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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:20250101T000000
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BEGIN:VEVENT
DTSTART;TZID=Asia/Kolkata:20251201T110000
DTEND;TZID=Asia/Kolkata:20251201T120000
DTSTAMP:20261010T153547
CREATED:20251124T162136Z
LAST-MODIFIED:20251124T162136Z
UID:2215-1764586800-1764590400@homecse.iitd.ac.in
SUMMARY:Building the Quantum Software Stack – and Verifying It by Prof. Ramanathan S. Thinniyam
DESCRIPTION:Venue: Bhart501\nAbstract: Quantum computing is approaching an inflection point. Global investment is scaling up\, hardware platforms are maturing – and the software stack is beginning to form. But what exactly is this stack? Who is building it\, and what remains to be done? In the first part of this talk\, I will give an overview of the emerging quantum software ecosystem: from quantum programming languages and compilers to simulators\, error mitigation\, and pulse-level control. I will highlight some of the current architectural directions in hardware (e.g.\, superconducting vs. neutral atom platforms) and outline the challenges of building reliable abstractions on top of noisy\, hardware-constrained systems.\n\nIn the second part\, I will shift focus to my own research: the use of formal methods—particularly automata-theoretic techniques—in reasoning about quantum circuits. I’ll present recent work on verifying properties of quantum circuits and how ideas from classical program analysis can be extended to this new domain. Throughout\, I’ll try to convey both the excitement and the difficulty of building a rigorous foundation for quantum software. \nBio: Ramanathan S. Thinniyam is an assistant professor in the Division of Computer Systems\, Department of Information Technology\, Uppsala University. Prior to joining Uppsala\, he was a postdoc at the Max Planck Institute for Software Systems\, Kaiserslautern\, and a visiting fellow at the Chennai Mathematical Institute. He obtained his Ph.D. from the Institute of Mathematical Sciences\, Chennai. He works on theoretical problems arising from verification in the classical settings\, with a recent focus on verification of quantum circuits. He is broadly interested in topics at the intersection of logic\, computation\, and mathematics.  
URL:https://homecse.iitd.ac.in/event/building-the-quantum-software-stack-and-verifying-it-by-prof-ramanathan-s-thinniyam/
LOCATION:Bharti 501\, IIT Campus\, Hauz Khas\, New Delhi
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=Asia/Kolkata:20251201T120000
DTEND;TZID=Asia/Kolkata:20251201T130000
DTSTAMP:20261010T153547
CREATED:20251129T134926Z
LAST-MODIFIED:20251129T134926Z
UID:2220-1764590400-1764594000@homecse.iitd.ac.in
SUMMARY:The End of "Seeing is Believing" - Securing Identity in the Age of Generative AI by Richa Singh
DESCRIPTION:Venue: SIT001 \nOnline Link: https://teams.microsoft.com/meet/4492945053702?p=Gz8szja9ebX7NoIitC \nAbstract: “In an era where AI can synthesize hyper-realistic faces and voices\, does the axiom ‘seeing is believing’ still hold water?” This question strikes at the very foundation of digital trust. Over a two-decade journey in biometrics\, contributing to large-scale systems like India’s Aadhaar and national security applications\, I have watched the challenges evolve from simple identification to complex verification. We have moved from solving “hard” recognition cases\, such as matching photos to forensic sketches or identifying individuals after plastic surgery and severe injury\, to confronting a far more insidious threat: the weaponization of generative AI. In this talk\, I will dissect the mechanics of this new adversarial landscape. I will present our work on detecting multimodal forgeries\, analyzing subtle visual artifacts\, temporal inconsistencies\, and acoustic anomalies in multilingual synthetic speech. Finally\, I will widen the lens to discuss the policy frameworks required to survive this shift—proposing a roadmap for digital identity that balances technical robustness with fairness\, bias mitigation\, and data sovereignty. \nBiography: Richa Singh is a Professor in the Department of Computer Science and Engineering at IIT Jodhpur. Her research spans responsible artificial intelligence\, machine learning\, pattern recognition\, biometrics\, and medical image analysis. She is a Fellow of the IEEE\, IAPR\, NASI\, and INAE\, and is an ACM Distinguished Member. Her honors include the NASSCOM AI Gamechangers Award and the Facebook Award for Ethics in AI. She is Founding Co-Editor-in-Chief of ACM AI Letters and Associate Editor-in-Chief of Pattern Recognition. She has served as organizing committee member of several conferences including PC Co-Chair of CVPR 2022and also served as Vice President (Publications) of the IEEE Biometrics Council.
