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X-WR-CALDESC:Events for Computer Science and Engineering
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DTSTART:20240101T000000
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BEGIN:VEVENT
DTSTART;TZID=Asia/Kolkata:20250103T160000
DTEND;TZID=Asia/Kolkata:20250103T170000
DTSTAMP:20260921T212500
CREATED:20241203T110026Z
LAST-MODIFIED:20241212T122254Z
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SUMMARY:Leveraging LLMs for Networking & Security in Cloud Environments
DESCRIPTION:Deepak Bansal\, Microsoft \nAbstract: Customer networks have grown\, mostly organically\, large and complex in cloud environments like Azure. Customers are often afraid to make changes and find it hard to diagnose when things go wrong. In this talk\, I am going to share how Microsoft is using LLMs to simplify network operations at scale in Azure and how it is enabling the same for its customers through Azure Copilot. On the security side\, I will share how LLMs are being used to enable security monitoring and threat hunting. \nBio: Deepak graduated from IIT D in CS in 1999 and did a Masters in CS at MIT. He is currently a Corp Vice President and Technical Fellow at Microsoft in Redmond\, WA USA and is driving cloud (Azure infrastructure) and security (Microsoft’s Secure Future Initiative).
URL:https://homecse.iitd.ac.in/event/leveraging-llms-for-networking-security-in-cloud-environments/
LOCATION:Bharti 501\, IIT Campus\, Hauz Khas\, New Delhi
CATEGORIES:Seminars
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DTSTART;TZID=Asia/Kolkata:20241219T120000
DTEND;TZID=Asia/Kolkata:20241219T130000
DTSTAMP:20260921T212500
CREATED:20241126T043850Z
LAST-MODIFIED:20241212T122814Z
UID:212-1734609600-1734613200@homecse.iitd.ac.in
SUMMARY:Global Search and Discovery with Differential Policy Optimization
DESCRIPTION:Chandrajit Bajaj\, UT Austin \nReinforcement learning (RL) with continuous state and action spaces is arguably one the most challenging problems within the field of machine learning.  Most current learning methods focus on integral identities such as value (Q) functions to derive an optimal strategy for the learning agent. In this talk we present the dual form of the original RL formulation to propose the first differential RL framework that can handle settings with limited training samples and short-length episodes. Our approach introduces Differential Policy Optimization (DPO)\, a pointwise and stage-wise iteration method that optimizes policies encoded by local-movement operators. We prove a pointwise convergence estimate for DPO and provide a regret bound comparable with the best current theoretical derivation. Such pointwise estimate ensures that the learned policy matches the optimal path uniformly across different steps. We then apply DPO to a class of practical RL problems with continuous state and action spaces\,  e.g. shape and material optimization and discovery of new molecules with targeted dynamics. \nThis is joint work with Garvit Bansal\, Minh Nguyen.
URL:https://homecse.iitd.ac.in/event/prospecting-for-global-optimizers-with-physics-agents/
LOCATION:Bharti 501\, IIT Campus\, Hauz Khas\, New Delhi
CATEGORIES:Seminars
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BEGIN:VEVENT
DTSTART;TZID=Asia/Kolkata:20241209T173000
DTEND;TZID=Asia/Kolkata:20241209T183000
DTSTAMP:20260921T212500
CREATED:20241125T234344Z
LAST-MODIFIED:20241126T044936Z
UID:178-1733765400-1733769000@homecse.iitd.ac.in
SUMMARY:New Algorithmic Challenges for Ethical Decision-Making
DESCRIPTION:Swati Gupta\, MIT. \nWhen someone is denied a job\, offered a different price for the same goods or services\, or declined a loan\, intent to discriminate is often not the case. The decision system applies the same data and rules to all and yet has a disproportionate effect on various groups. The causes of such disparate impact in machine learning and optimization are many\, and these create an opportunity for us to develop new algorithms. I will present three such opportunities. The first is motivated by challenges due to bias and errors in evaluation data. I will present new optimization problems using ordinal data\, which can create a pathway to solving discrimination in hiring (Management Science\, 2023 with Salem\, and UC Davis Law Review\, 2023 with Salem and Desai). Next\, I will discuss the challenge of selecting the “right” notion of fairness. I will present the concept of “portfolios”\, that ask to find a small set of approximate solutions that summarize the set (potentially infinite) set of fairness objectives. I will showcase combinatorial techniques to tackle this challenge\, and