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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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BEGIN:VEVENT
DTSTART;TZID=Asia/Kolkata:20260202T120000
DTEND;TZID=Asia/Kolkata:20260202T130000
DTSTAMP:20261010T211112
CREATED:20260115T072749Z
LAST-MODIFIED:20260115T072749Z
UID:2365-1770033600-1770037200@homecse.iitd.ac.in
SUMMARY:Deep generative models for single-cell and spatial genomics by Ajita Shree
DESCRIPTION:Speaker: Ms. Ajita Shree is a PhD student in the Department of Computer Science and Engineering at IIT Kanpur and will be joining EMBL-EBI\, UK\, as a postdoctoral researcher. \nAbstract: Recent advances in large-scale genomic assays\, including single-cell and spatial transcriptomics (ST)\, have provided unprecedented insights into the biological mechanisms underlying development\, disease\, and therapeutic response. However\, these datasets pose significant computational challenges. \nOne of the major challenges is the integration of heterogeneous single-cell datasets across donors\, time points\, and experimental conditions to create a unified resource for downstream analysis. In this talk\, I will present scDREAMER\, a novel deep generative model that can perform integration of multi-batch single-cell datasets in unsupervised\, semi-supervised and supervised settings. Using real benchmarking datasets\, we demonstrate that scDREAMER can overcome critical challenges including skewed cell type distribution among batches\, nested batch-effects\, large number of batches and enables integration of millions of cells across species. \nFurther\, I will discuss the emerging field of spatial genomics\, which enables the study of gene expression at a spatial level\, but its spot-level resolution poses challenges in resolving cell-type contributions in situ. This has spurred extensive development of deconvolution methods\, yet evaluation remains lacking. In this work\, we present a novel graph attention auto-encoder for simulating spatial cell type distributions from three major tissue types including brain\, cancer and organs\, and performed a comprehensive benchmarking. \nBio: Ajita Shree is a PhD student in the Department of Computer Science and Engineering at IIT Kanpur and will be joining EMBL-EBI\, UK\, as a postdoctoral researcher. Previously\, she worked for three years as a Data Scientist at GE Aerospace and GE Global Research\, and was a graduate of GE’s Global Leadership Program. She earned her M.Tech. in Computer Science and Engineering from IIT Delhi in 2017 and holds a B.Tech. in Computer Science and Engineering.
URL:https://homecse.iitd.ac.in/event/deep-generative-models-for-single-cell-and-spatial-genomics-by-ajita-shree/
LOCATION:Bharti 501\, IIT Campus\, Hauz Khas\, New Delhi
CATEGORIES:Seminars
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=Asia/Kolkata:20260130T120000
DTEND;TZID=Asia/Kolkata:20260130T130000
DTSTAMP:20261010T211112
CREATED:20251227T051556Z
LAST-MODIFIED:20251227T051556Z
UID:2253-1769774400-1769778000@homecse.iitd.ac.in
SUMMARY:Image decomposition with Fluorescence Microscopy Data by Ashesh
DESCRIPTION:Venue: Bharti501 \nAbstract: Fluorescence microscopy is limited by optics\, fuorophore chemistry\, and photon exposure\, forcing trade-ofs in speed\, resolution\, and depth. In this talk\, I will discuss my PhD research that addresses these challenges. Specifcally\, my PhD research enables imaging of multiple cellular structures within a single fuorescent channel\, allowing faster imaging with less photon exposure. Technically speaking\, given a superimposed image (e.g.\, containing nucleus and tubulin)\, the objective is to predict the constituent images separately. \nThis talk focuses on my frst work\, µSplit. Early in my PhD\, we found that regular deep architectures performed best with large image patches\, but GPU memory limits hindered scalability. We thus developed µSplit\, a novel meta-architecture enabling memory-efcient use of large image context. Built on Hierarchical-VAE (HVAE) and U-Net variants\, it modifes HVAE’s ELBO loss for non-autoencoding tasks\, modifes KL loss for high-frequency details extraction\, and reformulates the encoder output for stable training. We also created a synthetic dataset to evaluate our network’s capability to extract large image context. Lastly\, we explored tiling artifacts\, analyzed two mitigation strategies\, and demonstrated the superiority of one\, both empirically and via out-of-distribution arguments. \n\nBio: Ashesh is a postdoctoral fellow at Human Technopole\, Milan\, Italy. He recently completed his PhD in Computer Science at TU Dresden\, Germany\, conducted in Florian Jug’s lab at Human Technopole’s Computational Biology Center. His doctoral research focused on image decomposition\, specifcally unmixing superimposed fluorescence microscopy images into constituent channels. With frst-author publications in top CV/ML venues such as ECCV\, ICCV\, and NeurIPS\, and a recent one accepted to Nature Methods\, his work ofers a robust solution to this challenge. His thesis earned a nomination for TU Dresden’s PhD prize nominations (pending decision)\, an €8\,700 EMBO grant for a research visit to ENS de Lyon on self-supervised fnetuning and uncertainty quantifcation\, and the Best Oral Presentation Award at the 2024 HT PhD & Postdoc Symposium. Previously\, Ashesh earned a dual B.Tech+M.Tech in Computer Science (2015) from IIT Delhi\, India. He brings over three years of industry experience as a Data Scientist and served as Research Assistant at National Taiwan University under Prof. Hsuan-Tien Lin\, initiating multiple computer vision projects\, culminating in publications.
URL:https://homecse.iitd.ac.in/event/image-decomposition-with-fluorescence-microscopy-data-by-ashesh/
LOCATION:Bharti 501\, IIT Campus\, Hauz Khas\, New Delhi
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=Asia/Kolkata:20260129T120000
DTEND;TZID=Asia/Kolkata:20260129T130000
DTSTAMP:20261010T211112
CREATED:20260124T095221Z
LAST-MODIFIED:20260124T123516Z
UID:2392-1769688000-1769691600@homecse.iitd.ac.in
SUMMARY:Lumos: A DSL for Language Model System Certification by Isha Chaudhary
DESCRIPTION:Venue: Bharti 501\nAbstract: As Language Model Systems (LMS) are deployed across an expanding range of applications\, aligning them with human ethics has become crucial. Although recent works propose methods to formally certify LMS properties such as fairness\, correct question answering\, and safety\, these approaches are generally ad hoc and hard to generalize. We introduce a principled alternative: a domain-specific language\, Lumos\, for specifying and formally certifying LMS behaviors. Lumos is the first imperative probabilistic programming language over graphs\, with constructs to generate independent and identically distributed prompts for LMS. It offers a structured view of prompt distributions via graphs\, forming random prompts from sampled subgraphs. Lumos supports certifying LMS for arbitrary prompt distributions via integration with statistical certifiers. Lumos can encode existing LMS specifications\, including complex relational and temporal specifications. It also facilitates specifying new properties – we present the first safety specifications for vision-language models (VLMs) in autonomous driving scenarios developed with Lumos. Using these\, we show that the state-of-the-art VLM Qwen-VL exhibits critical safety failures\, producing incorrect and unsafe responses with at least 90% probability in right-turn scenarios under rainy driving conditions\, revealing substantial safety risks. We further demonstrate that specification programs written in Lumos enable finding specific failure cases exhibited by state-of-the-art LMS. Lumos is the first systematic and extensible language-based framework for specifying and certifying LMS behaviors\, paving the way for a wider adoption of LMS certification.\n  \nBio: Isha Chaudhary is a 4th year Computer Science Ph.D. student at the University of Illinois Urbana-Champaign\, advised by Prof. Gagandeep Singh. Her research focuses on formal methods to make frontier models trustworthy. Her work has been recognized at top-tier conferences including ICLR 2025\, AISTATS 2026 (spotlight)\, and MLSys 2024. She earned a B.Tech. in Electrical Engineering from IIT Delhi\, where she was awarded the Institute Silver Medal and Prof. C.S. Jha Memorial Award. She earned an MS in CS from UIUC.