URL:https://homecse.iitd.ac.in/event/the-end-of-seeing-is-believing-securing-identity-in-the-age-of-generative-ai-by-richa-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:20251201T160000
DTEND;TZID=Asia/Kolkata:20251201T170000
DTSTAMP:20261010T153547
CREATED:20251129T134307Z
LAST-MODIFIED:20251129T134307Z
UID:2218-1764604800-1764608400@homecse.iitd.ac.in
SUMMARY:Unifying Large Language Models and Knowledge Graphs for Faithful and Interpretable Reasoning by Gholamreza (Reza) Haffari
DESCRIPTION:Venue: SIT001 \nAbstract: Large Language Models (LLMs) demonstrate strong general reasoning ability\, yet still suffer from hallucination\, limited faithfulness\, and a lack of interpretability—especially in knowledge-intensive or domain-specific settings. Knowledge Graphs (KGs)\, on the other hand\, provide structured\, explicit\, and verifiable representations of facts\, but are incomplete and lack linguistic flexibility. This talk presents recent advances in unifying these two paradigms to achieve trustworthy and interpretable reasoning. In the first part\, I will introduce Reasoning on Graphs (RoG) and Graph-Constrained Reasoning (GCR)\, two frameworks that guide or constrain LLM reasoning using KG structure. RoG enables planning–retrieval–reasoning with faithful relation paths\, while GCR enforces KG-valid reasoning during decoding\, eliminating hallucinated reasoning paths and improving accuracy and interpretability. The second part of the talk presents GFM-RAG\, a graph foundation model trained on 60 diverse KGs with over 14 million triples for efficient\, multi-hop retrieval-augmented generation. GFM-RAG achieves state-of-the-art performance across multiple QA benchmarks and generalizes zero-shot to new datasets. Together\, these methods highlight a path toward unified\, scalable\, and reliable KG-LLM reasoning. \nBio:  Gholamreza (Reza) Haffari is a Professor in the Department of Data Science and Artificial Intelligence at Monash University\, Australia. He is a former ARC Future Fellow and previously served as Director of the Vision and Language Group. His research sits at the intersection of Natural Language Processing\, Deep Learning\, and Machine Learning\, with funding from ARC\, Google Research\, Amazon\, eBay\, Adobe\, and other industry partners. Reza also serves as the Chief AI Scientist at Openstream AI.
URL:https://homecse.iitd.ac.in/event/unifying-large-language-models-and-knowledge-graphs-for-faithful-and-interpretable-reasoning-by-gholamreza-reza-haffari/
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:20251209T140000
DTEND;TZID=Asia/Kolkata:20251209T150000
DTSTAMP:20261010T153547
CREATED:20251208T052758Z
LAST-MODIFIED:20251208T052758Z
UID:2233-1765288800-1765292400@homecse.iitd.ac.in
SUMMARY:Approximating Optimal Broadcast of Files in a Hose-Model Network
DESCRIPTION:Speaker:Sukriti Gupta (PhD student)\, CSE Dept.\, IIT Delhi\nAbstract -\nWe consider the problem of file sharing among peers who are connected to \na common core network through links of differing upload and download \ncapacities\, as is the case in networks provisioned according to the hose \nmodel. The file is assumed to be divided into equal-sized chunks\, and a \npeer can start sending a “chunk” of the file to another peer only after \nit has received the entire chunk. The objective is to share a chunk\, \ninitially residing on one of the peers\, with all other peers in the \nleast time possible. Peers can simultaneously send/receive parts of a \nchunk to/from multiple peers\, subject to the upload and download \ncapacity constraints.
