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:20250101T000000 END:STANDARD END:VTIMEZONE BEGIN:VEVENT DTSTART;TZID=Asia/Kolkata:20260310T090000 DTEND;TZID=Asia/Kolkata:20260310T100000 DTSTAMP:20261010T145532 CREATED:20260302T102353Z LAST-MODIFIED:20260302T102353Z UID:2429-1773133200-1773136800@homecse.iitd.ac.in SUMMARY:Integration of Structured Reasoning and Data-driven Learning for Acting and Planning by Dr. Sunandita Patra DESCRIPTION:Teams Link: MS Teams\nAbstract: The talk will focus on enabling autonomous actors\, such as digital agents or robots\, to take deliberative actions towards achieving their long horizon goals\, in the face of uncertainty and dynamic events. Today\, dynamic events or failures often require human intervention\, system restarts\, retraining\, or redesign\, for example\, when robots get stuck in dead ends or digital agents collapse under unanticipated events. Existing methods either rely on rule-based reasoning\, where it is extremely difficult for human experts to compile and maintain a complete set of rules\, or black-box machine learning models that need to be trained extensively for individual tasks. Neither approach is well suited when actors are operating in a dynamically changing environment. This work aims to overcome these limitations by creating integrated planning and learning algorithms that are practical to be executed in real-world environments (spanning finance\, robotics and cybersecurity) by incorporating within a single framework: (a) deliberative acting\, (b) online planning\, and (c) data-driven learning from the actor’s experiences.\n\n\nBy integrating structured reasoning with data-driven learning\, the goal of this research is to push towards the next generation of autonomous systems\, general-purpose agentic AI that can plan and act deliberately across a set of diverse tasks and domains.\n\n \nBio: Sunandita Patra is a Research Lead at J. P. Morgan AI Research\, Chicago\, USA. Her research interests include the integration of acting\, planning\, and machine learning\, focusing on finance\, cybersecurity\, and robotics domains. She completed her PhD and PostDoc in Computer Science at the University of Maryland\, College Park\, and holds BTech and MTech degrees in Computer Science and Engineering from IIT Kharagpur.  Her work received the Best Student Paper Honorable Mention Award at ICAPS 2020\, and the Best Student Paper Finalist Award (Top 3) at ECMR 2025. For more details\, please visit https://sunanditapatra.wixsite.com/camp URL:https://homecse.iitd.ac.in/event/integration-of-structured-reasoning-and-data-driven-learning-for-acting-and-planning-by-dr-sunandita-patra/ END:VEVENT BEGIN:VEVENT DTSTART;TZID=Asia/Kolkata:20260306T120000 DTEND;TZID=Asia/Kolkata:20260306T130000 DTSTAMP:20261010T145532 CREATED:20260303T103646Z LAST-MODIFIED:20260303T103646Z UID:2432-1772798400-1772802000@homecse.iitd.ac.in SUMMARY:Neural Circuit Discovery via Representation and Dynamics by Savik Kinger DESCRIPTION:Abstract: Neuroscience and AI share a bottleneck: while one can build (artificial) or record (biological) complex networks\, we struggle to explain their functional circuitry; i.e.\, how they compute. In this talk I use whole-brain recordings from C. elegans\, a canonical neurobiological system\, as a concrete testbed for “circuit interpretability.” I then introduce two complementary inference approaches for turning high-dimensional activity data into mechanistic structure. Approach 1 treats circuit discovery as a representation problem: learn time-varying functional structure and uncover recurring\, stimulus-dependent modules rather than a single static connectivity map. Approach 2 treats circuit discovery as a dynamics problem: go beyond correlation to estimate directed\, time-lagged influence—i.e.