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TZID:Asia/Kolkata
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DTSTART:20250101T000000
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BEGIN:VEVENT
DTSTART;TZID=Asia/Kolkata:20251027T120000
DTEND;TZID=Asia/Kolkata:20251027T130000
DTSTAMP:20261011T032228
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
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=Asia/Kolkata:20251014T120000
DTEND;TZID=Asia/Kolkata:20251014T130000
DTSTAMP:20261011T032228
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
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=Asia/Kolkata:20251013T120000
DTEND;TZID=Asia/Kolkata:20251013T130000
DTSTAMP:20261011T032228
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
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=Asia/Kolkata:20251009T120000
DTEND;TZID=Asia/Kolkata:20251009T130000
DTSTAMP:20261011T032228
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
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=Asia/Kolkata:20251007T120000
DTEND;TZID=Asia/Kolkata:20251007T130000
DTSTAMP:20261011T032228
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
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=Asia/Kolkata:20250912T120000
DTEND;TZID=Asia/Kolkata:20250912T130000
DTSTAMP:20261011T032228
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
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=Asia/Kolkata:20250901T120000
DTEND;TZID=Asia/Kolkata:20250901T130000
DTSTAMP:20261011T032228
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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BEGIN:VEVENT
DTSTART;TZID=Asia/Kolkata:20250822T140000
DTEND;TZID=Asia/Kolkata:20250822T150000
DTSTAMP:20261011T032228
CREATED:20250827T082004Z
LAST-MODIFIED:20250827T082004Z
UID:1825-1755871200-1755874800@homecse.iitd.ac.in
SUMMARY:Optimal Capacity Modification for Stable Matchings with Ties by Dr. Keshav Ranjan
DESCRIPTION:Abstract: In this talk\, we consider the Hospitals/Residents (HR) problem in the presence of ties in preference lists of hospitals. Among the three notions of stability\, viz. weak\, strong\, and super stability\, we focus on strong stability. Strong stability is appealing both theoretically and practically; however\, its existence is not guaranteed. Our objective is to optimally increase hospitals’ quotas so that the resulting instance admits a strongly stable matching.\nSuch an augmentation is guaranteed to exist when resident preference lists are strict. We explore two natural optimization criteria:\n\n\n\nMINSUM: minimizing the total capacity increase across all hospitals and \nMINMAX: minimizing the maximum capacity increase for any hospital\n\nWe prove that the MINSUM problem admits a polynomial-time algorithm\, whereas the MINMAX problem is NP-hard. We prove an analogue of the Rural Hospitals theorem for the MINSUM problem. When each hospital incurs a cost for a unit increase in its quota\, the MINSUM problem becomes NP-hard\, even for 0/1 costs. In fact\, we show that the problem cannot be approximated to any multiplicative factor. We also present a polynomial-time algorithm for optimal MINSUM augmentation when a specified subset of edges is required to be included in the matching.\n\nThe talk is based on a recent work accepted at IJCAI 2025 and is a joint work with Meghana Nasre (IIT-M) and Prajakta Nimbhorkar (CMI). \nBio: Keshav Ranjan recently (July 2025) completed his Ph.D. from the Department of Computer Science and Engineering\, IIT Madras\, under the supervision of Dr. Meghana Nasre. His Doctoral thesis\, titled “Two-Sided Matchings: Lower Quotas\, Ties\, and Capacity Augmentation”\, focuses on the algorithmic aspects of two-sided matching problems under various constraints. Previously\, he held an M. Tech degree in Mathematics and Computing from the Department of Mathematics\, IIT Patna. His research interests lie in the broad area of Graph Algorithms\, with a particular focus on matching problems with preferences.
URL:https://homecse.iitd.ac.in/event/optimal-capacity-modification-for-stable-matchings-with-ties-by-dr-keshav-ranjan/
LOCATION:Bharti 501\, IIT Campus\, Hauz Khas\, New Delhi
CATEGORIES:Seminars
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=Asia/Kolkata:20250814T120000
DTEND;TZID=Asia/Kolkata:20250814T130000
DTSTAMP:20261011T032228
CREATED:20250812T055310Z
LAST-MODIFIED:20250812T055310Z
UID:1773-1755172800-1755176400@homecse.iitd.ac.in
SUMMARY:A brief survey of quantum numerical algorithms by Pranav Singh
DESCRIPTION:Title: A brief survey of quantum numerical algorithms \nSpeaker: Prof. Pranav Singh \nDetails: August 14 (Thursday) | 12(noon)-1 PM | Bharti 501 \nAbstract:\nQuantum Numerical Algorithms (QNA) encompass a broad class of techniques including quantum numerical linear algebra (QNLA)\, quantum optimization\, quantum variational algorithms (QVA)\, quantum machine learning (QML)\, and Hamiltonian simulation (HS). These areas represent some of the most promising domains for realizing exponential quantum advantage and have seen rapid theoretical and algorithmic advances in recent years. In this talk\, I will provide a concise overview of these developments and highlight key challenges: both in terms of current quantum hardware limitations and the conceptual gap between classical numerical methods and emerging quantum paradigms. The goal is to offer both a technical snapshot of the field and a broader perspective on what makes quantum numerical thinking distinct and potentially transformative.
URL:https://homecse.iitd.ac.in/event/a-brief-survey-of-quantum-numerical-algorithms-by-pranav-singh/
LOCATION:Bharti 501\, IIT Campus\, Hauz Khas\, New Delhi
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=Asia/Kolkata:20250812T153000
DTEND;TZID=Asia/Kolkata:20250812T163000
DTSTAMP:20261011T032228
CREATED:20250809T092746Z
LAST-MODIFIED:20250809T092746Z
UID:1768-1755012600-1755016200@homecse.iitd.ac.in
SUMMARY:Enabling Energy-efficient AI Computing: Leveraging Application-specific Approximations by Akash Kumar
DESCRIPTION:Speaker: Akash Kumar (Ruhr University Bochum)\nDetails: Tue\, 12 Aug\, 3:30 PM\, SIT 001\n\nAbstract: The widespread adoption of Artificial intelligence and Machine Learning (AI/ML) models across various fields\, such as healthcare\, autonomous vehicles\, smart agriculture\, and industrial automation\, has led to a growing demand for efficient and scalable AI/ML solutions. However\, as AI/ML algorithms grow more complex\, their substantial memory requirements and high energy consumption pose significant challenges for deployment on resource-constrained embedded systems\, such as wearable health monitors and IoT devices. \nIn this talk\, I will first introduce the topic and outline the significance of cross-layer approximation framework\, emphasizing the necessity of a generic and scalable approach to designing approximate arithmetic operators. I will then talk about platform-specific optimizations for designing approximate operators optimized for FPGAs and end with how modern AI/ML-based DSE approaches can be used for approximate computer arithmetic. \nBiography: Akash Kumar received the joint Ph.D. degree in electrical engineering and embedded systems from the Eindhoven University of Technology\, Eindhoven\, The Netherlands\, and the National University of Singapore (NUS)\, Singapore\, in 2009. From 2009 to 2015\, he was with NUS. From October 2015 until March 2024\, he was a Professor with Technische Universität Dresden\, Dresden\, Germany\, where he was directing the Chair for Processor Design. Since April 2024\, he is directing the chair of Embedded Systems at Ruhr University Bochum\, Germany. His research interests include the design and analysis of low-power embedded multiprocessor systems and designing secure systems with emerging nano-technologies.