URL:https://homecse.iitd.ac.in/event/lumos-a-dsl-for-language-model-system-certification-by-isha-chaudhary/
LOCATION:Bharti 501\, IIT Campus\, Hauz Khas\, New Delhi
ORGANIZER;CN="Subodh Sharma":MAILTO:svs@cse.iitd.ac.in
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=Asia/Kolkata:20260122T120000
DTEND;TZID=Asia/Kolkata:20260122T130000
DTSTAMP:20261010T211112
CREATED:20260116T114035Z
LAST-MODIFIED:20260116T144924Z
UID:2385-1769083200-1769086800@homecse.iitd.ac.in
SUMMARY:Logical Relations for Formally Verified Authenticated Data Structures by Chaitanya Agarwal
DESCRIPTION:Venue: Bharti501 \nAbstract: Authenticated data structures (ADSs) allow untrusted third parties to carry out operations which produce proofs that can be used to verify an operation’s output. Such data structures are challenging to develop and implement correctly. In this talk\, I will talk about a library\, Authentikit\, that is implemented in OCaml\, that generates authenticated versions of data structures automatically. I will also talk about recent work by us (https://dl.acm.org/doi/abs/10.1145/3719027.3744801) that gives a formal proof of security and correctness of Authentikit. The proof is based on a new relational separation logic for reasoning about programs that use collision-resistant cryptographic hash functions. This logic provides a basis for constructing two semantic models of a type system\, which are used to justify how Authentikit makes use of type abstraction to enforce security and correctness. Using these models we also prove the correctness of several optimizations to Authentikit and then show how optimized\, hand-written implementations of authenticated data structures can be soundly linked with automatically generated code. All of the results have been mechanized in the Rocq prover using the Iris framework. \nSpeaker Bio: Chaitanya Agarwal (https://culechetoo.github.io <https://culechetoo.github.io/>) is a 3rd year computer science PhD student at the New York University\, advised by Joseph Tassarotti. He is broadly interested in programming languages and formal verification with a particular focus on verification of security applications. In the past\, he has also worked with Thomas Wies on abstract-interpretation analysis for recursive\, higher-order programs\, and with Shibashis Guha\, on developing statistical-model-checking techniques for Markov Decision Processes (MDPs). Chaitanya obtained his B.Tech. from IIIT Delhi.
URL:https://homecse.iitd.ac.in/event/logical-relations-for-formally-verified-authenticated-data-structures-by-chaitanya-agarwal/
LOCATION:Bharti 501\, IIT Campus\, Hauz Khas\, New Delhi
CATEGORIES:Seminars
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=Asia/Kolkata:20260116T120000
DTEND;TZID=Asia/Kolkata:20260116T130000
DTSTAMP:20261010T211112
CREATED:20260113T105922Z
LAST-MODIFIED:20260113T105922Z
UID:2354-1768564800-1768568400@homecse.iitd.ac.in
SUMMARY:WhiteLie: A Robust System for Spoofing User Data in Android Platforms by Harish Yadav
DESCRIPTION:Venue: SIT113 \nAbstract: The Android operating system uses a permission framework that allows users to control access to their private data\, such as location and contacts\, when using apps. However\, many apps become non-functional or crash if denied these permissions\, effectively pressuring users to grant access and compromising their privacy. In this paper\, we introduce WhiteLie\, a robust user data spoofing system designed to protect user privacy by feeding spoofed data to apps without requiring device rooting or binary modification. Through experiments on 70 pre-installed and user-installed Android apps\, we demonstrate that WhiteLie successfully spoofs 78.32% of the requested permissions without detection or crashes. Unlike previous methods that involve modifying the Android OS or rebuilding app binaries\, WhiteLie operates on non-rooted devices\, maintaining full app functionality. Furthermore\, WhiteLie is able to bypass continuous authentication mechanisms\, which rely on sensor data for ongoing user validation\, highlighting critical weaknesses in such security frameworks. Our findings show that WhiteLie effectively mitigates privacy risks from malicious apps\, as demonstrated in case studies where it prevented unauthorized data uploads and reduced the success rate of side-channel attacks. Additionally\, WhiteLie enhances user control over data privacy in everyday apps like Facebook\, where it was used to detect and block unauthorized audio recordings. Despite its powerful capabilities\, WhiteLie introduces minimal performance overhead\, with only a 2.52% increase in battery consumption and negligible impact on app execution time. WhiteLie proves to be a practical and efficient solution for enhancing user privacy in the Android ecosystem\, offering users greater control over their data while ensuring seamless app functionality. \nBio: Harish Yadav(2021CSY7544) is a MSR Student in the Department Of Computer Science & Engineering\, IIT Delhi. \nHe will be defending his thesis before a panel of Professors.
URL:https://homecse.iitd.ac.in/event/whitelie-a-robust-system-for-spoofing-user-data-in-android-platforms-by-harish-yadav/
LOCATION:SIT 113\, Amar Nath and Shashi Khosla School of Information Technology\, Indian Institute of Technology\, Delhi\, Hauz Khas\, New Delhi\, Delhi\, 110016\, India
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=Asia/Kolkata:20260115T110000
DTEND;TZID=Asia/Kolkata:20260115T120000
DTSTAMP:20261010T211112
CREATED:20260114T064025Z
LAST-MODIFIED:20260114T064025Z
UID:2361-1768474800-1768478400@homecse.iitd.ac.in
SUMMARY:Agentic Information Seeking for Knowledge Acquisition by Revanth Reddy
DESCRIPTION:Venue: SIT001 \nAbstract:  The vast expansion of online information has shifted the challenge from simply locating data to efficiently filtering and aggregating relevant content from diverse sources. This talk introduces innovative methodologies aimed at enhancing automated information seeking capabilities within intelligent systems. I will present a modular\, agent-based framework that decomposes the information-seeking process into navigation\, extraction\, and aggregation components. This approach enables exploratory behaviors that significantly outperform current web agents. Next\, I will discuss the application of these techniques to tackle novel challenges in knowledge acquisition across various domains: 1) Automated Wikipedia Updates: An approach to automatically update and maintain Wikipedia articles using real-time online sources\, and 2) Software Issue Localization: How enabling information-seeking over code repositories helps identify and address localization issues for software problems. \nBio: Revanth Gangi Reddy is a research scientist at Google DeepMind\, working on Gemini Post-Training. He finished his Ph.D. in Computer Science at the University of Illinois Urbana-Champaign\, advised by Prof. Heng Ji. His research interests lie in knowledge-driven natural language processing\, focusing on agentic search\, ranking models\, and retrieval-augmented generation. Revanth’s work has been published in leading conferences such as ICLR\, ACL\, AAAI\, EMNLP\, NAACL\, and SIGIR\, and he also presented a tutorial on Open-Retrieval Question Answering at IJCAI 2023. Revanth has previously done research internships at Salesforce Research\, Apple\, AI2\, IBM Research\, and Amazon Science\, and was the team lead for UIUC at the Alexa SocialBot Grand Challenge 5. Revanth holds a Bachelor’s degree in Computer Science from the Indian Institute of Technology Madras and is a Siebel Scholar (class of 2022).