\, which units appear to drive others and over what delays—using modern score-based generative modeling ideas. I will show how these ML methods produce testable hypotheses for biologists and\, potentially\, offer new avenues for understanding complex networks in AI. \nBio: Savik Kinger is a PhD candidate in Computer Science at Yale University\, advised by Steven Zucker. His research focuses on developing methods to analyze biological and artificial neural networks\, integrating ideas from machine learning\, dynamical systems\, and causal inference. He received Bachelor’s degrees in Math and Computer Science from Columbia University. He is supported by a Nathan Hale fellowship. URL:https://homecse.iitd.ac.in/event/neural-circuit-discovery-via-representation-and-dynamics-by-savik-kinger/ LOCATION:SIT 113\, Amar Nath and Shashi Khosla School of Information Technology\, Indian Institute of Technology\, Delhi\, Hauz Khas\, New Delhi\, Delhi\, 110016\, India CATEGORIES:Seminars END:VEVENT BEGIN:VEVENT DTSTART;TZID=Asia/Kolkata:20260219T110000 DTEND;TZID=Asia/Kolkata:20260219T120000 DTSTAMP:20261010T145532 CREATED:20260217T085749Z LAST-MODIFIED:20260217T085749Z UID:2426-1771498800-1771502400@homecse.iitd.ac.in SUMMARY:TESSERA: Programming Petabytes of Earth Observations using Foundation Models by Prof. Anil Madhavapeddy DESCRIPTION:Venue. Bharti 501\n\nAbstract. We present TESSERA\, a pixel-wise foundation model for multi-modal (Sentinel-1/2) earth observation time series that learns robust\, label-efficient embeddings.  Our goal with TESSERA is to make manipulating global satellite intelligence as easy as LLMs did for natural language! Towards this we release global\, annual\, 10m\, pixel-wise embeddings together with open weights and code and lightweight adaptation heads\, providing practical tooling for large-scale retrieval and inference at planetary scale.  As with any good foundation model\, there are a staggering array of downstream tasks which can benefit. TESSERA embeddings deliver state-of-the-art accuracy with high label efficiency across diverse classification\, segmentation\, and regression tasks.\n\nIn this talk\, I’ll take you through an array of problems our users are applying it to\, ranging from the ecological to the urban to the temporal. By the end of the talk\, we aim to have you identify a seemingly impossible spatial problem that is now within range to solve yourself using our easy-to-install Python package\, geotessera. Bring your favourite coding agents!\n\nSpeaker: Anil Madhavapeddy\, Professor of Planetary Computing\, University of Cambridge URL:https://homecse.iitd.ac.in/event/tessera-programming-petabytes-of-earth-observations-using-foundation-models-by-prof-anil-madhavapeddy/ LOCATION:Bharti 501\, IIT Campus\, Hauz Khas\, New Delhi END:VEVENT BEGIN:VEVENT DTSTART;TZID=Asia/Kolkata:20260212T120000 DTEND;TZID=Asia/Kolkata:20260212T130000 DTSTAMP:20261010T145532 CREATED:20260204T131611Z LAST-MODIFIED:20260204T131643Z UID:2416-1770897600-1770901200@homecse.iitd.ac.in SUMMARY:Abstractions for expressive\, extensible\, and scalable root cause analysis by Vipul Harsh DESCRIPTION:Venue: Bharti501 \nAbstract: Modern Internet-scale services must identify and mitigate customer-impacting incidents quickly. Despite the development of many Root Cause Analysis (RCA) algorithms—including recent LLM-assisted solutions—existing approaches struggle with the “long tail” of novel failure modes and the sheer scale of telemetry. In this talk\, I argue that the path forward requires a paradigm shift from developing point-solution algorithms to a systems-first approach. I will introduce MoCE: a Mixture-of-Experts (MoE) framework that provides high-level abstractions for failure diagnosis. This framework enables developers to express complex troubleshooting logic succinctly using MoCE’s domain specific operators while providing the underlying systems support for scalable telemetry processing. Finally\, I will briefly describe how these abstractions empower reliable\, autonomous agents to perform interactive diagnosis and discuss ongoing and promising future work based on these ideas. \nBio: Vipul Harsh is a postdoctoral researcher at Conviva with Vyas Sekar and Hui Zhang and a visiting researcher at Carnegie Mellon University (CMU). His research lies at the intersection of networked systems\, AI\, and theory. His works span design of reliable