URL:https://homecse.iitd.ac.in/event/enabling-energy-efficient-ai-computing-leveraging-application-specific-approximations-by-akash-kumar/
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:20250811T120000
DTEND;TZID=Asia/Kolkata:20250811T130000
DTSTAMP:20261011T032228
CREATED:20250809T094501Z
LAST-MODIFIED:20250809T094501Z
UID:1770-1754913600-1754917200@homecse.iitd.ac.in
SUMMARY:Explainable AI for Malware Analysis by Mohd Saqib
DESCRIPTION:Title: Explainable AI for Malware Analysis \n  \nAbstract: In recent years\, explainable artificial intelligence (XAI) has become a critical component of ensuring transparency and trust in machine learning systems\, particularly in high-stakes domains like cybersecurity. This talk will begin with a basic introduction to XAI\, highlighting its importance in understanding model decisions\, especially in the context of malware detection. I will then introduce GAGE (Genetic Algorithm-based Graph Explainer)\, a novel framework specifically designed for malware analysis. GAGE utilizes graph-based representations of malware features and applies a genetic algorithm to generate meaningful explanations for model predictions. This approach allows for both global and local interpretability of malware detection models\, making it easier for security professionals to understand how malware is identified and how detection decisions are made. The presentation will cover the theoretical foundations\, implementation details\, and experimental results of GAGE\, showcasing its potential to enhance trust and efficacy in automated malware detection systems. \n  \nBrief Bio: Dr. Mohd Saqib is a researcher and scholar specializing in Explainable AI (XAI)\, machine learning\, and cybersecurity. He completed his Ph.D. at McGill University\, where his research focused on developing interpretable models for malware analysis in collaboration with Defence Research and Development Canada (DRDC). Dr. Saqib also holds an M.Tech in Data Analytics from Indian Institute of Technology (ISM) Dhanbad. He has authored several Q1 journal papers\, including a comprehensive analysis of XAI for malware hunting published in ACM Computing Surveys (IF 23.8). Dr. Saqib has filed four U.S. patents in AI-related technologies during his collaborations with BlackBerry and Zayed University. In addition to his research\, Dr. Saqib has been a Teaching Assistant (TA) for cybersecurity\, hacking\, and AI courses at McGill University. He was honored with the Graduate Excellence Award at McGill and received the prestigious FRQNTscholarship. His expertise spans AI model explainability\, malware detection\, and the intersection of AI with critical infrastructure.
URL:https://homecse.iitd.ac.in/event/explainable-ai-for-malware-analysis-by-mohd-saqib/
LOCATION:Bharti 501\, IIT Campus\, Hauz Khas\, New Delhi
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=Asia/Kolkata:20250808T120000
DTEND;TZID=Asia/Kolkata:20250808T130000
DTSTAMP:20261011T032228
CREATED:20250804T083721Z
LAST-MODIFIED:20250804T083759Z
UID:1754-1754654400-1754658000@homecse.iitd.ac.in
SUMMARY:Multiturn Evals (and RL) for LLMs by Kartikeya Badola
DESCRIPTION:Title: Multiturn Evals (and RL) for LLMs \nDetails: 8th August\, 12 pm\, SIT001 \nAbstract: LLMs often fail at multi-step tasks requiring memory and strategic planning\, a gap not captured by traditional single-turn evals. To address this\, we’ve developed a suite of human and automated evals that stress test Gemini on these capabilities. This talk will cover the motivation and design behind these evals\, a discussion on latest results and will also touch upon some of the early promising experiments using multiturn RL methods to address some of these losses. \nBio: Kartikeya Badola is a Software Engineer at Google DeepMind in London\, where he works with the Gemini evals and Gemini thinking teams. Prior to this\, he was with Google Research in India\, working on multilingual semantic parsing. Kartikeya is a graduate of IIT Delhi\, where he worked with Prof. Mausam and Prof. Parag Singla on Distantly Supervised Relation Extraction. \n 
URL:https://homecse.iitd.ac.in/event/multiturn-evals-and-rl-for-llms-by-kartikeya-badola/
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:20250806T120000
DTEND;TZID=Asia/Kolkata:20250806T130000
DTSTAMP:20261011T032228
CREATED:20250730T061826Z
LAST-MODIFIED:20250730T165814Z
UID:1727-1754481600-1754485200@homecse.iitd.ac.in
SUMMARY:Rank Aggregation and Fairness by Diptarka Chakraborty
DESCRIPTION:Abstract: Aggregating multiple input rankings over a set of candidates to generate a consensus ranking is one of the fundamental ranking problems\, having many applications in social choice theory\, hiring\, college admission\, web search\, and databases. However\, the optimal consensus ranking might be biased against any individual candidate or candidates belonging to certain marginalized communities or groups. This has motivated studies of the rank aggregation problem from the fairness perspective. While finding a consensus ranking\, the additional objective is to ensure fair representation of each group in the top positions of the final aggregated ranking. In this talk\, we will discuss various algorithms to find such a fair ranking approximately.\n\nSpeaker: Diptarka Chakraborty is an Assistant Professor at the National University of Singapore. He did his Ph.D. at the Indian Institute of Technology\, Kanpur. Before joining NUS\, he spent two years at Charles University\, Prague\, and then almost a year at Weizmann Institute of Science\, Israel\, as a post-doctoral fellow. His research interest mostly lies in theoretical computer science\, more specifically\, algorithms on large data sets\, approximation algorithms\, sublinear algorithms\, string matching algorithms\, and graph algorithms. He is a recipient of the best paper award at FOCS 2018 and the Google South & Southeast Asia Research Award 2022.