URL:https://homecse.iitd.ac.in/event/agentic-information-seeking-for-knowledge-acquisition-by-revanth-reddy/
LOCATION:SIT 001\, Amar Nath and Shashi Khosla School of Information Technology\, IIT Delhi\, Hauz Khas\, New Delhi 110016\, India\, Delhi\, Delhi\, 110016\, India
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=Asia/Kolkata:20260109T160000
DTEND;TZID=Asia/Kolkata:20260109T170000
DTSTAMP:20261010T211112
CREATED:20260106T111834Z
LAST-MODIFIED:20260108T175420Z
UID:2263-1767974400-1767978000@homecse.iitd.ac.in
SUMMARY:Coping with choices - List Decoding in Coding Theory by Dr. Shashank Srivastava
DESCRIPTION:Venue: Bharti-501/MS Teams \nAbstract: The goal of error correcting codes is to encode data in a way that allows for this data to be recovered even if the encoded copy is corrupted by an adversary. \nThe usual algorithmic challenge associated with codes\, called decoding\, is to output the uncorrupted copy of data by looking only at the corrupted copy. However\, when noise levels are high\, the same corrupted copy could correspond to multiple uncorrupted copies. The task of list decoding is to output all such candidates. \nIn this talk\, we will talk about what makes list decoding interesting and challenging\, and its somewhat surprising connections to other areas in CS. We will then survey recent progress in list decoding for codes based on algebra and on expander graphs. \n  \nBio: Shashank Srivastava is a joint postdoc between Institute for Advanced Study (IAS)\, Princeton and DIMACS\, Rutgers University. Before this\, he obtained a PhD in 2024 from TTI Chicago and a BTech in 2018 from IIT Kharagpur. Shashank’s research focuses on coding theory and spectral algorithms\, and his work has won Best Paper and Best Student Paper awards at SODA
URL:https://homecse.iitd.ac.in/event/coping-with-choices-list-decoding-in-coding-theory-by-dr-shashank-srivastava/
LOCATION:Bharti 501\, IIT Campus\, Hauz Khas\, New Delhi
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=Asia/Kolkata:20260108T120000
DTEND;TZID=Asia/Kolkata:20260108T130000
DTSTAMP:20261010T211112
CREATED:20251225T164159Z
LAST-MODIFIED:20251225T164159Z
UID:2251-1767873600-1767877200@homecse.iitd.ac.in
SUMMARY:Online Flexible Busy Time Scheduling on Heterogeneous Machines by Gruia Calinescu
DESCRIPTION:Venue: Bharti501 \nAbstract: We study the online busy time scheduling model on heterogeneous machines. In our setting\, jobs with uniform length arrive online with a deadline that becomes known to the algorithm at the job’s arrival time. An algorithm has access to machines\, each with different associated capacities and costs. The goal is to schedule jobs on machines by their deadline\, so that the total cost incurred by the scheduling algorithm is minimized. While busy time scheduling has been well-studied\, relatively little is known when machines are heterogeneous (i.e.\, have different costs and capacities)\, despite this natural theoretical generalization being the most practical model for clients using cloud computing services. We make significant progress in understanding this model by designing an 8-competitive algorithm for the problem on unit-length jobs and provide a lower bound of 2 on the competitive ratio. The lower bound is tight in the setting when jobs form non-nested intervals. Our 8-competitive algorithm generalizes to one with competitive ratio 8(2p-1)/p < 16 when all jobs have uniform length p. \nJoint work with Sami Davies\, Samir Khuller\, and Shirley Zhang \n  \nBio: Gruia Calinescu has studied at University of Bucharest\, received his PhD in 1998 from Georgia Institute of Technology and has worked since 2000 at Illinois Tech. He has held short term positions at DIMACS\, U. Waterloo\, U. Wisconsin Milwaukee\, and Northwestern University\, and also visited the Max Plank Institute for Informatics and the Hausdorff Research Institute for Mathematics. \nHis best works (all of them improved or generalized by now) are on Multiway Cut\, Zero Extension\, and Maximizing a Monotone Submodular Function Subject to a Matroid Constraint.
URL:https://homecse.iitd.ac.in/event/online-flexible-busy-time-scheduling-on-heterogeneous-machines-by-gruia-calinescu/
LOCATION:Bharti 501\, IIT Campus\, Hauz Khas\, New Delhi
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=Asia/Kolkata:20260105T120000
DTEND;TZID=Asia/Kolkata:20260105T130000
DTSTAMP:20261010T211112
CREATED:20260103T092244Z
LAST-MODIFIED:20260103T170405Z
UID:2260-1767614400-1767618000@homecse.iitd.ac.in
SUMMARY:Traceable Secret Sharing: Strong Security and Efficient Constructions by Aditi Partap
DESCRIPTION:Venue: Bharti 501 \nAbstract: Suppose Alice uses a t-out-of-n secret sharing to store her secret key on n servers. Her secret key is protected as long as t of them do not collude. However\, what if a less-than-t subset of the servers decides to offer the shares they have for sale? In this case\, Alice should be able to hold them accountable\, or else nothing prevents them from selling her shares. With this motivation in mind\, Goyal\, Song\, and Srinivasan (CRYPTO 21) introduced the concept of {\em traceable secret sharing}. In such schemes\, it is possible to provably trace the leaked secret shares back to the servers who leaked them. Goyal et al. presented the first construction of a traceable secret sharing scheme. However\, secret shares in their construction are quadratic in the secret size\, and their tracing algorithm is quite involved as it relies on Goldreich-Levin decoding. \nIn this work\, we put forth new definitions and practical constructions for traceable secret sharing. In our model\, some f<t servers output a reconstruction box R that may arbitrarily depend on their shares. Given t-f additional shares\, R reconstructs and outputs the secret. The task is to trace R back to the corrupted servers given black-box access to R. Unlike Goyal et al.\, we do not assume that the tracing algorithm has any information on how the corrupted servers constructed R from the shares in their possession. \nWe then present two very efficient constructions of traceable secret sharing based on two classic secret sharing schemes. In both of our schemes\, shares are only twice as large as the secret\, improving over the quadratic overhead of Goyal et al. Our first scheme is obtained by presenting a new practical tracing algorithm for the widely-used Shamir secret sharing scheme. Our second construction is based on an extension of Blakley’s secret sharing scheme. Tracing in this scheme is optimally efficient\, and requires just one successful query to R. We believe that our constructions are an important step towards bringing traceable secret-sharing schemes to practice. This work also raises several interesting open problems that we describe in the paper. \nIf there’s time\, perhaps I’ll mention our new results on TSS (https://eprint.iacr.org/2025/1980) \n  \nSpeaker Bio: Aditi Partap is a fifth year CS Ph.D. student at Stanford University\, where she works on cryptography research (advised by Dan Boneh). Her current focus is on accountability in threshold cryptography and leader election protocols. \nPrior to joining Stanford\, she completed her Masters in May 2021 from University of Illinois at Urbana Champaign. She received her bachelors degree in Computer Science from IIT Delhi in 2018.
