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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:20260101T000000
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BEGIN:VEVENT
DTSTART;TZID=Asia/Kolkata:20260202T120000
DTEND;TZID=Asia/Kolkata:20260202T130000
DTSTAMP:20261010T140004
CREATED:20260115T072749Z
LAST-MODIFIED:20260115T072749Z
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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
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DTSTART;TZID=Asia/Kolkata:20260204T120000
DTEND;TZID=Asia/Kolkata:20260204T130000
DTSTAMP:20261010T140004
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
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DTSTART;TZID=Asia/Kolkata:20260205T120000
DTEND;TZID=Asia/Kolkata:20260205T130000
DTSTAMP:20261010T140004
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
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DTSTART;TZID=Asia/Kolkata:20260206T160000
DTEND;TZID=Asia/Kolkata:20260206T170000
DTSTAMP:20261010T140004
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
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DTSTART;TZID=Asia/Kolkata:20260212T120000
DTEND;TZID=Asia/Kolkata:20260212T130000
DTSTAMP:20261010T140004
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
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BEGIN:VEVENT
DTSTART;TZID=Asia/Kolkata:20260219T110000
DTEND;TZID=Asia/Kolkata:20260219T120000
DTSTAMP:20261010T140004
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
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