URL:https://homecse.iitd.ac.in/event/rank-aggregation-and-fairness/
LOCATION:Bharti 501\, IIT Campus\, Hauz Khas\, New Delhi
CATEGORIES:Seminars
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=Asia/Kolkata:20250728T113000
DTEND;TZID=Asia/Kolkata:20250728T123000
DTSTAMP:20261011T032228
CREATED:20250721T150910Z
LAST-MODIFIED:20250728T034019Z
UID:1709-1753702200-1753705800@homecse.iitd.ac.in
SUMMARY:Dot-Product Proofs by Prahladh Harsha
DESCRIPTION:Title: Dot-Product Proofs\n\nSpeaker: Prahladh Harsha (TIFR Mumbai):  Details: July 28 (Monday) | 11:30 AM | Bharti 404 \n\n\nAbstract: A dot-product proof is a simple probabilistic proof system in which the verifier decides whether to accept an input vector based on a single linear combination of the entries of the input and a proof vector. In this talk\, I will present constructions of linear-size dot-product proofs for circuit satisfiability and discuss two kinds of applications: basing the exponential-time hardness of approximating MAX-LIN (maximal number of linear equations that can be simultaneously satisfied) on the standard exponential-time hypothesis\, and minimizing the verification complexity of cryptographic proof systems.\n\n[Joint work with Nir Bitansky\, Yuval Ishai\, Ron Rothblum\, and David Wu] \nBio: Prahladh Harsha is a Professor at the School of Technology and Computer Science at the Tata Institute of Fundamental Research (TIFR)\, Mumbai\, India. He obtained his BTech degree in Computer Science and Engineering from IIT Madras in 1998 and his PhD in Computer Science from the Massachusetts Institute of Technology (MIT) in 2004. He has worked at Microsoft Research\, TTI Chicago\, and has been at TIFR since 2010. \nPrahladh’s research interests are in the area of theoretical computer science\, with special emphasis on computational complexity theory and algebraic coding theory. He is best known for his work in the area of probabilistically checkable proofs. Prahladh Harsha is a winner of the NASI Young Scientist Award for Mathematics and the Swarnajayanti Fellowship (Govt. of India). \n\n\nProf. Harsha has served on the editorial boards of SIAM Journal on Computing and Algorithmica. He is currently the Editor-in-Chief of the ACM Transactions on Computation Theory and a Fellow of the Indian Academy of Sciences.
URL:https://homecse.iitd.ac.in/event/dot-product-proofs-by-prahladh-harsha/
LOCATION:Bharti 404\, IIT Delhi\, Delhi\, New Delhi\, 110016\, India
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=Asia/Kolkata:20250725T120000
DTEND;TZID=Asia/Kolkata:20250725T130000
DTSTAMP:20261011T032228
CREATED:20250724T051140Z
LAST-MODIFIED:20250724T051140Z
UID:1721-1753444800-1753448400@homecse.iitd.ac.in
SUMMARY:Thinking Geometrically in the World of Data-driven Models by Niloy J. Mitra
DESCRIPTION:Title: Thinking Geometrically in the World of Data-driven Models \nAbstract:\nAs learning models grow in scale and capacity\, a natural question arises: is it still relevant to think geometrically\, or should we seek more data? In this talk\, I will argue that geometry remains essential\, not just for interpretability\, but for enabling control\, structure\, and generalization. I will illustrate this through three examples. First\, how to distil image diffusion features to enrich geometric models by combining projection operation with diffusion features. Second\, in video generation\, 3D awareness proves critical for spatial control\, object permanence\, and task awareness in multimodal LLMs. Finally\, I will present a neural surface representation that exposes direct access to first and second fundamental forms\, enabling a new approach to defining Laplace operators on neural surfaces. Together\, these examples highlight that geometry continues to offer powerful tools\, perhaps more so now than ever. For more details\, please visit https://geometry.cs.ucl.ac.uk/. \nBio:\nNiloy J. Mitra leads the Smart Geometry Processing group in the Department of Computer Science at University College London and the Adobe Research London Lab. He received his PhD from Stanford University under the guidance of Leonidas Guibas. He was an assistant professor at IIT Delhi from 2007-2009. His research focuses on developing machine learning frameworks for generative models for high-quality geometric and appearance content for CG applications. He was awarded the Eurographics Outstanding Technical Contributions Award in 2019\, the British Computer Society Roger Needham Award in 2015\, and the ACM SIGGRAPH Significant New Researcher Award in 2013. He was elected as a fellow of Eurographics in 2021 and served as the Technical Papers Chair for SIGGRAPH in 2022. His work has also earned him a place in the SIGGRAPH Academy in 2023. Besides research\, Niloy is an active DIYer and loves reading\, cricket\, and cooking. For more\, please visit https://geometry.cs.ucl.ac.uk
URL:https://homecse.iitd.ac.in/event/thinking-geometrically-in-the-world-of-data-driven-models-by-niloy-j-mitra/
LOCATION:Bharti 501\, IIT Campus\, Hauz Khas\, New Delhi
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=Asia/Kolkata:20250609T120000
DTEND;TZID=Asia/Kolkata:20250609T130000
DTSTAMP:20261011T032228
CREATED:20250604T065636Z
LAST-MODIFIED:20250604T065657Z
UID:1639-1749470400-1749474000@homecse.iitd.ac.in
SUMMARY:Designing advanced cryptographic primitives in distributed settings
DESCRIPTION:Speaker: Dr. Anshu Yadav\, Postdoctoral Researcher\, Institute of Science and Technology\, Austria \nAbstract: In today’s world\, the rapid advancement of technology has led to the generation of vast amounts of sensitive data\, which must be accessed in a secure and controlled manner to facilitate research across various domains. Often this data\, associated with a single logical entity\, is generated in a distributed manner\, yet must be protected with the same level of security as if it were produced by a single source. Furthermore\, distributing authority among multiple entities is essential to avoid a single point of security failure. These are natural\, yet complex challenges in modern cryptography. My research focuses on exploring how advanced cryptographic primitives can provide effective solutions to such problems. In this talk\, I will begin with a brief overview of my research interests and profile. I will then focus on the themes discussed above. In particular\, I will briefly talk about a result on multi-input attribute based encryption which is a generalization of attribute-based encryption (ABE) – a novel encryption paradigm enabling expressive access control on encrypted data. In ABE\, a message m is encrypted under an attribute x\, and decryption keys are associated with a policy 𝑓. Decryption is possible if and only if 𝑓(x)=1\, unlike traditional public key encryption scheme where a single key can decrypt all ciphertexts. In the multi-input setting\, data is generated by k non-interacting parties\, with each party contributing an input (x_i\, m_i)\, so that x=(x_1\,…\,x_k) and m=(m_1\,…\,m_k). The function 𝑓 is now a k-ary predicate. The goal is for each party to independently encrypt their data as ct_1\,…\,ct_k\, and for a decryption algorithm with key sk_f to recover (m_1\,…\,m_k) if and only if 𝑓(x_1\,…\,x_k)=1. We formally defined the notion of multi-input ABE (k-ABE) and presented constructions for different k under different cryptographic hardness assumptions. I will describe the key challenges in designing cryptographic schemes in the multi-input setting and how our work addresses these challenges. If time permits\, I will also briefly talk about my work in threshold cryptography – a very useful and active field of cryptography with advanced practical applications in distributed environments (e.g.\, block chains\, distributed key generation\, etc.). In threshold cryptography\, a privileged operations—such as ‘signing’ in digital signature scheme or ‘decryption’ in an encryption scheme—is distributed among n parties\, ensuring that at least a threshold t of them are required to perform the operation. In this area\, my research has mostly focussed on threshold signatures\, where we improve the security of post-quantum threshold signature scheme. Finally\, I will conclude the talk with a discussion of my future research directions\, including open problems in the areas discussed above.
