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X-WR-CALNAME:Computer Science and Engineering
X-ORIGINAL-URL:https://homecse.iitd.ac.in
X-WR-CALDESC:Events for Computer Science and Engineering
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BEGIN:VTIMEZONE
TZID:Asia/Kolkata
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TZOFFSETFROM:+0530
TZOFFSETTO:+0530
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DTSTART:20260101T000000
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BEGIN:VEVENT
DTSTART;TZID=Asia/Kolkata:20260105T120000
DTEND;TZID=Asia/Kolkata:20260105T130000
DTSTAMP:20261010T144524
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:20260108T120000
DTEND;TZID=Asia/Kolkata:20260108T130000
DTSTAMP:20261010T144524
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:20260109T160000
DTEND;TZID=Asia/Kolkata:20260109T170000
DTSTAMP:20261010T144524
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:20260115T110000
DTEND;TZID=Asia/Kolkata:20260115T120000
DTSTAMP:20261010T144524
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:20260116T120000
DTEND;TZID=Asia/Kolkata:20260116T130000
DTSTAMP:20261010T144524
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:20260122T120000
DTEND;TZID=Asia/Kolkata:20260122T130000
DTSTAMP:20261010T144524
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:20260129T120000
DTEND;TZID=Asia/Kolkata:20260129T130000
DTSTAMP:20261010T144524
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:20260130T120000
DTEND;TZID=Asia/Kolkata:20260130T130000
DTSTAMP:20261010T144524
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
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