agents for interactive troubleshooting\, systems for failure diagnosis in networked systems\, datacenter topology\, distributed monitoring\, and parallel algorithms. His research has been published in top-tier CS conferences (SIGCOMM\, NSDI\, SPAA among others) and his projects have been adopted into real-world products. He completed his Ph.D. from UIUC where he worked with Brighten Godfrey and holds an undergraduate degree from IIT Bombay. His thesis was nominated by UIUC for the ACM SIGCOMM dissertation award. URL:https://homecse.iitd.ac.in/event/2416/ LOCATION:Bharti 501\, IIT Campus\, Hauz Khas\, New Delhi END:VEVENT BEGIN:VEVENT DTSTART;TZID=Asia/Kolkata:20260206T160000 DTEND;TZID=Asia/Kolkata:20260206T170000 DTSTAMP:20261010T145532 CREATED:20260206T095221Z LAST-MODIFIED:20260206T095221Z UID:2419-1770393600-1770397200@homecse.iitd.ac.in SUMMARY:Molecular Machine Learning for Chemical Catalysis by Dr. Sukriti Singh DESCRIPTION:Venue: SIT001\nAbstract: The development of new reaction methodology could become a tedious task demanding both time and resources. The application of machine learning (ML) approaches for reaction optimization and prediction can make a significant impact on efficient exploration of the high-dimensional chemical space. But the direct adaptation of ML as used in well-developed domains\, is unlikely to succeed in reaction discovery. Some of the challenges stem from ineffective featurization of the molecular space\, unavailability of quality data and its distribution. Given these backgrounds\, rendering ML tools conducive for reactions is an exciting as well as challenging endeavor at the same time.\nIn this talk\, I will present molecular machine learning strategies specifically designed for small-data reaction discovery\, typically involving only hundreds to a few thousand data points. I will first discuss feature engineering approaches based on quantum-chemically derived physical organic descriptors\, illustrated through catalytic asymmetric hydrogenation of imines and alkenes for predicting enantioselectivity. I will then introduce feature learning methods that learn molecular representations directly from data and demonstrate their effectiveness in predicting reaction yield and enantioselectivity across diverse catalytic transformations.\n\nTo address data scarcity\, I will describe a transfer learning framework in which a chemical language model is trained on large number of molecules and fine-tuned on a focused library of desired reactions. Finally\, I will briefly introduce a meta-learning workflow that leverages literature-derived reaction data to identify shared reaction features\, enabling accurate outcome prediction with only a few experimental examples. Overall\, this talk will highlight how technically sound deployment of molecular machine learning tools can guide reaction development and help us get closer to sustainable practices by reducing the number of heuristic and empirical steps.\n\nBio: Sukriti Singh received her M.Sc. and Ph.D. degrees from the Department of Chemistry\, IIT Bombay in 2022 under the supervision of Prof. Raghavan B. Sunoj. She received the Naik and Rastogi Award for Excellence in Ph.D. Research. She was a postdoctoral research associate with Prof. J. M. Hernandez-Lobato at the Department of Engineering\, University of Cambridge. Her research interests involve density functional theory studies of catalytic reactions and developing machine learning methods to tackle low-data situations aimed at accelerating the exploration of chemical reaction space of high contemporary interest. URL:https://homecse.iitd.ac.in/event/molecular-machine-learning-for-chemical-catalysis-by-dr-sukriti-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:20260205T120000 DTEND;TZID=Asia/Kolkata:20260205T130000 DTSTAMP:20261010T145532 CREATED:20260131T075508Z LAST-MODIFIED:20260131T075508Z UID:2410-1770292800-1770296400@homecse.iitd.ac.in SUMMARY:Approximately Packing Dijoins Via Nowhere-Zero Flows by Dr. Ravi DESCRIPTION:Venue: Bharti501 \nAbstract: In a digraph\, a dicut is a cut where all the arcs cross