Sukriti Singh received her M.Sc. and Ph.D. degrees from the Department of Chemistry\, IIT Bombay in 2022 under the supervision of Prof. Raghavan B. Sunoj. She received the Naik and Rastogi Award for Excellence in Ph.D. Research. She was a postdoctoral research associate with Prof. J. M. Hernandez-Lobato at the Department of Engineering\, University of Cambridge. Her research interests involve density functional theory studies of catalytic reactions and developing machine learning methods to tackle low-data situations aimed at accelerating the exploration of chemical reaction space of high contemporary interest.
URL:https://homecse.iitd.ac.in/event/molecular-machine-learning-for-chemical-catalysis-by-dr-sukriti-singh/
LOCATION:SIT 001\, Amar Nath and Shashi Khosla School of Information Technology\, IIT Delhi\, Hauz Khas\, New Delhi 110016\, India\, Delhi\, Delhi\, 110016\, India
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BEGIN:VEVENT
DTSTART;TZID=Asia/Kolkata:20260205T120000
DTEND;TZID=Asia/Kolkata:20260205T130000
DTSTAMP:20261010T052227
CREATED:20260131T075508Z
LAST-MODIFIED:20260131T075508Z
UID:2410-1770292800-1770296400@homecse.iitd.ac.in
SUMMARY:Approximately Packing Dijoins Via Nowhere-Zero Flows by Dr. Ravi
DESCRIPTION:Venue: Bharti501 \nAbstract: In a digraph\, a dicut is a cut where all the arcs cross in one direction. A dijoin is a subset of arcs that intersects each dicut. Woodall conjectured in 1976 that in every digraph\, the minimum size of a dicut equals to the maximum number of disjoint dijoins. By building connections with nowhere-zero k-flows\, we prove that every digraph with minimum dicut size $\tau$ contains $\lfloor \tau/k \rfloor$ disjoint dijoins if the underlying undirected graph admits a nowhere-zero k-flow. \nJoint work with Gérard Cornuéjols (CMU) and Siyue Liu (CMU) \nSpeaker Bio: Dr. Ravi is the Vasantrao Dempo Professor of Operations Research and Computer Science at Carnegie Mellon University. His research is on models\, methods and applications of discrete optimization and their application to business and technological systems. He has published widely in diverse areas ranging from theoretical computer science to Operations and Marketing. In Computer Science\, his main research interests are in approximation algorithms and network optimization.
URL:https://homecse.iitd.ac.in/event/approximately-packing-dijoins-via-nowhere-zero-flows-by-dr-ravi/
LOCATION:Bharti 501\, IIT Campus\, Hauz Khas\, New Delhi
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END:VCALENDAR