URL:https://homecse.iitd.ac.in/event/designing-advanced-cryptographic-primitives-in-distributed-settings/
LOCATION:Bharti 501\, IIT Campus\, Hauz Khas\, New Delhi
CATEGORIES:Seminars
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=Asia/Kolkata:20250605T100000
DTEND;TZID=Asia/Kolkata:20250605T110000
DTSTAMP:20261011T032228
CREATED:20250602T072215Z
LAST-MODIFIED:20250602T094001Z
UID:1633-1749117600-1749121200@homecse.iitd.ac.in
SUMMARY:Multimodal Learning in 3D environments: Perception\, and Simulation
DESCRIPTION:Speaker: Dr. Arun Balajee Vasudevan is currently a Research Scientist at Amazon \nAbstract: \nAutonomous robots have several potential applications such as virtual assistants\, VR/AR\, gaming\, self-driving technologies\, city planning and others. To achieve autonomy\, a robot needs to see and hear the environment\, before it can converse or navigate favorably to perform a human-desired task. Precisely\, scene understanding begins with building 3D geometry\, decoding semantics\, understanding surround objects/humans\, and planning and actions. My research talk addresses these fundamental challenges independently under two broad themes: understanding geometry and multimodal perception & data-driven simulation and navigation. \nUnder geometry and perception\, I introduce the usage of several multimodalities such as the user’s gaze\, visual sensors such as cameras or range sensors (e.g. Kinect)\, and speech/language instructions from human referrals for robot perception tasks. Further\, I delve deep into the investigation of the audio sensing modality for the task using binaural sound microphones. Secondly\, regarding the geometry\, my talk addresses one of my ongoing works about the construction of digital twins of the real world with 4D reconstruction of dynamic scenes from ground visuals of a robot. \nUnder the theme of Data-driven simulation and robot navigation. Following perception\, robots must navigate and take meaningful actions in the world. This involves broadly two aspects: wayfinding and motion planning. Earlier works address wayfinding based on directional instructions\, overlooking human aspects. I briefly talk about a new paradigm that integrates principles from cognitive science with learning-based methods to tackle the challenge of language-based wayfinding for robots in real-world outdoor environments. The second aspect is motion planning for which I propose the learning of driver behavior models for MPC-based planners to build data-driven simulators. \nLong term\, I envision to bridge the above two themes to build multimodal digital twins simulators of the real-world. This potentially helps in training and testing of planners\, VR/AR setups\, gaming\, and others. Lastly\, I also cover my future research plan in the talk. \nShort Bio: \nArun Balajee Vasudevan is currently a Research Scientist at Amazon. Previously\, he was a postdoctoral researcher at Carnegie Mellon University under Prof. Deva Ramanan till March 2025. His core research interest is in Computer Vision and Multimodal Learning. He has works in multimodal (vision\, language and sounds) perception and navigation\, 3D/4D reconstruction\, Motion Planning and improving Foundational models. He published papers predominantly in vision and machine learning conferences/journals such as CVPR\, ECCV\, ICML\, IJCV\, TPAMI\, and others. He defended his PhD under Prof. Luc Van Gool at ETH Zurich. He received his MSc in Computer Science from EPFL in 2016 and an undergraduate degree in Electrical Engineering from the Indian Institute of Technology Jodhpur in the year 2014.
URL:https://homecse.iitd.ac.in/event/multimodal-learning-in-3d-environments-perception-and-simulation/
CATEGORIES:Seminars
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=Asia/Kolkata:20250529T120000
DTEND;TZID=Asia/Kolkata:20250529T130000
DTSTAMP:20261011T032228
CREATED:20250526T171008Z
LAST-MODIFIED:20250526T171008Z
UID:1628-1748520000-1748523600@homecse.iitd.ac.in
SUMMARY:Giving Some Space Can Be Hard: Two New Models to Match Agents with Locations by Shivika Narang
DESCRIPTION:Title: Giving Some Space Can Be Hard: Two New Models to Match Agents with Locations \nSpeaker: Shivika Narang (UNSW Sydney)\n\nAbstract: 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 office spaces. 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 and analyze distance preservation games (DPGs). In DPGs\, agents express ideal distances to other agents and need to choose locations in the unit interval while preserving their ideal distances as closely as possible. We analyze the existence and computation of location profiles that are jump stable (i.e.\, no agent can benefit by moving to another location) or welfare optimal for DPGs\, respectively.\n\nJoint Work with Hadi Hosseini and Tomasz Wąs (Fair Delivery) and Haris Aziz\, Hau Chan\, Patrick Lederer\, and Toby Walsh (DPGs).\n\nSpeaker Bio: 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. Her work is currently focused on fairness and efficiency 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-shivika-narang/
LOCATION:Bharti 501\, IIT Campus\, Hauz Khas\, New Delhi
CATEGORIES:Seminars
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=Asia/Kolkata:20250528T120000
DTEND;TZID=Asia/Kolkata:20250528T130000
DTSTAMP:20261011T032228
CREATED:20250518T062803Z
LAST-MODIFIED:20250526T171039Z
UID:1592-1748433600-1748437200@homecse.iitd.ac.in
SUMMARY:Enhancing Safety and Ethical Alignment in Large Language Models by Rima Hazra
DESCRIPTION:Speaker:  Dr. Rima Hazra \n\n\nAbstract: In this talk\, we explore cutting-edge strategies for enhancing the safety and ethical alignment of large language models (LLMs). The research spans various approaches\, including red teaming and jailbreaking techniques\, which assess and improve model robustness and ethical integrity. We delve into how instruction-centric responses\, when generated by LLMs\, can increase the likelihood of unethical output\, thereby highlighting the vulnerabilities of these AI systems. Through the introduction of frameworks like ‘Safety Arithmetic’ and ‘SafeInfer\,’ we demonstrate methods to mitigate risks by manipulating model parameters and decoding-time behaviors to foster safer interactions. The discussions also emphasize the importance of safety alignment strategies and the challenges posed by integrating new knowledge through model edits\, which can paradoxically destabilize ethical guidelines. This comprehensive examination not only sheds light on the current vulnerabilities of LLMs but also presents a pathway toward more reliable and ethically aligned AI implementations. \n\nBio: Dr. Rima Hazra is a senior postdoc at Eindhoven University of Technology (TU\e)\, Netherlands. Earlier she was a Postdoctoral Researcher at the Singapore University of Technology and Design\, working in the area of AI safety alignment\, natural language processing\, and LLM reasoning. She earned her Ph.D. from the Indian Institute of Technology\, Kharagpur\, where she explored the area of Information retrieval\, NLP and graph learning. With experience in information retrieval\, NLP and graph learning\, Dr. Hazra has published several papers in prestigious CORE A* and A conferences such as AAAI\, ACL\, EMNLP\, NAACL\, ECIR\, ECMLP PKDD and JCDL. She has also received the prestigious Microsoft Academic Partnership Grant (MAPG) and the PaliGemma Academic Program award from Google for her work in AI safety alignment.