URL:https://homecse.iitd.ac.in/event/approximating-optimal-broadcast-of-files-in-a-hose-model-network/
LOCATION:Bharti 501\, IIT Campus\, Hauz Khas\, New Delhi
CATEGORIES:Seminars
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DTSTART;TZID=Asia/Kolkata:20251211T110000
DTEND;TZID=Asia/Kolkata:20251211T170000
DTSTAMP:20261010T153547
CREATED:20251209T113155Z
LAST-MODIFIED:20251209T113155Z
UID:2239-1765450800-1765472400@homecse.iitd.ac.in
SUMMARY:Multiparty Session Types: Separation and Encodability Results by Prof. Nobuko Yoshida
DESCRIPTION:Venue: Bharti501 / Teams link will also be shared \nAbstract: Multiparty session types (MPST) are a type discipline for enforcing the structured\, deadlock-free communication of concurrent and message-passing programs. Traditional MPST have a limited form of choice in which alternative communication possibilities are offered by a single participant and selected by another. Mixed choice multiparty session types (MCMP) extend the choice construct to include both selections and offers in the same choice. This talk presents a mixed-choice synchronous multiparty session calculus and its typing system\, which guarantees communication safety and deadlock-freedom. We then talk of expressiveness of nine subcalculi of the MCMP-calculus by examining their encodability (there exists a good encoding from one to another) and separation (there exists no good encoding from one calculus to another). The highlight is that the binary (2-party) mixed sessions by Casal et al. (2022) are strictly less expressive than the MCMP-calculus. \n\nJoint work with Kirstin Peters appeared in LICS’24 (https://arxiv.org/abs/2405.08104) \nAbout the speaker. Nobuko Yoshida is Christopher Strachey Chair of Computer Science in University of Oxford. She is an EPSRC Established Career Fellow and an Honorary Fellow at Glasgow University. Last 10 years\, her main research interests are theories and applications of protocol specification and verification. She introduced multiparty session types [ POPL’08\, JACM ] which received the Most Influential POPL Paper Award in 2018 (judged by its influence over the last decade). This work enlarged the community and widened the scope of applications of session types\, e.g. runtime monitoring based on Scribble (co-developed with Red Hat) has been deployed to other projects such as cyberinfrastructure in the US Ocean Observatories Initiative (OOI); and widened the scope of her research areas. She received the Test-of-time-award from PPDP’24 and the best paper awards from CC’20\, COORDINATION’23 and DisCoTech’23. She received the third Suffrage Science Award for Mathematics and Computing from MRC for her STEM activity. She is an editor of ACM Transactions on Programming Languages and Systems\, Theoretical Computer Science\,  ACM Formal Aspects of Computing\, Mathematical Structures in Computer Science\, Journal of Logical Algebraic Methods in Programming\, and the chief editor of The Computer-aided Verification and Concurrency Column for EATCS Bulletin.
