BEGIN:VCALENDAR
VERSION:2.0
PRODID:-//Computer Science and Engineering - ECPv6.13.0//NONSGML v1.0//EN
CALSCALE:GREGORIAN
METHOD:PUBLISH
X-WR-CALNAME:Computer Science and Engineering
X-ORIGINAL-URL:https://homecse.iitd.ac.in
X-WR-CALDESC:Events for Computer Science and Engineering
REFRESH-INTERVAL;VALUE=DURATION:PT1H
X-Robots-Tag:noindex
X-PUBLISHED-TTL:PT1H
BEGIN:VTIMEZONE
TZID:Asia/Kolkata
BEGIN:STANDARD
TZOFFSETFROM:+0530
TZOFFSETTO:+0530
TZNAME:IST
DTSTART:20240101T000000
END:STANDARD
END:VTIMEZONE
BEGIN:VEVENT
DTSTART;TZID=Asia/Kolkata:20241209T173000
DTEND;TZID=Asia/Kolkata:20241209T183000
DTSTAMP:20261010T182433
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
END:VEVENT
BEGIN:VEVENT
DTSTART;VALUE=DATE:20241203
DTEND;VALUE=DATE:20241208
DTSTAMP:20261010T182433
CREATED:20241126T052658Z
LAST-MODIFIED:20241126T052705Z
UID:224-1733203800-1733549399@homecse.iitd.ac.in
SUMMARY:Winter School on Formal Verification and Program Synthesis
DESCRIPTION:We are excited to invite you to the Winter School on Formal Verification and Program Synthesis\, hosted at CSE IIT Delhi from December 3rd to 6th\, 2024. This workshop is designed to provide participants with a comprehensive overview of recent advancements in constraint solvers and automated reasoning\, along with their cutting-edge applications in diverse fields such as the verification of deep neural networks\, security\, blockchains and synthesis. The four-day workshop will feature a series of lectures and hands-on sessions led by Subodh Sharma\, Kumar Madhukar\, and Priyanka Golia .\n\nList of Topics\n\nSubodh Sharma: Introduction to Symbolic Execution and Bounded Model Checking; Applications to Security and Blockchains. Tools: CProver\nKumar Madhukar: Verification of Deep Neural Networks\, Abstraction-Refinement\, Invariants and Connection to Program Verification Hands-on session with a DNN verification tool (NeuralSAT/Marabou).\nPriyanka Golia: Introduction to propositional logic modeling\, constraint encoding\, and the basics of SAT (Satisfiability) and SMT (Satisfiability Modulo Theories) solvers (basic introduction to DPLL and CDCL). Applications of these solvers in synthesis! Tools: Manthan\, MiniSAT\, Z3.\n\n\n\nRegistration\nThe typical profile of a participant will be a final-year undergraduate student or a post-graduate (Masters/PhD) student or an industry professional interested in getting introduced to advanced topics in formal verification and program synthesis.\nHow to apply? Please register before 11:59pm on 15th November 24th November\, 2024 to apply to attend the Winter school. The registration requires you to read any one of the following papers\, and write what you understood from it\, please budget at least two days for this. \n\nSoftware Model Checking for People Who Love Automata\nFormal Verification of Piece-Wise Linear Feed-Forward Neural Networks\n\nRegister Now \n\n\nLogistics\n\nDates: 3-6 December 2024\nVenue: CSE department\, IIT Delhi.\nTravel Support and Hostel Accommodation: We will provide travel support of 3000 INR for outstation candidates and 1000 INR for students from Delhi-NCR\, along with hostel accommodation for the selected participants during December 2-7\, 2024. Participants are required to check out before lunch on December 7\, 2024.\nPlease note that the availability of hostel accommodation depends on several external factors beyond our control. While we anticipate that all participants will be able to secure accommodation\, please do not assume this until you receive confirmation from us.\nAfter winter school: Interested students will be encouraged to apply for internships at IIT Delhi during eight weeks of summer 2025. Stipend will be offered to cover living and stay expenses in the IIT hostel during the internship.\n\nPlease note that we will NOT give any certificate of completion and will NOT provide recommendation letters to graduate schools or otherwise for participating in the school. \n\n\nProgramme\nThe detailed programme will be announced soon. Please check back later for updates. \n\n  \n 
URL:https://homecse.iitd.ac.in/event/winter-school-on-formal-verification-and-program-synthesis/
LOCATION:Bharti 501\, IIT Campus\, Hauz Khas\, New Delhi
CATEGORIES:Schools/Workshops
ORGANIZER;CN="Subodh Sharma":MAILTO:svs@cse.iitd.ac.in
END:VEVENT
BEGIN:VEVENT
DTSTART;VALUE=DATE:20241202
DTEND;VALUE=DATE:20241208
DTSTAMP:20261010T182433
CREATED:20241126T001433Z
LAST-MODIFIED:20241126T001433Z
UID:182-1733117400-1733549399@homecse.iitd.ac.in
SUMMARY:Winter School 2024 on Data Systems
