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X-ORIGINAL-URL:https://homecse.iitd.ac.in
X-WR-CALDESC:Events for Computer Science and Engineering
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TZID:Asia/Kolkata
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TZOFFSETFROM:+0530
TZOFFSETTO:+0530
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DTSTART:20260101T000000
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BEGIN:VEVENT
DTSTART;TZID=Asia/Kolkata:20260810T120000
DTEND;TZID=Asia/Kolkata:20260810T130000
DTSTAMP:20260923T012339
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
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BEGIN:VEVENT
DTSTART;TZID=Asia/Kolkata:20260812T120000
DTEND;TZID=Asia/Kolkata:20260812T130000
DTSTAMP:20260923T012339
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/
LOCATION:Delhi
CATEGORIES:Seminars
ORGANIZER;CN="Subodh Sharma":MAILTO:svs@cse.iitd.ac.in
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BEGIN:VEVENT
DTSTART;TZID=Asia/Kolkata:20260813T120000
DTEND;TZID=Asia/Kolkata:20260813T130000
DTSTAMP:20260923T012339
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/
LOCATION:Delhi
CATEGORIES:Seminars
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BEGIN:VEVENT
DTSTART;TZID=Asia/Kolkata:20260814T120000
DTEND;TZID=Asia/Kolkata:20260814T130000
DTSTAMP:20260923T012339
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
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DTSTART;TZID=Asia/Kolkata:20260814T140000
DTEND;TZID=Asia/Kolkata:20260814T150000
DTSTAMP:20260923T012339
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
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BEGIN:VEVENT
DTSTART;TZID=Asia/Kolkata:20260819T110000
DTEND;TZID=Asia/Kolkata:20260819T120000
DTSTAMP:20260923T012339
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
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BEGIN:VEVENT
DTSTART;TZID=Asia/Kolkata:20260825T120000
DTEND;TZID=Asia/Kolkata:20260825T130000
DTSTAMP:20260923T012339
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
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