URL:https://homecse.iitd.ac.in/event/multiparty-session-types-separation-and-encodability-results-by-prof-nobuko-yoshida/
LOCATION:Bharti 501\, IIT Campus\, Hauz Khas\, New Delhi
CATEGORIES:Seminars
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=Asia/Kolkata:20251212T110000
DTEND;TZID=Asia/Kolkata:20251212T120000
DTSTAMP:20261010T153547
CREATED:20251207T103353Z
LAST-MODIFIED:20251207T103353Z
UID:2230-1765537200-1765540800@homecse.iitd.ac.in
SUMMARY:Relational Transformer: Toward Zero-Shot Foundation Models for Relational Data by Rishabh Ranjan
DESCRIPTION:Venue: Bharti501 \nAbstract: Pretrained transformers readily adapt to new sequence modeling tasks via zero-shot prompting\, but relational domains still lack architectures that transfer across datasets and tasks. The core challenge is the diversity of relational data\, with varying heterogeneous schemas\, graph structures and functional dependencies. In this talk\, I will present the Relational Transformer (RT) architecture\, which can be pretrained on diverse relational databases and directly applied to unseen datasets and tasks without task- or dataset-specific fine-tuning\, or retrieval of in-context examples. RT (i) tokenizes cells with table/column metadata\, (ii) is pretrained via masked token prediction\, and (iii) utilizes a novel Relational Attention mechanism over columns\, rows\, and primary-foreign key links. Pretrained on RelBench datasets spanning tasks such as churn and sales forecasting\, RT attains strong zero-shot performance\, averaging 93% of fully supervised AUROC on binary classification tasks with a single forward pass of a 22M parameter model\, as opposed to 84% for a 27B LLM. Fine-tuning yields state-of-the-art results with high sample efficiency. Our experiments show that RT’s zero-shot transfer harnesses task-table context\, relational attention patterns and schema semantics. Overall\, RT provides a practical path toward foundation models for relational data. https://arxiv.org/abs/2510.06377 \nBio: Rishabh Ranjan is a 3rd year PhD student at Stanford University co-advised by Jure Leskovec and Carlos Guestrin and supported by the Amazon Core AI Fellowship. His research is on building foundation models for relational data\, which includes databases\, tables\, time series and graphs. Before Stanford\, he has spent time at CMU and IIT Delhi\, where he was the President’s Gold Medalist for 2022. https://rishabh-ranjan.github.io
URL:https://homecse.iitd.ac.in/event/relational-transformer-toward-zero-shot-foundation-models-for-relational-data-by-rishabh-ranjan/
LOCATION:Bharti 501\, IIT Campus\, Hauz Khas\, New Delhi
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=Asia/Kolkata:20251215T120000
DTEND;TZID=Asia/Kolkata:20251215T130000
DTSTAMP:20261010T153547
CREATED:20251209T045950Z
LAST-MODIFIED:20251209T045950Z
UID:2235-1765800000-1765803600@homecse.iitd.ac.in
SUMMARY:Matroids are Equitable by Hannaneh Akrami
DESCRIPTION:Abstract: We show that if the ground set of a matroid can be partitioned into k≥2 bases\, then for any given subset S of the ground set\, there is a partition into k bases such that the sizes of the intersections of the bases with S may differ by at most one. This settles the matroid equitability conjecture by Fekete and Szabó (Electron.~J.~Comb.~2011) in the affirmative. We also investigate equitable splittings of two disjoint sets S1 and S2\, and show that there is a partition into k bases such that the sizes of the intersections with S1 may differ by at most one and the sizes of the intersections with S2 may differ by at most two; this is the best possible one can hope for arbitrary matroids. \nWe also derive applications of this result into matroid constrained fair division problems. We show that there exists a matroid-constrained fair division that is envy-free up to 1 item if the valuations are identical and tri-valued additive. We also show that for bi-valued additive valuations\, there exists a matroid-constrained allocation that provides everyone their maximin share. \nThis is based on joint work with Siyue Liu\, Roshan Raj\, and László A. Végh. \nSpeaker Bio: Hannaneh Akrami is a postdoctoral fellow at the University of Bonn and a Minerva Fast-track fellow at MPI-Informatik. She obtained her PhD from the University of Saarlandes and MPI-Informatik in 2024 and completed a BSc from Sharif University of Technology in 2019. Her interests are in fair division\, Algorithmic Game theory\, Combinatorics\, Graph theory and Approximation algorithms. \n 
URL:https://homecse.iitd.ac.in/event/matroids-are-equitable-by-hannaneh-akrami/
LOCATION:Bharti 501\, IIT Campus\, Hauz Khas\, New Delhi
CATEGORIES:Seminars
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