DESCRIPTION:The Winter School 2024 on Data Systems\, organized by the Data Systems Group of the Department of Computer Science and Engineering at IIT Delhi\, will be held from December 2nd to December 6th\, 2024. Supported by the Mohit Aron Endowment\, this winter school provides an exceptional opportunity for final-year undergraduate students\, master’s and PhD students\, and industry professionals to deepen their knowledge in scalable systems for Big Data\, Data Science and AI. \nParticipants will engage in lectures and hands-on lab sessions on a range of data systems topics\, led by Prof. Kaustubh Beedkar and Prof. Abhilash Jindal. \nRegistration\nApplications are open to final-year undergraduate students\, master’s and PhD students\, and industry professionals with an interest in scalable systems for Big Data\, Data Science\, and AI. The application process for Winter School 2024 consists of two rounds: \nFirst Round – Application Submission\nInterested candidates must submit their applications via the provided online form by 5:00 pm on 14th November 2024. Only candidates who complete this submission will be considered for the next round. \nSecond Round – Online Test\nCandidates who have successfully applied in the first round will be invited to participate in an online test scheduled for 16th November 2024 17th November 2024 at 10:00 AM. \nSelected candidates will receive notification of their acceptance into the Winter School by 17th November 2024 21st November 2024. \nLogistics\nVenue: Department of Computer Science and Engineering\, IIT Delhi\nDates: December 2nd – December 6th\, 2024\nAccommodation: Hostel accommodation including breakfast\, lunch\, dinner\, and high tea will be provided from Dec 1st – Dec 7th\, 2024. In addition\, a stipend of 2000 INR will be provided to selected students. Participants will be required to check in on December 1st\, 2024\, and check out before noon on December 7th\, 2024.\nAfter winter school: Participants who successfully complete the Winter School will receive a Certificate of Participation. Please note that we will not provide recommendation letters for graduate school applications. Students will be encouraged to apply for paid internships at IIT Delhi during the 8 weeks of summer 2025.\nDaily Schedule\n09:00 am – 10:15 am: Lecture\n10:15 am – 10:45 am: High tea + offline discussions\n10:45 am – 01:00 pm: Lectures with breaks\n01:00 pm – 02:00 pm: Lunch Break\n02:00 pm – 05:00 pm: Lab\n03:30 pm – 04:00 pm: High tea \nProgram\nThe Winter School will cover the following topics: \nDay 1: \n\nDistributed data processing\nHoly grail: transparently scale\, and tolerate faults/stragglers;\nDifficulties in transparent locality\, scalability\, fault tolerance\, and straggler mitigation;\nIntroduction to checkpointing and replication;\nIntroduction to distributed data processing with MapReduce;\nMain ideas: functional programming model; separate control and data plane; locality optimizations\, re-execute lost/backup tasks\, deterministic/idempotent tasks.\nLab: Write a fault tolerant computation from scratch\n\nDay 2: \n\nDataflow systems for batch processing\nKeynote address: TBA\nSpark’s Resilient distributed dataset (RDD) abstraction: write-once for consistent replication\, coarse-grained transformations to reduce lineage\, lineage-based re-execution of lost/backup tasks;\nAbstractions beyond MapReduce\, lazy execution\, stage planning\, narrow and wide-dependencies\, query optimizations.\nLab: Hands-on exercises with Spark.\n\nDay 3: \n\nDataflow systems for stream processing\nSemantics of stream processing: unbounded streams\, event time vs processing time\, hopping/sliding/session windows\, watermarks;\nStreaming query optimizations and continuous operator model of Flink for low-latency;\nFault tolerance: problems due to state in continuous operators\, pull state out of operators by discretizing streams\, consistent checkpoints in Flink using Chandy-Lamport algorithm.\nLab: Hands-on exercises with Flink.\n\nDay 4: \n\nCyclic dataflow systems\nIterative data processing: solving graph problems with iterative data processing\, driver-based iterations vs native iterations with cyclic dataflows\, changes for creating consistent checkpoints.\nIntroduction to ML workflows: error handling and correction as a first class citizen\, Reactive dataflow with deletes\, edits\, and appends.\nBackward and forward lineage for backward tracing and delete propagation\, stage planning with incremental/non-incremental\, monotonic/non-monotonic operators\nLab: Hands-on exercises with Popper.\n\nDay 5: \n\nCross-platform data processing\nIntroduction to Wayang: a platform for cross-framework data processing\, allowing optimizations across Spark\, Flink\, and Relational Databases.\nSessions about data systems research at IITD\, guiding participants interested in pursuing higher studies\, and research success stories.\nLab: Hands-on exercises with Wayang.\n\nDisclaimer: The schedule and topics are only tentative. They are subject to change depending upon attendee interests.
URL:https://homecse.iitd.ac.in/event/winter-school-2024-on-data-systems/
LOCATION:Bharti 501\, IIT Campus\, Hauz Khas\, New Delhi
CATEGORIES:Schools/Workshops
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=Asia/Kolkata:20241129T173000
DTEND;TZID=Asia/Kolkata:20241129T183000
DTSTAMP:20261010T182433
CREATED:20241126T044001Z
LAST-MODIFIED:20241126T044001Z
UID:214-1732901400-1732905000@homecse.iitd.ac.in
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
END:VEVENT
END:VCALENDAR