URL:https://homecse.iitd.ac.in/event/enhancing-safety-and-ethical-alignment-in-large-language-models-by-dr-rima-hazra/
LOCATION:Bharti 501\, IIT Campus\, Hauz Khas\, New Delhi
CATEGORIES:Seminars
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=Asia/Kolkata:20250523T120000
DTEND;TZID=Asia/Kolkata:20250523T130000
DTSTAMP:20261011T032228
CREATED:20250519T054519Z
LAST-MODIFIED:20250520T083950Z
UID:1599-1748001600-1748005200@homecse.iitd.ac.in
SUMMARY:Next-Generation AI-Enhanced Stream Processing
DESCRIPTION:Speaker: Dr. Manisha Luthra Agnihotri is the Deputy Head of the German Research Center for Artificial Intelligence (DFKI) in Darmstadt. \nIt is an online talk. Please write to the CSE office to get the Teams link. \nAbstract: In this talk\, I will outline my vision for next-generation\, AI-enhanced data management systems through the lens of learned stream processing. Today’s stream processing platforms demand extensive manual tuning to optimize critical decisions such as query plan selection\, operator placement\, and parallelism. My vision eliminates these labor-intensive processes by leveraging zero-shot learning to automatically derive optimal configurations\, thereby radically enhancing performance and generalisability. A key contribution of my work is a novel learned operator placement optimization provided by a novel cost model that forecasts the execution costs of streaming queries on heterogeneous hardware. Particularly in IoT environments—where diverse hardware and network conditions are the norm—our approach employs graph neural networks to predict query costs accurately\, even for unseen placements and query patterns. This approach not only overcomes the generalizability limitations of existing methods but also paves the way for more robust and adaptive cost-based optimizations for stream processing systems. I will also discuss my future research directions\, focusing on extending these AI-driven techniques to multi-modal stream processing. This work aims to redefine data management by creating systems that adapt to evolving computational needs for multiple modalities\, ultimately setting new standards for understanding data inputs and autonomy in stream processing. \nBio: Manisha Luthra Agnihotri is the Deputy Head of the German Research Center for Artificial Intelligence (DFKI) in Darmstadt and a Research Group Leader at TU Darmstadt. She co-leads the Systems AI for Decision Support group with focus of research on learned system optimizations and multimodal data management. Her work sits at the dynamic intersection of machine learning\, data systems\, and hardware\, with major contributions in learned cost-based optimization and the acceleration of query workloads via GPU and RDMA technologies. \nThroughout her academic journey\, Manisha has received several prestigious awards\, including the German national Best Ph.D. Thesis award from the GI/ITG special interest group on Communication and Distributed Systems (KuVS)\, the Athena Young Investigator Award\, the Anita Borg Faculty Scholarship\, the Zeiss Top Dissertation Scholarship\, and mentoring and networking accolades from the German Research Foundation (DFG). Her expertise has led her to speak at top-tier institutions such as the University of Toronto\, and she has presented her innovative research at premier conferences like SIGMOD\, VLDB\, ICDE\, and EDBT. Manisha also actively contributes to the academic community as a program committee member for major data management conferences\, including VLDB\, SIGMOD\, and EuroSys.
URL:https://homecse.iitd.ac.in/event/next-generation-ai-enhanced-stream-processing/
CATEGORIES:Seminars
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=Asia/Kolkata:20250514T100000
DTEND;TZID=Asia/Kolkata:20250514T110000
DTSTAMP:20261011T032228
CREATED:20250512T144512Z
LAST-MODIFIED:20250512T144600Z
UID:1589-1747216800-1747220400@homecse.iitd.ac.in
SUMMARY:Synthesis and Arithmetic of Quantum Circuits
DESCRIPTION:Speaker: Amolak Kalra (https://sites.google.com/view/amolakratankalra/home) \nAbstract: Efficient decomposition of a unitary operator U using words from a universal gate set G is a fundamental problem in quantum computing. The process by which this is achieved is called circuit synthesis. This problem arises naturally in the context of quantum circuit\ncompilation. In this talk\, I will introduce this problem and explain how one can use tools from number theory to solve it. I will then explain some recent results that build on this connection.
URL:https://homecse.iitd.ac.in/event/synthesis-and-arithmetic-of-quantum-circuits/
LOCATION:Bharti 501\, IIT Campus\, Hauz Khas\, New Delhi
CATEGORIES:Seminars
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=Asia/Kolkata:20250508T140000
DTEND;TZID=Asia/Kolkata:20250508T150000
DTSTAMP:20261011T032228
CREATED:20250507T062717Z
LAST-MODIFIED:20250507T063432Z
UID:1581-1746712800-1746716400@homecse.iitd.ac.in
SUMMARY:Trading Prophets: How to trade multiple stocks optimally
DESCRIPTION:Speaker: Surbhi Rajput\, MSR Student\, CSE Dept.\, IIT Delhi \nAbstract:\n\nIn the (single stock) \emph{trading prophet} problem formulated by Correa et\nal.\ [2023]\, an online algorithm observes a sequence of prices of a stock.\nAt each step\, the algorithm can either buy the stock by paying the current\nprice if it doesn't already hold the stock\, or it can sell the currently\nheld stock and collect the current price as a reward. The goal of the\nalgorithm is to maximize its overall profit. Correa et al.\ showed that the\noptimal competitive ratio for this problem is $\nicefrac{1}{2}$ when the\nstock prices are identically and independently distributed.\nIn this talk\, I will discuss the simplifications and generalizations of\nCorrea et al.'s analysis\, which led us to generalize the model by allowing\nthe algorithm to trade multiple stocks. First\, we generalize the model to\n$(k\,\ell\, \ell')$-\textsc{Trading Prophet Problem}\, wherein there are $k$\nstocks in the market\, and the online algorithm can hold up to $\ell$ stocks\nat any time\, where $\ell \leq k$. The online algorithm competes against an\noffline algorithm that can hold at most $\ell' \leq \ell$ stocks at any\ntime. Under the assumption that prices of different stocks are independent\,\nwe show that\, for any $\ell$\, $\ell'$\, and $k$\, the optimal competitive\nratio of $(k\,\ell\, \ell')$-\textsc{Trading Prophet Problem} is\n$\min\left\{\frac{1}{2}\,\frac{\ell}{k}\right\}$.\nWe further generalize it to $\mathcal{M}$-\textsc{Trading Prophet Problem}\nover a matroid $\mathcal{M}$ on the set of $k$ stocks\, wherein the stock\nprices at any given time are possibly correlated (but are independent across\ntime). The algorithm is allowed to hold only a feasible subset of stocks at\nany time. We prove a tight bound of $\frac{1}{1+d}$ on the competitive ratio\nof the $\mathcal{M}$-\textsc{Trading Prophet Problem}\, where $d$ is the\n\textit{density} of the matroid.\nWe then consider the non-i.i.d.\ random order setting over a matroid\,\nwherein stock prices drawn independently from $n$ potentially different\ndistributions are presented in a uniformly random order. In this setting\, we\nachieve a competitive ratio of at least $\frac{1}{1+d} - \mathcal{O}\n\left(\frac{1}{n} \right)$\, where $d$ is the density of the matroid\,\nmatching the hardness result for i.i.d.\ instances as $n$ approaches\n$\infty$.\nOur analysis of the above problems is based on the following key insights.\nFirst\, any algorithm can be simulated by one that\, on each time step\, sells\n\emph{all} its currently held stocks before buying a suitable subset of\nstocks. Second\, we prove that the general problem reduces to a restriction\nwhere the expected price of every stock is zero.\nThird\, we reduce the problem in the random order non-i.i.d.\ setting to the\ni.i.d. setting by leveraging the fact that the outcome of sampling two\nobjects without replacement from a large set is almost identically\ndistributed as the outcome of sampling with replacement.