URL:https://homecse.iitd.ac.in/event/title-traceable-secret-sharing-strong-security-and-efficient-constructions-by-aditi-partap/
LOCATION:Bharti 501\, IIT Campus\, Hauz Khas\, New Delhi
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=Asia/Kolkata:20251215T120000
DTEND;TZID=Asia/Kolkata:20251215T130000
DTSTAMP:20261010T211112
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
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=Asia/Kolkata:20251212T110000
DTEND;TZID=Asia/Kolkata:20251212T120000
DTSTAMP:20261010T211112
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:20251211T110000
DTEND;TZID=Asia/Kolkata:20251211T170000
DTSTAMP:20261010T211112
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:20251209T140000
DTEND;TZID=Asia/Kolkata:20251209T150000
DTSTAMP:20261010T211112
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
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=Asia/Kolkata:20251201T160000
DTEND;TZID=Asia/Kolkata:20251201T170000
DTSTAMP:20261010T211112
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
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=Asia/Kolkata:20251201T120000
DTEND;TZID=Asia/Kolkata:20251201T130000
DTSTAMP:20261010T211112
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
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=Asia/Kolkata:20251201T110000
DTEND;TZID=Asia/Kolkata:20251201T120000
DTSTAMP:20261010T211112
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:20251127T120000
DTEND;TZID=Asia/Kolkata:20251127T130000
DTSTAMP:20261010T211112
CREATED:20251124T161843Z
LAST-MODIFIED:20251124T161843Z
UID:2212-1764244800-1764248400@homecse.iitd.ac.in
SUMMARY:Constructing Long Paths in Graphs Streams by Chhaya Trehan
DESCRIPTION:Venue: Bharti501 \nAbstract: In the graph stream model of computation\, an algorithm processes the edges of an n-vertex input graph in one or more sequential passes while using a memory that is sublinear in the input size. The streaming model poses significant challenges for algorithmically constructing long paths. Many known algorithms that are tasked with extending an existing path as a sub-routine require an entire pass over the input to add a single additional edge. This raises a fundamental question: Are multiple passes inherently necessary to construct paths of non-trivial lengths\, or can a single pass suffice? To address this question\, we systematically study the Longest Path problem in the one-pass streaming model. \nIn this problem\, given a desired approximation factor α\, the objective is to compute a path of length at least lp(G)/α\, where lp(G) is the length of a longest path in the input graph G.We give algorithms as well as space lower bound results for both undirected and directed graphs. Besides the insertion-only model\, where the input stream solely consists of the edges of the input graph\, we also study the insertion-deletion model\, where previously inserted edges may be deleted again. Our results include: \n1. We show that for undirected graphs\, in both the insertion-only and the insertion- deletion streaming models\, there are semi-streaming algorithms\, i.e.\, algorithms that use space O(n poly log n)\, that compute a path of length at least d/3 with high probability\, where d is the average degree of the input graph. These algorithms can also yield an α-approximation to Longest Path using space \tilde O(n^2/α). \n\n2. Next\, we show that such a result cannot be achieved for directed graphs\, even in the insertion-only model. We show that computing a (n^{1−o(1)})-approximation to Longest Path in directed graphs in the insertion-only model requires space Ω(n^2). This result is in line with recent results that demonstrate that processing directed graphs is often significantly harder than undirected graphs in the streaming model. \n\n3. We further complement our results with two additional lower bounds. First\, we show that semi-streaming space is insufficient for small constant factor approximations to Longest Path for undirected graphs in the insertion-only model. Last\, in undirected graphs in the insertion-deletion model\, we show that computing an α-approximation requires space Ω(n^2/α^3). \n\nBio: Chhaya has a PhD in mathematics from the London School of Economics & Political Science. Prior to doing a PhD she has worked in tech Industry for 8 years in various capacities. She is currently doing a postdoc at the Indian Statistical Institute\, Kolkata
URL:https://homecse.iitd.ac.in/event/constructing-long-paths-in-graphs-streams-by-chhaya-trehan/
LOCATION:Bharti 501\, IIT Campus\, Hauz Khas\, New Delhi
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=Asia/Kolkata:20251117T150000
DTEND;TZID=Asia/Kolkata:20251117T160000
DTSTAMP:20261010T211112
CREATED:20251112T104537Z
LAST-MODIFIED:20251112T104537Z
UID:2187-1763391600-1763395200@homecse.iitd.ac.in
SUMMARY:Rank bounds and Polynomial Identity Testing by Prof. Akash
DESCRIPTION:Venue: Bharti501 \nAbstract: Polynomial Identity Testing (PIT) is the problem of checking whether a given algebraic circuit computes the zero polynomial. The PIT problem has a myriad of applications\, such as algorithms for the perfect matching problem\, primality testing\, and learning algorithms for sparse polynomials. While there are efficient randomized algorithms for PIT\, there is no deterministic poly-time algorithm for general circuits. Derandomizing PIT is a foundational problem in theoretical computer science\, as it is also intrinsically related to lower bounds for algebraic circuits and the VP vs. VNP problem. \nIn this talk\, we will discuss the PIT problem for depth-4 circuits. I’ll talk about recent progress on proving rank bounds for depth-4 identities\, and the first deterministic poly-time algorithm for depth-4 circuits with top fan-in 3 and constant bottom fan-in. We will discuss algebraic-geometric ideas such as the Stillman uniformity principle\, that lead to rank bounds and non-linear generalizations of classical results from combinatorial geometry.\n\nBio: Prof. Akash is an Assistant Professor in the Department of Computer Science at Rutgers University. I am part of the Theory group at Rutgers. His research interests are in Mathematics and Theoretical Computer Science\, in particular algebraic geometry\, computational complexity theory\, coding theory\, combinatorics and number theory. 
URL:https://homecse.iitd.ac.in/event/rank-bounds-and-polynomial-identity-testing-by-prof-akash/
LOCATION:Bharti 501\, IIT Campus\, Hauz Khas\, New Delhi
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=Asia/Kolkata:20251110T120000
DTEND;TZID=Asia/Kolkata:20251110T130000
DTSTAMP:20261010T211112
CREATED:20251104T161156Z
LAST-MODIFIED:20251104T161156Z
UID:2181-1762776000-1762779600@homecse.iitd.ac.in
SUMMARY:Non-Closure properties in algebraic complexity by Dr. Prateek Dwivedi
DESCRIPTION:Venue: Bharti501\n\nAbstract: A central question in algebraic complexity theory is understanding the behaviour of polynomial computation models under basic algebraic operations. While closure under addition and multiplication holds for most of the standard models like algebraic circuits\, closure under factorisation remains subtle. In this talk\, we will discuss a new result which proves that the well-studied model called read-once oblivious algebraic branching programs (roABPs) is not closed under factoring. This offers a contrasting perspective in light of the recent breakthrough work that proved a unified framework for analysing closure under factorisation. We will also discuss similar non-closure properties of roABP under other natural operations such as powering and symmetric composition. \nThis is based on joint work with Andrews\, Armand\, Hansen\, Limaye\, Srinivasan\, and Tavenas. \n[arxiv]
URL:https://homecse.iitd.ac.in/event/non-closure-properties-in-algebraic-complexity-by-dr-prateek-dwivedi/
LOCATION:Bharti 501\, IIT Campus\, Hauz Khas\, New Delhi
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=Asia/Kolkata:20251107T120000
DTEND;TZID=Asia/Kolkata:20251107T130000
DTSTAMP:20261010T211112
CREATED:20251011T113534Z
LAST-MODIFIED:20251011T113534Z
UID:2091-1762516800-1762520400@homecse.iitd.ac.in
SUMMARY:Can a Bucket of Water Translate? Exploring the Encoding–Decoding Ability of Randomly Initialized Neuro-Symbolic Transformers by Dr. Arghya Pal
DESCRIPTION:Venue: Bhart501 \nAbstract: There is a growing interest in developing artificial neural networks through the training of large models. But what lies hidden in an overparameterized neural network with random weights? If the distribution is properly scaled\, such a network contains a subnetwork that can perform well without ever modifying its weights. The number of possible subnetworks grows combinatorially with the size of the network\, and modern neural networks often contain millions or even billions of parameters. Thus\, we should expect that even a randomly weighted neural network contains a subnetwork capable of performing well on a given task. The core idea of this talk is to frame the subnetwork-finding problem as a Differentiable Integer Linear Programming (ILP) Solving problem. Unlike existing neuro-symbolic solvers\, this talk will introduce an algorithm that does not require a continuous relaxation of semantic constraints. Instead\, it allows for a direct\, more precise\, and efficient integration of neural representations into the ILP formulation. By the end of the talk\, we will see that the solver achieves superior performance compared to conventional ILP solvers\, neuro-symbolic black-box solvers\, and Transformer-based encoders. Furthermore\, a deeper analysis reveals that such a solver can significantly enhance the precision\, consistency\, and faithfulness of the generated explanations. This opens new opportunities for advancing neuro-symbolic architectures toward explainable and transparent deep learning in complex domains. \n  \nBio: Dr. Arghya Pal is a Lecturer at Monash University. He earned his Ph.D. from the Indian Institute of Technology Hyderabad\, followed by postdoctoral research appointments at Monash University and Harvard University.  His research focuses on generative models\, transfer learning\, causal inference\, learning under limited supervision\, and logical reasoning. He is a recipient of a gold medal for his Master’s degree\, the Intel Ph.D. Fellowship (2016–2020)\, honored as best researcher award twice – one during PhD and other as an Alumni from IIT Hyderabad. He has been honored with the Magna-cum-Laude award from Harvard MRI society and best researcher award from School of IT Monash University. \nDr. Pal has experience in teaching units like Modeling Discrete Optimization Problems\, Data Analytics\, Deep Learning\, Programming Paradigm\, Malicious Attack and Dark Sides of AI\, and Modeling Data Science Problems at Monash University. He assumed academic positions beyond teaching such as; session chair in IJCNN\, Senior Area Chair in AAAI\, Reviewer of prestigious venues such as TPAMI\, NeurIPS\, AAAI\, CVPR\, ICLR\, etc.
