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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
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DTSTART:20250101T000000
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BEGIN:VEVENT
DTSTART;TZID=Asia/Kolkata:20250806T120000
DTEND;TZID=Asia/Kolkata:20250806T130000
DTSTAMP:20261012T022647
CREATED:20250730T061826Z
LAST-MODIFIED:20250730T165814Z
UID:1727-1754481600-1754485200@homecse.iitd.ac.in
SUMMARY:Rank Aggregation and Fairness by Diptarka Chakraborty
DESCRIPTION:Abstract: Aggregating multiple input rankings over a set of candidates to generate a consensus ranking is one of the fundamental ranking problems\, having many applications in social choice theory\, hiring\, college admission\, web search\, and databases. However\, the optimal consensus ranking might be biased against any individual candidate or candidates belonging to certain marginalized communities or groups. This has motivated studies of the rank aggregation problem from the fairness perspective. While finding a consensus ranking\, the additional objective is to ensure fair representation of each group in the top positions of the final aggregated ranking. In this talk\, we will discuss various algorithms to find such a fair ranking approximately.\n\nSpeaker: Diptarka Chakraborty is an Assistant Professor at the National University of Singapore. He did his Ph.D. at the Indian Institute of Technology\, Kanpur. Before joining NUS\, he spent two years at Charles University\, Prague\, and then almost a year at Weizmann Institute of Science\, Israel\, as a post-doctoral fellow. His research interest mostly lies in theoretical computer science\, more specifically\, algorithms on large data sets\, approximation algorithms\, sublinear algorithms\, string matching algorithms\, and graph algorithms. He is a recipient of the best paper award at FOCS 2018 and the Google South & Southeast Asia Research Award 2022.
URL:https://homecse.iitd.ac.in/event/rank-aggregation-and-fairness/
LOCATION:Bharti 501\, IIT Campus\, Hauz Khas\, New Delhi
CATEGORIES:Seminars
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DTSTART;TZID=Asia/Kolkata:20250808T120000
DTEND;TZID=Asia/Kolkata:20250808T130000
DTSTAMP:20261012T022647
CREATED:20250804T083721Z
LAST-MODIFIED:20250804T083759Z
UID:1754-1754654400-1754658000@homecse.iitd.ac.in
SUMMARY:Multiturn Evals (and RL) for LLMs by Kartikeya Badola
DESCRIPTION:Title: Multiturn Evals (and RL) for LLMs \nDetails: 8th August\, 12 pm\, SIT001 \nAbstract: LLMs often fail at multi-step tasks requiring memory and strategic planning\, a gap not captured by traditional single-turn evals. To address this\, we’ve developed a suite of human and automated evals that stress test Gemini on these capabilities. This talk will cover the motivation and design behind these evals\, a discussion on latest results and will also touch upon some of the early promising experiments using multiturn RL methods to address some of these losses. \nBio: Kartikeya Badola is a Software Engineer at Google DeepMind in London\, where he works with the Gemini evals and Gemini thinking teams. Prior to this\, he was with Google Research in India\, working on multilingual semantic parsing. Kartikeya is a graduate of IIT Delhi\, where he worked with Prof. Mausam and Prof. Parag Singla on Distantly Supervised Relation Extraction. \n 
URL:https://homecse.iitd.ac.in/event/multiturn-evals-and-rl-for-llms-by-kartikeya-badola/
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:20250811T120000
DTEND;TZID=Asia/Kolkata:20250811T130000
DTSTAMP:20261012T022647
CREATED:20250809T094501Z
LAST-MODIFIED:20250809T094501Z
UID:1770-1754913600-1754917200@homecse.iitd.ac.in
SUMMARY:Explainable AI for Malware Analysis by Mohd Saqib