URL:https://homecse.iitd.ac.in/event/trading-prophets-how-to-trade-multiple-stocks-optimally/
LOCATION:Bharti 501\, IIT Campus\, Hauz Khas\, New Delhi
CATEGORIES:Seminars
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=Asia/Kolkata:20250506T120000
DTEND;TZID=Asia/Kolkata:20250506T130000
DTSTAMP:20261011T032228
CREATED:20250503T125437Z
LAST-MODIFIED:20250503T125437Z
UID:1572-1746532800-1746536400@homecse.iitd.ac.in
SUMMARY:Power and limitations of quantum computation and quantum cryptography by Dr. Srijita Kundu
DESCRIPTION:Title: Power and limitations of quantum computation and quantum cryptography \nSpeaker: Dr. Srijita Kundu \nAbstract: Quantum computers are approaching practical viability\, and quantum cryptography is already being deployed for secure communication. Understanding the capabilities and limitations of these technologies is crucial for their effective use. \nMy research lies at the intersection of quantum complexity theory and cryptography. I focus on proving what quantum computation can and cannot do in concrete models such as query and communication complexity. In this talk\, I will share results in both directions:\n1. I will talk about direct product theorems for quantum communication complexity\, which are a useful lower bound technique for quantum communication protocols.\n2. I will talk about quantum proofs being more powerful than classical proofs in query complexity.\nAdditionally\, I will talk about quantum protocols for novel cryptographic tasks such as certified deletion and uncloneable encryption\, whose security can be proved using the communication direct product theorems. \nShort Bio: Srijita Kundu completed her PhD at the Centre for Quantum Technologies in the National University of Singapore in 2021\, under the supervision of Prof. Rahul Jain. Since 2022\, she has been a postdoctoral researcher at the Institute for Quantum Computing in the University of Waterloo. She is interested in quantum complexity theory and cryptography.
URL:https://homecse.iitd.ac.in/event/power-and-limitations-of-quantum-computation-and-quantum-cryptography-by-dr-srijita-kundu/
LOCATION:Bharti 501\, IIT Campus\, Hauz Khas\, New Delhi
CATEGORIES:Seminars
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=Asia/Kolkata:20250501T110000
DTEND;TZID=Asia/Kolkata:20250501T120000
DTSTAMP:20261011T032228
CREATED:20250425T023119Z
LAST-MODIFIED:20250427T060150Z
UID:1521-1746097200-1746100800@homecse.iitd.ac.in
SUMMARY:Certifying Large Language Models with LLMCert
DESCRIPTION:Speaker: Isha Chaudhary \nAbstract: Large Language Models (LLMs) are increasingly deployed in critical systems\, e.g.\, healthcare and finance and can produce incorrect and biased responses. These can cause huge social and economic losses to the deploying agencies and their clients. Conventional studies are\, however\, insufficient to thoroughly evaluate LLMs\, as they cannot scale to a large number of possible inputs and provide no formal guarantees. Therefore\, we develop and present the first family of LLM certification frameworks\, LLMCert\, consisting of certifiers providing formal probabilistic guarantees for desirable properties such as correct LLM reasoning and fairness on prohibitively large distributions of prompts. Our certificates are quantitative — they consist of provably high-confidence\, tight bounds on the probability of desirable LLM responses for random prompts sampled from a distribution. We design and certify novel specifications for bias and knowledge comprehension in individual certifiers – LLMCert-B (https://certifyllm.com/) and LLMCert-C (https://arxiv.org/abs/2402.15929)\, respectively. We illustrate bias certification for distributions of prompts created by applying varying prefixes drawn from a prefix distribution to a given set of prompts. We consider prefix distributions for random token sequences\, mixtures of manual jailbreaks\, and jailbreaks in the LLM’s embedding space to certify bias. We obtain non-trivial certified bounds on the probability of unbiased responses of SOTA LLMs\, exposing their vulnerabilities over distributions of prompts generated from computationally inexpensive prefix distributions. \nFor knowledge comprehension certification\, we design and use novel distributions of knowledge comprehension prompts with natural noise\, using knowledge graphs. We certify SOTA LLMs over specifications arising in precision medicine and general question-answering. We show previously undiscovered vulnerabilities of SOTA LLMs owing to natural noise in prompts. We also establish the first performance hierarchies with formal guarantees among SOTA LLMs\, pertaining to question-answering in precision medicine. \n  \nBio: Isha Chaudhary is a third-year Ph.D. candidate at the Siebel School of Computing and Data Science\, University of Illinois Urbana-Champaign\, advised by Prof. Gagandeep Singh. Her research interest is broadly in trustworthy foundation models and neural networks for computer systems. She graduated from a B.Tech. in Electrical Engineering from IIT Delhi in 2022. For details about her work\, please check out: https://ishachaudhary.web.illinois.edu/.