URL:https://homecse.iitd.ac.in/event/can-a-bucket-of-water-translate-exploring-the-encoding-decoding-ability-of-randomly-initialized-neuro-symbolic-transformers-by-dr-arghya-pal/
LOCATION:Bharti 501\, IIT Campus\, Hauz Khas\, New Delhi
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=Asia/Kolkata:20251103T120000
DTEND;TZID=Asia/Kolkata:20251103T130000
DTSTAMP:20261010T211112
CREATED:20251030T064546Z
LAST-MODIFIED:20251030T064546Z
UID:2178-1762171200-1762174800@homecse.iitd.ac.in
SUMMARY:Two-party cryptography beyond computational assumptions: Some old and new results by Dr. Akshay Bansal
DESCRIPTION:Venue: Bharti-425 \nAbstract: The impossibility of information-theoretic or unconditional security under classical communication is already established for many two-party cryptographic primitives\, including but not limited to coin flipping\, bit commitment\, and oblivious transfer. In this talk\, we first discuss the known limits of information-theoretic security using quantum communication and propose the novel framework of stochastic switching that uses stochastic semidefinite programming to develop simple protocols for various two-party tasks. We also briefly discuss the insufficiency of standalone security from the perspective of (in)composability of a weaker version of coin flipping. \nBio: Akshay Bansal is currently a Senior Scientist at a stealth venture based out of Bangalore. He recently completed his Ph.D. in Computer Science from Virginia Tech advised by Jamie Sikora\, with a research focus on quantum algorithms\, learning theory\, and convex optimization. He holds a Bachelor’s from IIT Kanpur and Master’s in Computer Science from ISI Kolkata. He has previously worked at the Centre for Quantum Technologies in Singapore\, Institute for Quantum Computing at University of Waterloo\, and held research position at IISc Bengaluru. His current work delves into the mathematical foundations of quantum cryptography\, classical and quantum machine learning\, and convex analysis.
URL:https://homecse.iitd.ac.in/event/two-party-cryptography-beyond-computational-assumptions-some-old-and-new-results-by-dr-akshay-bansal/
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=Asia/Kolkata:20251030T110000
DTEND;TZID=Asia/Kolkata:20251030T120000
DTSTAMP:20261010T211112
CREATED:20251028T055031Z
LAST-MODIFIED:20251028T055031Z
UID:2136-1761822000-1761825600@homecse.iitd.ac.in
SUMMARY:Towards Reliable LLM Reasoning: Coordinated Agents\, Variance-Aware Evaluation\, and Lean Inference by Prof. Akhil Arora
DESCRIPTION:Abstract: Large language models (LLMs) are increasingly deployed as reasoning engines\, yet their practical use remains constrained by three persistent challenges: achieving high-quality reasoning at low cost\, measuring performance reliably\, and ensuring efficient\, reproducible deployment. In this talk\, I will present a research agenda addressing these challenges through new methods\, benchmarks\, and systems for practical LLM reasoning. I begin with Next\, I turn to Finally\, I focus on Together\, these contributions chart a path toward LLM reasoning that is not only more powerful\, but also leaner\, more reliable\, and environmentally responsible.\n\n  \n\nBio: Akhil Arora is a Tenure-Track Assistant Professor of Computer Science at Aarhus University\, where he heads the CLAN for AI Research on Language and Networks (or “CLAN” for short). He is a fellow of the Copenhagen Center for Social Data Science (SODAS)\, an affiliate of the Pioneer Centre for AI and ELLIS\, and a formal collaborator of the Wikimedia Foundation\, the non-profit organization that manages Wikipedia and related projects. Akhil’s research lies broadly in human-centered AI with a focus on improving human knowledge-seeking\, bridging knowledge gaps\, and promoting knowledge equity on the Web. To this end\, he devises methods and tools blending techniques from NLP\, AI\, Graph ML\, and Computational social science. Recently\, his group has been devising robust\, trustworthy\, accessible\, and efficient LLM inference strategies.\nAkhil received his PhD in Computer Science from EPFL (2024) in Switzerland\, his MS from IIT Kanpur (2013)\, and his undergraduate degree from NCU Gurgaon (2010). In days of yore\, he spent close to five years in the industry working with the research labs of Xerox and American Express as a Research Scientist. His work on influence maximization has been recognized as the 8th most influential paper of SIGMOD 2017 by Paper Digest and received the 2018 ACM SIGMOD Most Reproducible Paper Award. He is a recipient of the prestigious EDIC Doctoral Fellowship\, an alumnus of the coveted Heidelberg Laureate Forum\, and a DAAD AINet fellow on human-centered AI. Akhil is a director of the P1-programs on
URL:https://homecse.iitd.ac.in/event/towards-reliable-llm-reasoning-coordinated-agents-variance-aware-evaluation-and-lean-inference-by-prof-akhil-arora/
LOCATION:Bharti 501\, IIT Campus\, Hauz Khas\, New Delhi
CATEGORIES:Seminars
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BEGIN:VEVENT
DTSTART;TZID=Asia/Kolkata:20251028T120000
DTEND;TZID=Asia/Kolkata:20251028T130000
DTSTAMP:20261010T211112
CREATED:20251016T070350Z
LAST-MODIFIED:20251024T203402Z
UID:2115-1761652800-1761656400@homecse.iitd.ac.in
SUMMARY:Adaptive Human-Robot Interaction: Human Inspired Handovers and Robotic Failure Explanations. (An Intersection of Robotics and Machine Learning) by Dr. Parag Khanna
DESCRIPTION:Venue: Bharti-501/MS Teams \nAbstract: \nAs robots become more advanced\, they are expected to be increasingly present among humans\, engaging frequently in physical and social interactions. Among these interactions\, handovers—the transfer of an object from one individual to another—play a vital role in daily life. This talk focuses on my research on enhancing human-robot interaction (HRI) by drawing inspiration from human-human handovers and utilizing handovers to resolve robotic failures by providing explanations for these failures as well as adapting these explanations based on human behavioral responses. \nFor physical interaction\, I present my work on formulating a weight-adaptive robot grip release strategy that determines when to release an object as a human recipient begins to take it and adapts to variations in object weight. I recorded and published datasets of human-human handovers to develop data-driven (LSTM\, VAE-LSTM based) grip release strategies\, which were experimentally validated in user studies. I also present how object weight affects human motion during handovers\, enabling robots to observe changes in human motion to estimate object weights and adapt their motions to convey weight information. Lastly\, I present my research on the use of non-touch modalities\, such as EEG brain signals and gaze tracking\, to discern human intentions during HRI\, differentiating between motions intended for handovers and those that are not. \nFor social interaction\, I explored how different levels of explanation content impact collaborative performance of human-robot teams and human satisfaction. I present my research on explanation variation strategies for repeated failures and adapting explanations by predicting user confusion. I further present a context-specific explanation generation system using behavior tree representation of collaborative tasks combined with Large Language Models (LLMs). This system was implemented as a failure communication module that enabled adapting explanation levels based on user queries and behavior\, effectively improving failure resolution rates for collaborative tasks\, as evaluated in user studies. \nBy this talk\, I aim to demonstrate how human-inspired approaches and machine learning can enhance both physical and social aspects of HRI\, and to outline my future research directions for adaptive HRI. \n  \nBiosketch:\nDr. Parag Khanna is a postdoctoral researcher at the Division of Robotics\, Perception\, and Learning (RPL) at