DESCRIPTION:Title: Explainable AI for Malware Analysis \n  \nAbstract: In recent years\, explainable artificial intelligence (XAI) has become a critical component of ensuring transparency and trust in machine learning systems\, particularly in high-stakes domains like cybersecurity. This talk will begin with a basic introduction to XAI\, highlighting its importance in understanding model decisions\, especially in the context of malware detection. I will then introduce GAGE (Genetic Algorithm-based Graph Explainer)\, a novel framework specifically designed for malware analysis. GAGE utilizes graph-based representations of malware features and applies a genetic algorithm to generate meaningful explanations for model predictions. This approach allows for both global and local interpretability of malware detection models\, making it easier for security professionals to understand how malware is identified and how detection decisions are made. The presentation will cover the theoretical foundations\, implementation details\, and experimental results of GAGE\, showcasing its potential to enhance trust and efficacy in automated malware detection systems. \n  \nBrief Bio: Dr. Mohd Saqib is a researcher and scholar specializing in Explainable AI (XAI)\, machine learning\, and cybersecurity. He completed his Ph.D. at McGill University\, where his research focused on developing interpretable models for malware analysis in collaboration with Defence Research and Development Canada (DRDC). Dr. Saqib also holds an M.Tech in Data Analytics from Indian Institute of Technology (ISM) Dhanbad. He has authored several Q1 journal papers\, including a comprehensive analysis of XAI for malware hunting published in ACM Computing Surveys (IF 23.8). Dr. Saqib has filed four U.S. patents in AI-related technologies during his collaborations with BlackBerry and Zayed University. In addition to his research\, Dr. Saqib has been a Teaching Assistant (TA) for cybersecurity\, hacking\, and AI courses at McGill University. He was honored with the Graduate Excellence Award at McGill and received the prestigious FRQNTscholarship. His expertise spans AI model explainability\, malware detection\, and the intersection of AI with critical infrastructure.
URL:https://homecse.iitd.ac.in/event/explainable-ai-for-malware-analysis-by-mohd-saqib/
LOCATION:Bharti 501\, IIT Campus\, Hauz Khas\, New Delhi
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BEGIN:VEVENT
DTSTART;TZID=Asia/Kolkata:20250812T153000
DTEND;TZID=Asia/Kolkata:20250812T163000
DTSTAMP:20261012T022647
CREATED:20250809T092746Z
LAST-MODIFIED:20250809T092746Z
UID:1768-1755012600-1755016200@homecse.iitd.ac.in
SUMMARY:Enabling Energy-efficient AI Computing: Leveraging Application-specific Approximations by Akash Kumar
DESCRIPTION:Speaker: Akash Kumar (Ruhr University Bochum)\nDetails: Tue\, 12 Aug\, 3:30 PM\, SIT 001\n\nAbstract: The widespread adoption of Artificial intelligence and Machine Learning (AI/ML) models across various fields\, such as healthcare\, autonomous vehicles\, smart agriculture\, and industrial automation\, has led to a growing demand for efficient and scalable AI/ML solutions. However\, as AI/ML algorithms grow more complex\, their substantial memory requirements and high energy consumption pose significant challenges for deployment on resource-constrained embedded systems\, such as wearable health monitors and IoT devices. \nIn this talk\, I will first introduce the topic and outline the significance of cross-layer approximation framework\, emphasizing the necessity of a generic and scalable approach to designing approximate arithmetic operators. I will then talk about platform-specific optimizations for designing approximate operators optimized for FPGAs and end with how modern AI/ML-based DSE approaches can be used for approximate computer arithmetic. \nBiography: Akash Kumar received the joint Ph.D. degree in electrical engineering and embedded systems from the Eindhoven University of Technology\, Eindhoven\, The Netherlands\, and the National University of Singapore (NUS)\, Singapore\, in 2009. From 2009 to 2015\, he was with NUS. From October 2015 until March 2024\, he was a Professor with Technische Universität Dresden\, Dresden\, Germany\, where he was directing the Chair for Processor Design. Since April 2024\, he is directing the chair of Embedded Systems at Ruhr University Bochum\, Germany. His research interests include the design and analysis of low-power embedded multiprocessor systems and designing secure systems with emerging nano-technologies.
