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X-WR-CALDESC:Events for Computer Science and Engineering
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TZID:Asia/Kolkata
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DTSTART:20250101T000000
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DTSTART;TZID=Asia/Kolkata:20251201T110000
DTEND;TZID=Asia/Kolkata:20251201T120000
DTSTAMP:20261010T162542
CREATED:20251124T162136Z
LAST-MODIFIED:20251124T162136Z
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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
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DTSTART;TZID=Asia/Kolkata:20251201T120000
DTEND;TZID=Asia/Kolkata:20251201T130000
DTSTAMP:20261010T162542
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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DTSTART;TZID=Asia/Kolkata:20251201T160000
DTEND;TZID=Asia/Kolkata:20251201T170000
DTSTAMP:20261010T162542
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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