KTH Royal Institute of Technology\, Sweden. As a collaborative roboticist\, his research combines insights from human behavior\, cognitive science\, and robotics to design intuitive and explainable robotic systems. His key research topics include physical and social human-robot interaction (HRI)\, analyzing and learning from human behavior\, data-driven and human-inspired robotic strategies\, and adaptive explanations for robotic failures. He is passionate about bringing robotics from the lab to everyday life through adaptive\, user-centered solutions that make robots more effective\, safer\, and easier to work with in real-world environments. \nHe received his PhD from KTH in 2025\, focusing on human-robot interaction—specifically\, developing adaptive techniques for seamless handovers between robots and humans. He holds dual M.Sc. degrees from the Erasmus Mundus European Masters in Advanced Robotics (EMARO+) program— from École Centrale de Nantes\, France\, and from the University of Genoa\, Italy (2019). He earned his B.Tech. in Mechanical Engineering from Visvesvaraya National Institute of Technology (VNIT)\, Nagpur\, India\, in 2017\, where his bachelor’s thesis on an autonomous snake robot reconfigurable into a quadcopter led to an Indian patent filed in 2017 (granted in 2025). \nFrom 2019 to 2021\, he worked as a research engineer at the French National Center for Scientific Research (CNRS) in Nantes\, France\, designing and controlling a bio-inspired tensegrity manipulator. \nHe has also organized workshops at the IEEE Humanoids conferences (2024–25) and the IEEE IROS 2025 conference\, and serves as a program chair for the HRI Pioneers Workshop at the HRI 2026 conference and as a Associate Editor for the IEEE/SICE System Integration (SII) 2026 conference. \nDr. Khanna’s accomplishments include the Honorable Mention for Best Short Contribution Paper Award and selection as a HRI Pioneer at the ACM/IEEE HRI conference 2025\, Best Poster Awards at KTH EECS Research and Impact Day 2025 and the Human Agent Interaction conference (HAI) 2023\, and representation of VNIT at the National Innovation Club member meeting at the Rashtrapati Bhavan in 2017.
URL:https://homecse.iitd.ac.in/event/adaptive-human-robot-interaction-human-inspired-handovers-and-robotic-failure-explanations-an-intersection-of-robotics-and-machine-learning-by-dr-parag-khanna/
LOCATION:Bharti 501\, IIT Campus\, Hauz Khas\, New Delhi
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=Asia/Kolkata:20251027T120000
DTEND;TZID=Asia/Kolkata:20251027T130000
DTSTAMP:20261010T211112
CREATED:20251023T103521Z
LAST-MODIFIED:20251024T205651Z
UID:2117-1761566400-1761570000@homecse.iitd.ac.in
SUMMARY:Sketching and Uncertainity: Through the Geometric Lens by Prof. Sujoy
DESCRIPTION:Venue: Bharti501\nAbstract: In many modern applications\, including machine learning\, robotics\, distributed systems\, and network design\, the input\, often represented as points in a finite metric space\, can bve massive in size. Efficient proceesing of such data requires compact representations that preserve the essential structural properties of the underlying space. Metric sketching provides a principled way to achieve this compression. Among the most fundamental sketching primitives are spanners and tree covers\, which capture distance relationships in a concise form. \nIn the first part of the talk\, I will discuss recent advances in geometric sketching. Traditional algorithmic models assume complete knowledge of the input in advance; however\, this assumption often fails in dynamic scenarios where inpiuts evolve over time. In such settings\, algorithms must adapt to changes while maintaining strong performance guarantees. \nIn the second part\, I will explore the dynamic aspects of metric sketching part\, related problems\, and highlight emerging directions that connect geometry with uncertainity.
URL:https://homecse.iitd.ac.in/event/sketching-and-uncertainity-through-the-gerometric-lens-by-prof-sujoy/
LOCATION:Bharti 501\, IIT Campus\, Hauz Khas\, New Delhi
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BEGIN:VEVENT
DTSTART;TZID=Asia/Kolkata:20251014T120000
DTEND;TZID=Asia/Kolkata:20251014T130000
DTSTAMP:20261010T211112
CREATED:20251007T161139Z
LAST-MODIFIED:20251007T161139Z
UID:2079-1760443200-1760446800@homecse.iitd.ac.in
SUMMARY:Expanding the Frontiers of Computer Vision: From Robotics to Wildlife and Beyond by Ilan Shimshoni
DESCRIPTION:Abstract: In my talk I will describe in general how to cooperate with people from various fields of research in computer vision research projects and then describe three research projects that I was involved in in the last few years. \nIn the first project which is the field of archaeology we studied scarabs. Scarabs are seals whose origin is from ancient Egypt (2000 BC) but were also found in Israel. The dataset we obtained from an archaeologist consisted of pairs of a photograph and a drawing of a scarab made by an archaeological artist. We developed models for classifying the scarabs according to their etchings and according to the era when they were produced. The drawings are naturally of higher quality than the photographs. During training the model was fed with the photograph and the drawing and during inference only photographs were given as input\, since they are naturally more common. The algorithm also generated a drawing of the scarab. \nIn the second project\, which is in the field of agriculture\, a camera was placed above a drinking facility for sheep. The facility measures the amount of water the sheep drinks and its weight. A video of each sheep was recorded and its face\, back and legs were detected. The results of all these detections were fed into classifiers and the identity of the sheep was returned as a combination of the results from the single classifiers.  The process was basically automatic without human interaction. This algorithm can be used to monitor the condition of each sheep and report to the farmer if it seems that its medical condition has deteriorated. \nIn the last project\, which is in the field of ecology\, a colony of over a thousand terns on a small island was monitored. The terns fly from Europe to Africa and back and stay for some time on the island in Israel. The colony includes two types of terns: common terns and small terns. The whole island was scanned automatically using two PTZ cameras. Using Yolo the types of terns and whether they are brooding or not were classified. In a second stage the results improved since the actual size of the terns\, their motion pattern and their population statistics were taken into account. The results are very accurate and also include their geographic position on the island. This method can now be used to monitor the colony population over time. \nBio: Ilan Shimshoni has been working in the fields of computer vision\, computer graphics and machine learning for more than thirty years. He has been working on various problems in computer vision and applying them to applications in robotics and computer graphics. In recent years he has also been interested in addressing important problems in other fields which are challenging for researchers in my fields of research. He has been working for example on problems in medical rehabilitation\, geography\, agriculture\, and archaeology. One of my main fields of interest is developing algorithms in computer vision addressing challenges in the study of animals (wildlife\, pets\, and domestic animals). This include automatic detection of pain in cats and rabbits\, emotion in dogs\, and identification of individual sheep on a farm. In the realm of wildlife\, He has been working detecting flocks of birds from weather radars\, and counting terns of two types and identifying whether they are brooding or not. This helps ecologists estimate their number in one of the main places they stop in Israel while migrating from Europe to Africa.
