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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:20260406T110000
DTEND;TZID=Asia/Kolkata:20260406T120000
DTSTAMP:20261010T121637
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:20260413T120000
DTEND;TZID=Asia/Kolkata:20260413T130000
DTSTAMP:20261010T121637
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:20260415T160000
DTEND;TZID=Asia/Kolkata:20260415T170000
DTSTAMP:20261010T121637
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
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BEGIN:VEVENT
DTSTART;TZID=Asia/Kolkata:20260416T100000
DTEND;TZID=Asia/Kolkata:20260416T110000
DTSTAMP:20261010T121637
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:20260416T120000
DTEND;TZID=Asia/Kolkata:20260416T130000
DTSTAMP:20261010T121637
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:20260420T153000
DTEND;TZID=Asia/Kolkata:20260420T163000
DTSTAMP:20261010T121637
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
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DTSTART;TZID=Asia/Kolkata:20260424T120000
DTEND;TZID=Asia/Kolkata:20260424T130000
DTSTAMP:20261010T121637
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:20260430T163000
DTEND;TZID=Asia/Kolkata:20260430T173000
DTSTAMP:20261010T121637
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/
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