URL:https://homecse.iitd.ac.in/event/certifying-large-language-models-with-llmcert/
LOCATION:Bharti 501\, IIT Campus\, Hauz Khas\, New Delhi
CATEGORIES:Seminars
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=Asia/Kolkata:20250429T150000
DTEND;TZID=Asia/Kolkata:20250429T160000
DTSTAMP:20261011T032228
CREATED:20250425T023346Z
LAST-MODIFIED:20250427T060123Z
UID:1523-1745938800-1745942400@homecse.iitd.ac.in
SUMMARY:Computation-In-Memory based Edge-AI for Healthcare: A Cross-Layer Approach
DESCRIPTION:Speaker: Sumit Diware \nAbstract: Recent advancements in artificial intelligence (AI) have driven the emergence of real-world cognitive products and services\, which rely on neural networks to perform complex tasks. Edge computing for AI (edge-AI) combines data sources with local hardware that executes neural network computations\, to improve the response latency\, data privacy/security\, and service reliability. Computation-in-memory (CIM) offers an energy-efficient and compact alternative to conventional neural network hardware for edge-AI\, by enabling in-situ data processing with emerging memory technologies called memristors. Healthcare stands out as a key domain for CIM\, due to its critical impact on society and the need for energy-efficient\, compact hardware in healthcare edge applications. However\, developing AI models for healthcare that are effective\, accurate\, and can fully reap CIM benefits remains a significant challenge. Moreover\, memristors exhibit non-idealities that lead to errors during hardware execution. In this talk\, I will describe our cross-layer research approach and contributions towards addressing these challenges. We first create effective\, accurate\, and CIM-oriented AI models for two healthcare applications: electrocardiogram (ECG) classification and diabetic retinopathy screening. We then devise mitigation strategies against memristor non-idealities and develop a system-on-chip tapeout as a holistic solution that covers the entire abstraction layer stack from application to fabrication. \nShort Bio: Sumit Diware obtained Ph.D. from the Computer Engineering Group at Delft University of Technology (TU Delft)\, Netherlands\, and M.Tech. in VLSI Design Tools and Technology (VDTT) from IIT Delhi. His research focuses on artificial intelligence (AI) processing architectures\, with expertise in computation-in-memory\, neuromorphic computing\, emerging memory technologies\, hardware-algorithm co-design\, and system-on-chip (SoC) design/tapeout. He has authored/co-authored several publications in leading conferences such as DATE\, DAC\, and ICCAD\, as well as IEEE journals including TBioCAS and TETCI. For his doctoral work\, he recently received the European Design & Automation Association (EDAA) Outstanding Dissertation Award at DATE 2025 conference. Before his Ph.D.\, Sumit was a research assistant at the Karlsruhe Institute of Technology (KIT)\, Germany\, where he worked on multicore SoC architectures. Prior to that\, he worked at Qualcomm India as a part of IIT Delhi’s VDTT program\, focusing on SoC power management architecture.
URL:https://homecse.iitd.ac.in/event/computation-in-memory-based-edge-ai-for-healthcare-a-cross-layer-approach/
LOCATION:Bharti 501\, IIT Campus\, Hauz Khas\, New Delhi
CATEGORIES:Seminars
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=Asia/Kolkata:20250428T120000
DTEND;TZID=Asia/Kolkata:20250428T130000
DTSTAMP:20261011T032228
CREATED:20250425T023615Z
LAST-MODIFIED:20250427T060100Z
UID:1526-1745841600-1745845200@homecse.iitd.ac.in
SUMMARY:Algebra and co-  ..in the theory of programming
DESCRIPTION:Speaker: Prof. Sanjiva Prasad \nThe talk explores a few elementary concepts from abstract algebra that (should) inform our data-centric development of common programs and data types\, but are often elided in most textbook treatments.  Included are sets\, monoids\, boolean algebras\, semirings and Kleene algebras\, structure-preserving maps and homomorphisms\, and notions of co-induction.  The talk is intended to be accessible to a general audience. \nThe talk will begin at 12:00 PM\, with refreshments served at 11:50 AM
URL:https://homecse.iitd.ac.in/event/algebra-and-co-in-the-theory-of-programming/
LOCATION:Bharti 501\, IIT Campus\, Hauz Khas\, New Delhi
CATEGORIES:Seminars
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=Asia/Kolkata:20250425T120000
DTEND;TZID=Asia/Kolkata:20250425T170000
DTSTAMP:20261011T032228
CREATED:20250421T081314Z
LAST-MODIFIED:20250425T112605Z
UID:1492-1745582400-1745600400@homecse.iitd.ac.in
SUMMARY:How many matches does it take to find a champion?
DESCRIPTION:Speaker: Neeldhara Misra\n\nAbstract: Suppose there are n horses and we have a track with k lanes. If we pick k horses to run a race\, a linear ordering is established among the chosen horses\, based only on the finishing order (race time is not considered). How many races do we need to organize to determine the best two horses? How many races are necessary to reveal the full ranking?\n \nTo address this question\, we might assume that there is an overall linear ordering among all horses and that the outcomes of the races are always consistent with this global linear order. However\, this may not be true in real-world tournaments: actual outcomes may deviate from our estimate of the global order. In this talk\, we will discuss some developments around questions of determining the “top k” elements in the general setting of tournaments and the special situation when we are promised that comparisons are consistent with an underlying linear order.\n \nThe results presented are drawn from the following papers:\n \nVariations on the Tournament Problem\nFabrizio Luccio\, Linda Pagli\, Nicola Santoro\nFUN 2024\n \nQuery Complexity of Tournament Solutions\nArnab Maiti\, Palash Dey\nTCS 2024\n \nShort bio: Neeldhara Misra is a Smt. Amba and Sri. V S Sastry Chair Associate Professor of Computer Science and Engineering at the Indian Institute of Technology\, Gandhinagar. She completed her PhD from the Institute for Mathematical Sciences in 2012 in Theoretical Computer Science. Her research interests include the design and analysis of algorithms and computational social choice. She is also interested in visualizations and other methods to communicate computational thinking at an elementary level. She also enjoys learning about new card tricks\, especially self-working ones — even though she can’t remember any!\nhttps://www.neeldhara.com/
URL:https://homecse.iitd.ac.in/event/how-many-matches-does-it-take-to-find-a-champion/
LOCATION:Bharti 501\, IIT Campus\, Hauz Khas\, New Delhi
CATEGORIES:Seminars
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BEGIN:VEVENT
DTSTART;TZID=Asia/Kolkata:20250424T120000
DTEND;TZID=Asia/Kolkata:20250424T170000
DTSTAMP:20261011T032228
CREATED:20250421T130049Z
LAST-MODIFIED:20250421T130049Z
UID:1494-1745496000-1745514000@homecse.iitd.ac.in
SUMMARY:Computing Lindahl Equilibrium for Public Goods with and without Funding Caps
DESCRIPTION:Speaker: Dominik Peters \n\nAbstract: Lindahl equilibrium is a solution concept for allocating a fixed budget across several divisible public goods. It always lies in the core\, meaning that the equilibrium allocation satisfies desirable stability and proportional fairness properties. We consider a model where agents have separable linear utility functions over the public goods\, and the output assigns to each good an amount of spending\, summing to at most the available budget.\n\nIn the uncapped setting\, each of the public goods can absorb any amount of funding. In this case\, it is known that Lindahl equilibrium is equivalent to maximizing Nash social welfare. We introduce a new convex programming formulation for computing this solution and show that it is related to Nash welfare maximization through duality and reformulation. We then show that running mirror descent on our new formulation gives rise to a proportional response dynamics\, which converges rapidly to an equilibrium. Our new formulation has similarities to Shmyrev’s convex program for Fisher market equilibrium.\n \nIn the capped setting\, each public good has an upper bound on the amount of funding it can receive. In this setting\, existence of Lindahl equilibrium was only known via fixed-point arguments. The existence of an efficient algorithm computing one has been a long-standing open question. We prove that our new convex program continues to work when the cap constraints are added\, and its optimal solutions are Lindahl equilibria. Thus\, we establish that Lindahl equilibrium can be efficiently computed in the capped setting.\n \nShort bio: Dominik Peters is a CNRS researcher at Université Paris Dauphine – PSL\, working on computational social choice. After postdocs with Ariel Procaccia (Harvard) and Nisarg Shah (Toronto)\, he is studying topics in voting theory with a focus on proportional representation and participatory budgeting\, as well as fair division problems. With his collaborators\, he has proposed the Method of Equal Shares\, a voting method that is now used by several cities across Europe to allow their citizens to influence how the city government spends its budget.\nhttps://dominik-peters.de/