in one direction. A dijoin is a subset of arcs that intersects each dicut. Woodall conjectured in 1976 that in every digraph\, the minimum size of a dicut equals to the maximum number of disjoint dijoins. By building connections with nowhere-zero k-flows\, we prove that every digraph with minimum dicut size $\tau$ contains $\lfloor \tau/k \rfloor$ disjoint dijoins if the underlying undirected graph admits a nowhere-zero k-flow. \nJoint work with Gérard Cornuéjols (CMU) and Siyue Liu (CMU) \nSpeaker Bio: Dr. Ravi is the Vasantrao Dempo Professor of Operations Research and Computer Science at Carnegie Mellon University. His research is on models\, methods and applications of discrete optimization and their application to business and technological systems. He has published widely in diverse areas ranging from theoretical computer science to Operations and Marketing. In Computer Science\, his main research interests are in approximation algorithms and network optimization. URL:https://homecse.iitd.ac.in/event/approximately-packing-dijoins-via-nowhere-zero-flows-by-dr-ravi/ LOCATION:Bharti 501\, IIT Campus\, Hauz Khas\, New Delhi END:VEVENT BEGIN:VEVENT DTSTART;TZID=Asia/Kolkata:20260204T120000 DTEND;TZID=Asia/Kolkata:20260204T130000 DTSTAMP:20261010T145532 CREATED:20260201T193547Z LAST-MODIFIED:20260201T193547Z UID:2413-1770206400-1770210000@homecse.iitd.ac.in SUMMARY:A new characterization of VNP via colored determinant by Dr. Prasad Chaugule DESCRIPTION:Venue: Bharti501 \nAbstract: Understanding the algebraic complexity class VNP through alternative characterizations is a central theme in algebraic complexity theory\, closely tied to the VP vs. VNP problem. While the permanent provides a canonical complete polynomial for VNP\, identifying natural and combinatorial variants that lead to new structural insights remains an important challenge.In this talk\, I will present a new characterization of VNP based on acombinatorial variant of the determinant\, which we call the colored determinant. This polynomial is defined as a signed sum over properly colored cycle covers of a directed graph\, where each cycle is required to be monochromatic. We show that the colored determinant is VNP-complete under p-projections over all fields\, thereby adding a new non-monotone VNP-complete polynomial family distinct from the permanent and previously studied determinant variants. \nUsing this polynomial\, we introduce a new computational model called the conditional stack branching program. Unlike standard stack branching programs\, this model allows the stack operation on an edge to depend on the current top of the stack. We show that this added conditional power is sufficient to increase expressiveness: a single-stack conditional stack branching program already characterizes VNP. This sharply contrasts with prior results\, where at least two stacks were required to capture VNP. \n  \nSpeaker Bio: Dr. Prasad Chaugule is a Post Doctoral fellow in the Theory Group at Department of Computer Science and Engineering\, IIT Delhi. His research lies in Arithmetic Circuit Complexity. He earned his Ph.D from IIT Bombay. URL:https://homecse.iitd.ac.in/event/a-new-characterization-of-vnp-via-colored-determinant-by-dr-prasad-chaugule/ LOCATION:Bharti 501\, IIT Campus\, Hauz Khas\, New Delhi END:VEVENT BEGIN:VEVENT DTSTART;TZID=Asia/Kolkata:20260202T120000 DTEND;TZID=Asia/Kolkata:20260202T130000 DTSTAMP:20261010T145532 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:20261010T145532 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:20261010T145532 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:20261010T145532 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 ) 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:20261010T145532 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:20261010T145532 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:20261010T145532 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:20261010T145532 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:20261010T145532 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