URL:https://homecse.iitd.ac.in/event/enabling-energy-efficient-ai-computing-leveraging-application-specific-approximations-by-akash-kumar/
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:20250814T120000
DTEND;TZID=Asia/Kolkata:20250814T130000
DTSTAMP:20261012T022647
CREATED:20250812T055310Z
LAST-MODIFIED:20250812T055310Z
UID:1773-1755172800-1755176400@homecse.iitd.ac.in
SUMMARY:A brief survey of quantum numerical algorithms by Pranav Singh
DESCRIPTION:Title: A brief survey of quantum numerical algorithms \nSpeaker: Prof. Pranav Singh \nDetails: August 14 (Thursday) | 12(noon)-1 PM | Bharti 501 \nAbstract:\nQuantum Numerical Algorithms (QNA) encompass a broad class of techniques including quantum numerical linear algebra (QNLA)\, quantum optimization\, quantum variational algorithms (QVA)\, quantum machine learning (QML)\, and Hamiltonian simulation (HS). These areas represent some of the most promising domains for realizing exponential quantum advantage and have seen rapid theoretical and algorithmic advances in recent years. In this talk\, I will provide a concise overview of these developments and highlight key challenges: both in terms of current quantum hardware limitations and the conceptual gap between classical numerical methods and emerging quantum paradigms. The goal is to offer both a technical snapshot of the field and a broader perspective on what makes quantum numerical thinking distinct and potentially transformative.
URL:https://homecse.iitd.ac.in/event/a-brief-survey-of-quantum-numerical-algorithms-by-pranav-singh/
LOCATION:Bharti 501\, IIT Campus\, Hauz Khas\, New Delhi
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BEGIN:VEVENT
DTSTART;TZID=Asia/Kolkata:20250822T140000
DTEND;TZID=Asia/Kolkata:20250822T150000
DTSTAMP:20261012T022647
CREATED:20250827T082004Z
LAST-MODIFIED:20250827T082004Z
UID:1825-1755871200-1755874800@homecse.iitd.ac.in
SUMMARY:Optimal Capacity Modification for Stable Matchings with Ties by Dr. Keshav Ranjan
DESCRIPTION:Abstract: In this talk\, we consider the Hospitals/Residents (HR) problem in the presence of ties in preference lists of hospitals. Among the three notions of stability\, viz. weak\, strong\, and super stability\, we focus on strong stability. Strong stability is appealing both theoretically and practically; however\, its existence is not guaranteed. Our objective is to optimally increase hospitals’ quotas so that the resulting instance admits a strongly stable matching.\nSuch an augmentation is guaranteed to exist when resident preference lists are strict. We explore two natural optimization criteria:\n\n\n\nMINSUM: minimizing the total capacity increase across all hospitals and \nMINMAX: minimizing the maximum capacity increase for any hospital\n\nWe prove that the MINSUM problem admits a polynomial-time algorithm\, whereas the MINMAX problem is NP-hard. We prove an analogue of the Rural Hospitals theorem for the MINSUM problem. When each hospital incurs a cost for a unit increase in its quota\, the MINSUM problem becomes NP-hard\, even for 0/1 costs. In fact\, we show that the problem cannot be approximated to any multiplicative factor. We also present a polynomial-time algorithm for optimal MINSUM augmentation when a specified subset of edges is required to be included in the matching.\n\nThe talk is based on a recent work accepted at IJCAI 2025 and is a joint work with Meghana Nasre (IIT-M) and Prajakta Nimbhorkar (CMI). \nBio: Keshav Ranjan recently (July 2025) completed his Ph.D. from the Department of Computer Science and Engineering\, IIT Madras\, under the supervision of Dr. Meghana Nasre. His Doctoral thesis\, titled “Two-Sided Matchings: Lower Quotas\, Ties\, and Capacity Augmentation”\, focuses on the algorithmic aspects of two-sided matching problems under various constraints. Previously\, he held an M. Tech degree in Mathematics and Computing from the Department of Mathematics\, IIT Patna. His research interests lie in the broad area of Graph Algorithms\, with a particular focus on matching problems with preferences.
URL:https://homecse.iitd.ac.in/event/optimal-capacity-modification-for-stable-matchings-with-ties-by-dr-keshav-ranjan/
LOCATION:Bharti 501\, IIT Campus\, Hauz Khas\, New Delhi
CATEGORIES:Seminars
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