URL:https://homecse.iitd.ac.in/event/expanding-the-frontiers-of-computer-vision-from-robotics-to-wildlife-and-beyond-by-ilan-shimshoni/
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:20251013T120000
DTEND;TZID=Asia/Kolkata:20251013T130000
DTSTAMP:20261010T211112
CREATED:20251007T162719Z
LAST-MODIFIED:20251007T162719Z
UID:2088-1760356800-1760360400@homecse.iitd.ac.in
SUMMARY:Frontiers in Boolean Circuit Lower Bounds by Dr. Vaibhav Krishan
DESCRIPTION:Venue: Bharti501 \nAbstract:\nBoolean circuits provide a combinatorial representation of computation\, where the number of gates (the size) represents running time\, and the number of layers (the depth) capture parallel running time.\nThey form a framework for answering fundamental questions such as P vs NP: proving that some NP problem requires circuits of superpolynomial size would separate P from NP. \nWith limited progress on this question for general circuits\, early breakthroughs focused on restricted circuit classes.\nHåstad (STOC `86) proved that constant-depth circuits with AND\, OR\, and NOT gates require exponential size to compute the parity function (which determines whether the sum of inputs is even or odd).\nRazborov (Matematicheskie Zametki `87) and\, independently\, Smolensky (STOC `87)\, extended this to circuits augmented with parity or modular gates (for prime moduli)\, showing that such circuits require exponential size to compute the majority function (which determines whether the sum of inputs is at least half their number). \nFollowing these foundational results\, research has advanced along two principal directions\, though further progress has become increasingly challenging.\nIn this talk\, I will present some of my work contributing new advances at the frontier of both directions. \n______________________________________________________________________________________________________________________________________ \nPart I: Threshold Circuits. \nThe first part of the talk will focus on constant-depth threshold circuits\, circuits that can use majority gates\, or more generally\, threshold gates.\nThreshold gates can be seen as a simple abstraction for neurons\, and threshold circuits were among the earliest models studied to understand the computational power of neural networks.\nThese circuits are quite powerful; for instance\, they can efficiently implement integer arithmetic operations such as exponentiation and square root. \nIn joint work with Bajpai\, Kush\, Limaye\, and Srinivasan\, Algorithmica `21\, we study a generalization of threshold circuits\, called polynomial threshold circuits\, that use polynomial threshold gates.\nA polynomial threshold gate outputs a Boolean value based on the sign of a polynomial evaluated over the inputs.\nWe design an algorithm to count the number of assignments on which a polynomial threshold circuit outputs 1.\nFor any constant depth\, our algorithm runs faster-than-brute-force when the circuit size is slightly superlinear and the degree of each gate is bounded by a constant.\nPrior to our work\, no such algorithm was known even for a single polynomial threshold gate\, except in the special case of degree 2.\nFaster-than-brute-force algorithms are known to imply circuit lower bounds (Williams\, JACM `14)\, although the particular lower bounds implied by our work were already established by Kane\, Kabanets\, and Lu (STOC `17). \nOur work builds on a long line of research initiated by Impagliazzo\, Paturi\, and Saks (SIAM J. Comput. `97)\, who proved a tight lower bound for threshold circuits with a slightly superlinear number of wires computing the parity function.\nTheir core idea\, simplification of threshold circuits under random partial assignments\, has inspired a series of influential results\, leading to average-case lower bounds and satisfiability algorithms (Chen\, Santhanam\, and Srinivasan\, Theory Comput. `18)\, as well as pseudorandom generator constructions (Hatami\, Hoza\, Tal\, and Tell\, FOCS `22).\nEven seemingly small improvements to these results could lead to major breakthroughs in circuit complexity (Chen and Tell\, STOC `19)\, marking a central frontier for the community. \n______________________________________________________________________________________________________________________________________ \nPart II: Modular Circuits \nThe second part of my talk will focus on circuits with modular gates for general (not necessarily prime) moduli.\nHere\, in a joint work with S. Vishwanathan\, we develop an approach toward resolving a long-standing conjecture (Barrington\, JCSS `89)\, that constant-depth circuits with modular gates (the modulus does not grow with input size) require superpolynomial size to compute the majority function.\nThe classical lower-bound techniques of Håstad and Razborov-Smolensky fail to extend to this setting\, while the algorithms-to-lower bounds framework of Williams (JACM `14) does not apply to simple functions such as majority\, making new ideas necessary. \nVarious approaches to this conjecture have been proposed over the years.\nIn earlier work (Krishan\, CSR 2019)\, I showed that torus polynomials provide the most refined framework for tackling this problem.\nTorus polynomials were introduced by Bhrushundi\, Hosseini\, Lovett\, and Rao (ITCS `19) for proving lower bounds against such modular circuits.\nUsing torus polynomials\, we translate the lower bound conjecture to the task of finding feasible solutions to an infinite family of linear programs.\nThis reformulation allows for incremental progress\, by finding solutions for progressively larger sets from the family. \nWe find solutions for almost all of these programs\, leaving only a finite set unresolved.\nFinding feasible solutions for the remaining cases would lead to a resolution of the conjecture.\nTo make this task more tractable\, we show that the family of programs has far fewer degrees of freedom than initially expected\, and we describe a potential set of feasible solutions for some of the remaining cases.\nI will conclude the talk with open problems and directions for future progress. \n  \nBio: Vaibhav is a postdoctoral researcher at The Institute of Mathematical Sciences\, Chennai. He completed his Ph.D. at IIT Bombay under the supervision of Prof. Sundar Vishwanathan and Prof. Nutan Limaye. Before beginning his Ph.D.\, he worked as a quantitative researcher and a data scientist for four years.
URL:https://homecse.iitd.ac.in/event/frontiers-in-boolean-circuit-lower-bounds-by-dr-vaibhav-krishan/
LOCATION:Bharti 501\, IIT Campus\, Hauz Khas\, New Delhi
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BEGIN:VEVENT
DTSTART;TZID=Asia/Kolkata:20251009T120000
DTEND;TZID=Asia/Kolkata:20251009T130000
DTSTAMP:20261010T211112
CREATED:20251007T162143Z
LAST-MODIFIED:20251007T162143Z
UID:2085-1760011200-1760014800@homecse.iitd.ac.in
SUMMARY:Incentives and Information in Algorithmics Economics by Dr. Divyarthi Mohan
DESCRIPTION:Venue: Bharti501/MS Teams \nAbstract: Digital markets and platforms have shaped the algorithmic landscape into a complex ecosystem of strategic\, self-interested entities. This has motivated the study and development of mechanisms or algorithms that are robust to strategic behaviour\, using tools from algorithms\, game theory and economics. Standard assumptions in mechanism design are too strong to capture the informational challenges present in many real scenarios\, from ad auctions where bidders’ values depend on competitors’ private market data\, to resource allocation where there is uncertainty about future demands. In this talk\, I will provide an overview of my recent work that tackles three important challenges—strategic behavior\, interdependence\, and online decision making—going beyond standard assumptions. In particular\, I will focus on my work establishing the first constant-approximation algorithms for prophet and secretary problems with interdependent values. \n  \nBio: Divyarthi Mohan is a postdoctoral researcher in the Faculty of Computing & Data Sciences at Boston University\, hosted by Kira Goldner. Her research broadly lies at the intersection of computer science and economics\, with a focus on algorithmic mechanisms design and the interplay of incentives and information. She obtained her PhD in Computer Science at Princeton University\, advised by Matt Weinberg\, and was previously a postdoctoral fellow at Tel Aviv University hosted by Michal Feldman. Her research has been recognized with the Simons-Berkeley Research Fellowship for Fall 2022\, the class of 2021 Siebel Scholarship\, and 2019 SEAS award for excellence at Princeton University\, and her work was invited to the Highlights Beyond EC 2024.