connections to polyhedral structure (EC 2023\, SODA 2025\, with Singh and Moondra). Finally\, motivated by the recent lawsuits on price fluctuations\, I will discuss challenges in trajectory-constrained stochastic optimization\, which for example\, can provide algorithms that monotonically change prices in demand learning (WINE 2022\, with Kamble and Salem). This talk is based on joint work with Jad Salem\, Deven Desai\, Mohit Singh\, Jai Moondra\, and Vijay Kamble. \n  \nBio: Dr. Swati Gupta is an Associate Professor at the MIT Sloan School of Management in the Operations Research and Statistics Group\, and holds the Class of 1947 Career Development Professorship. She received a Ph.D. in Operations Research from MIT\, and a dual Bachelors + Masters in Computer Science and Engineering from IIT Delhi. Her research interests include optimization and machine learning\, with a focus on algorithmic fairness. Her work is cross-disciplinary and spans various domains such as hiring\, admissions\, e-commerce\, healthcare\, districting\, power systems\, and quantum optimization. She served as the lead of Ethical AI for the NSF AI Institute on Advances in Optimization\, from 2021-2023. She has received the NSF CAREER Award in 2023\, the JP Morgan Early Career Faculty Recognition in 2021\, the NSF CISE Research Initiation Initiative Award in 2019\, Simons-Berkeley Research Fellowship in 2017-2018\, and the Google Women in Engineering Award (India) in 2011. Dr. Gupta’s research is partially funded by the National Science Foundation (NSF) and Defense Advanced Research Projects Agency (DARPA)\, as well as Social and Ethical Responsibilities in Computing (SERC) at MIT.
URL:https://homecse.iitd.ac.in/event/new-algorithmic-challenges-for-ethical-decision-making/
LOCATION:Bharti 501\, IIT Campus\, Hauz Khas\, New Delhi
CATEGORIES:Seminars
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BEGIN:VEVENT
DTSTART;TZID=Asia/Kolkata:20241129T173000
DTEND;TZID=Asia/Kolkata:20241129T183000
DTSTAMP:20260921T212500
CREATED:20241126T044001Z
LAST-MODIFIED:20241126T044001Z
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SUMMARY:How Do We Involve People in AI Decision-Making? Towards Effective Participatory AI Designs
DESCRIPTION:Vijay Keswani\, Duke University. \n  \nThe expanding capabilities of AI come with a surge in the reports of societal and personal harms related to its use. Examples range from systemic biases in AI decision-aid tools in healthcare and policing to stereotype propagation in AI-based search and translation tools. Technical research on mitigating such harms forward certain solutions to ensure that AI behavior is aligned with ethical norms and values. Yet\, this research leaves unanswered the question of “whose norms are followed” and can fail to counter AI harms when there is a disparity between the assumed ethical norms and the values of the people impacted by AI. But what if there was a way for the stakeholders (e.g.\, AI users or domain experts) to tell us how an AI tool should ideally operate? \nIn this talk\, I will argue for democratizing how we build AI tools and undertaking a participatory approach to AI assessment and development. By eliciting feedback from relevant stakeholders on the harms they observe and the outcomes they expect\, AI models can be aligned with the expressed stakeholder values. We will see concrete illustrations of such participatory mechanisms for image search audits\, multi-winner elections\, and medical decision-making. Across these applications\, certain features of participation in AI will become clear: (a) participatory designs are domain-specific\, (b) their efficacy relies heavily on the effectiveness of mechanisms used for eliciting stakeholder preferences\, and (c) (when done right) they enhance user agency and trust in AI tools. \nVijay Keswani is a Postdoctoral Associate at Duke University. His research interests center around community-focused AI development and the ethics of data and technology. His work leverages tools from various disciplines to build robust AI models\, combining computational and statistical learning mechanisms with methods from law\, philosophy\, psychology\, and economics. He received his PhD from Yale University in 2023. While at Yale\, he was a Resident Fellow at the Information Society Project during 2022-2023 and a 2022 Policy Fellow at the Yale Institute for Social and Policy Studies.
URL:https://homecse.iitd.ac.in/event/how-do-we-involve-people-in-ai-decision-making-towards-effective-participatory-ai-designs/
LOCATION:Bharti 501\, IIT Campus\, Hauz Khas\, New Delhi
CATEGORIES:Seminars
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