URL:https://homecse.iitd.ac.in/event/computing-lindahl-equilibrium-for-public-goods-with-and-without-funding-caps/
LOCATION:Bharti 501\, IIT Campus\, Hauz Khas\, New Delhi
CATEGORIES:Seminars
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BEGIN:VEVENT
DTSTART;TZID=Asia/Kolkata:20250422T120000
DTEND;TZID=Asia/Kolkata:20250422T130000
DTSTAMP:20261011T032228
CREATED:20250416T065439Z
LAST-MODIFIED:20250422T032206Z
UID:1490-1745323200-1745326800@homecse.iitd.ac.in
SUMMARY:Mathematics and Programming: The Past\, Present and Future A Personal Perspective
DESCRIPTION:Speaker: Dr. Pritam Choudhury \nAbstract: In his article „Constructive Mathematics and Computer Programming‟\, the renowned logician and type-theorist\, Per Martin-Löf notes\, “If programming is understood not as the writing of instructions for this or that computing machine but as the design of methods of computation that it is the computer‟s duty to execute (a difference that Dijkstra has referred to as the difference between computer science and computing science)\, then it no longer seems possible to distinguish the discipline of programming from constructive mathematics.”\nIndeed\, mathematics (constructive) and programming can be viewed as two sides of the same coin via the celebrated Curry-Howard or Propositions-as-Types Correspondence. This correspondence has been of great value to both mathematics and programming. On one side\, it enabled mechanization of mathematics and consequently\, production of machine-certified proofs of mathematical theorems. On the other side\, it provided a solid mathematical foundation for programming languages and guided their development.\nIn this talk\, I shall first introduce the Curry-Howard Correspondence through examples and then present some of its technical details. We shall start with a key result\, which states that the Simply-Typed λ-calculus\, a foundational functional programming language\, is nothing but intuitionistic/constructive propositional logic. Thereafter\, I shall touch upon multiple other similar correspondences\, all manifestations of the overarching Curry-Howard Correspondence\, and discuss how these correspondences guided the development of programming languages. Then\, I shall present some of my research work on extending the Curry-Howard Correspondence in the area of dependency analysis over the Simply-Typed λ- calculus (https://dl.acm.org/doi/10.1145/3563335). Note that dependency analysis is vital to several applications\, such as\, language-based security\, multi-stage compilation\, code optimization\, etc. Finally\, I shall wind up the talk discussing some of my ongoing and future research projects that leverage the close connection between mathematics and programming for their mutual benefit. \nBio: Pritam Choudhury is a researcher in type systems and programming language design. His research focuses on graded type systems and their applications. In his doctoral dissertation\, he used graded type systems to analyze linearity and dependency in programming languages. Linearity and dependency analyses are particularly useful in memory management\, language-based security\, multi-stage compilation and code optimization. Pritam completed his PhD at University of Pennsylvania in August 2023. After graduating from UPenn\, he taught as a Visiting Assistant Professor of Computer Science at Haverford College for a year. Before joining UPenn\, he worked on formal verification at University of Cambridge. Pritam received his M.Phil. in Advanced Computer Science from University of Cambridge in 2015 and his B.Tech. in Electrical Engineering from IIT Roorkee in 2014.
URL:https://homecse.iitd.ac.in/event/mathematics-and-programming-the-past-present-and-future-a-personal-perspective/
LOCATION:Bharti 501\, IIT Campus\, Hauz Khas\, New Delhi
CATEGORIES:Seminars
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=Asia/Kolkata:20250422T110000
DTEND;TZID=Asia/Kolkata:20250422T120000
DTSTAMP:20261011T032228
CREATED:20250416T031907Z
LAST-MODIFIED:20250416T031907Z
UID:1487-1745319600-1745323200@homecse.iitd.ac.in
SUMMARY:Impact Assessment of Natural Resource Management (NRM) Interventions in India
DESCRIPTION:Speaker: Ramneek Kaur\, post-doctoral fellow\, CSE\, IIT Delhi \nAbstract: With over 70% of India’s rural population dependent on agriculture\, and 82% of farmers being small and marginal\, the availability of water for irrigation is critical to ensuring sustainable rural livelihoods. Government welfare schemes such as the Mahatma Gandhi National Rural Employment Guarantee Act (MGNREGA) play a pivotal role in this context by funding the creation of Natural Resource Management (NRM) assets in rural areas for the conservation and sustainable use of water. In this talk\, I will present methods and insights from our ongoing research on evaluating the impact of NRM structures built under MGNREGA – one of the world’s largest demand-driven employment and asset creation programs. The scheme supports the creation of assets under different NRM categories such as bunds\, check dams and trenches for groundwater recharge\, and farm ponds and wells for protective irrigation. We assess the effectiveness of these interventions at both the site-level and the broader landscape-level\, using satellite imagery and other secondary data sources. In our work on impact assessment of farm ponds\, we evaluate the impact of farm ponds in their surrounding cropping areas\, on the seasonal agricultural productivity and drought resilience across the Agro-ecological Zones (AEZs) of India\, by employing the Difference-in-Differences (DiD) method and the Double ML method. Our findings show that the average impact of farm ponds is shaped by contextual AEZ-level factors such as agricultural suitability\, private borewell investments\, and canal infrastructure. Building on this\, we are currently evaluating the impact of check dams using similar techniques. At the landscape scale\, we are developing a system dynamics based framework to capture the cumulative effects of multiple MGNREGA interventions and water bodies. This approach helps model causal pathways and interdependencies between various subsystems using data-driven models\, allowing for a deeper understanding of how NRM activities influence water security and agricultural sustainability. Our methods hold potential for broader applications\, including assessing the impact of NRM efforts on forest conservation\, carbon and water credits\, and other ecosystem services. \n  \nSpeaker bio: Dr. Ramneek Kaur is a postdoctoral fellow with the ACT4D research group at IIT Delhi\, where she has been working for the past two years on using technology to strengthen resilient rural livelihoods. Her research focuses on the impact evaluation of Natural Resource Management (NRM) interventions\, particularly the construction and maintenance of water structures in rural areas. Her work leverages satellite imagery and computational methods to evaluate the effectiveness of these interventions in enhancing agricultural productivity and drought resilience\, for the broader aim of informing strategies for sustainable agricultural practices. Prior to this\, she completed her Ph.D. from IIIT-Delhi in 2022\, where her research focused on developing navigation and task allocation algorithms to promote sustainability in urban transportation systems. Her broader research interests lie in the use of ICT for social development\, with a focus on leveraging computational methods to drive sustainability and equity in both rural and urban contexts.
URL:https://homecse.iitd.ac.in/event/impact-assessment-of-natural-resource-management-nrm-interventions-in-india/
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
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