URL:https://homecse.iitd.ac.in/event/incentives-and-information-in-algorithmics-economics-by-dr-divyarthi-mohan/
LOCATION:Bharti 501\, IIT Campus\, Hauz Khas\, New Delhi
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DTSTART;TZID=Asia/Kolkata:20251007T120000
DTEND;TZID=Asia/Kolkata:20251007T130000
DTSTAMP:20261010T211112
CREATED:20251005T191731Z
LAST-MODIFIED:20251005T191731Z
UID:2082-1759838400-1759842000@homecse.iitd.ac.in
SUMMARY:Amnesiac Flooding and the curious case of a Unique Algorithm by Amitabh Trehan
DESCRIPTION:Venue: Bharti501 \nAbstract: In the field of distributed algorithm design\, it is often standard to abstract the network as an undirected graph with the nodes as vertices and connections as edges. About the simplest process one can imagine on a network/graph is flooding: A node is in possession of a message M which has to be  eventually sent to every node on the graph (this is called achieving broadcast)- the node sends M immediately to all its neighbours and they send to all their neighbours they did not just receive the message from and so on. Clearly\, this achieves broadcast. But\, how to achieve termination? i.e. the copies of the message should not circulate indefinitely. \n\n\nAt the advent of distributed computing\, more than 50 years ago\, a simple solution was devised – keep a copy of M\, and if M is received again\, simply discard this M. However\, this requires memory/state and a stack of earlier received messages. Surprisingly\, we discovered [PODC2019\,STACS2020\,DC2023] that state is unnecessary to achieve terminating broadcast – the same process without any state or memory beyond the immediate receipt (hence\, called Amnesiac Flooding (AF))\, due to some still slightly mysterious properties of simple undirected graphs\, terminates in asymptotically optimal time on every graph.  Intriguingly\, we have recently discovered [DISC2025] that AF is Unique! i.e. under certain reasonable conditions\, AF is the one and only algorithm that achieves terminating broadcast. Are there other examples of Unique algorithms in literature\, and is counting the number of algorithms for solving a problem a concept we can reasonably postulate? \n\n\nAF on Wikipedia: https://en.wikipedia.org/wiki/Amnesiac_flooding \n\nBio: Amitabh Trehan is an associate professor at the department of Computer Science\, Durham University\, where he heads the NESTiD (Network Engineering\, Science\, and Theory in Durham] research group. He did his PhD in Computer Science from the University of New Mexico\, USA\, following a M.Tech. in Computer Applications from the Indian Institute of Technology\, Delhi.
URL:https://homecse.iitd.ac.in/event/amnesiac-flooding-and-the-curious-case-of-a-unique-algorithm-by-amitabh-trehan/
LOCATION:Bharti 501\, IIT Campus\, Hauz Khas\, New Delhi
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DTSTART;TZID=Asia/Kolkata:20250912T120000
DTEND;TZID=Asia/Kolkata:20250912T130000
DTSTAMP:20261010T211112
CREATED:20250912T045614Z
LAST-MODIFIED:20250912T045614Z
UID:1862-1757678400-1757682000@homecse.iitd.ac.in
SUMMARY:Chromatic number of randomly augmented graphs by Prof. Anand Srivastav\, Kiel University
DESCRIPTION:Abstract: An extension of the Erdős-Renyi random graph model Gn\,p is the model of perturbed graphs introduced by Bohman\, Frieze and Martin (Bohman\, Frieze\, \nMartin 2003). This is a special case of the randomly augmented graphs studied in this paper. An augmented graph is the union of a deterministic host graph \nand a random graph. Among the first problems in perturbed graphs has been the question how many random edges are needed to ensure Hamiltonicity of \nthe graph. This question was answered in the paper by Bohman\, Frieze and Martin. The host graph is often chosen to be a dense graph. In recent years \nseveral papers on combinatorial functions of perturbed graphs were published\, e.g. on the emergence of powers of Hamiltonian cycles (Dudek\, Reiher\, Ruciński\, \nSchacht 2020)\, the properties of Positional Games played on perturbed graphs (Clemens\, Hamann\, Mogge\, Parczyk\, 2020) and the emergence of multiple \ninvariants e.g. fixed clique size (Bohman\, Frieze\, Krivelevich\, Martin\, 2004). In this talk I will present our results on the chromatic number of randomly augmented \ngraphs. \n 
URL:https://homecse.iitd.ac.in/event/chromatic-number-of-randomly-augmented-graphs-by-prof-anand-srivastav-kiel-university/
LOCATION:Bharti 501\, IIT Campus\, Hauz Khas\, New Delhi
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BEGIN:VEVENT
DTSTART;TZID=Asia/Kolkata:20250901T120000
DTEND;TZID=Asia/Kolkata:20250901T130000
DTSTAMP:20261010T211112
CREATED:20250806T062326Z
LAST-MODIFIED:20250809T094539Z
UID:1766-1756728000-1756731600@homecse.iitd.ac.in
SUMMARY:Giving Some Space Can Be Hard: Two New Models to Match Agents with Locations by Shivika Narang
DESCRIPTION:Abstract: There can be a multitude of reasons to match agents to specific locations in a given space. In this talk we cover two: distributing delivery orders and assigning shared hostel rooms. For both settings we shall try to find solutions that satisfy desirable properties and characterize instances for which they exist.\n \nWe first initiate the study of fair distribution of delivery tasks among a set of agents wherein delivery jobs are placed along the vertices of a graph. Our goal is to fairly distribute delivery costs (modeled as a submodular function) among a fixed set of agents while satisfying some desirable notions of economic efficiency. We characterize instances that admit fair and efficient solutions by exploiting underlying graph structures. Unfortunately\, finding these solutions proves to be NP-hard. We complement this by designing an XP algorithm (parameterized by the number of agents) that can find all fair and efficient solutions when they exist. We conclude this discussion by theoretically and experimentally analyzing the price of fairness.\n \nWe shall then introduce Leontief utilities to the problem of roommate matchings. We aim to find strategyproof mechanisms that give good bounds on agent welfare. We first find that no approximation to welfare can be achieved under strategyproof mechanisms for either Leontief or additive utilities. Even for binary additive utilities no maximum welfare mechanism can be strategyproof. In contrast\, we then –surprisingly– find that binary Leontief utilities enable us to find strategyproof mechanisms that maximize welfare.\n \nJoint work with Hadi Hosseini\, Sanjukta Roy and Tomasz Was.\n \nBio: Shivika Narang is a postdoctoral fellow at UNSW Sydney. Previously she was a postdoc at Simons Laufer Mathematical Sciences Institute\, Berkeley (SLMath) and completed her PhD from IISc Bengaluru. During her PhD\, she received the Tata Consultancy Services Research Fellowship. Her work is currently focused on finding fair and efficient solutions to societal problems. She largely works in computational social choice\, especially matching and allocation problems.
URL:https://homecse.iitd.ac.in/event/giving-some-space-can-be-hard-two-new-models-to-match-agents-with-locations-by-dr-shivika-narang/
LOCATION:Bharti 501\, IIT Campus\, Hauz Khas\, New Delhi
CATEGORIES:Seminars
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