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DTSTART:20250101T000000
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BEGIN:VEVENT
DTSTART;TZID=Asia/Kolkata:20251007T120000
DTEND;TZID=Asia/Kolkata:20251007T130000
DTSTAMP:20261012T003003
CREATED:20251005T191731Z
LAST-MODIFIED:20251005T191731Z
UID:2082-1759838400-1759842000@homecse.iitd.ac.in
SUMMARY:Amnesiac Flooding and the curious case of a Unique Algorithm by Amitabh Trehan
DESCRIPTION:Venue: Bharti501 \nAbstract: In the field of distributed algorithm design\, it is often standard to abstract the network as an undirected graph with the nodes as vertices and connections as edges. About the simplest process one can imagine on a network/graph is flooding: A node is in possession of a message M which has to be  eventually sent to every node on the graph (this is called achieving broadcast)- the node sends M immediately to all its neighbours and they send to all their neighbours they did not just receive the message from and so on. Clearly\, this achieves broadcast. But\, how to achieve termination? i.e. the copies of the message should not circulate indefinitely. \n\n\nAt the advent of distributed computing\, more than 50 years ago\, a simple solution was devised – keep a copy of M\, and if M is received again\, simply discard this M. However\, this requires memory/state and a stack of earlier received messages. Surprisingly\, we discovered [PODC2019\,STACS2020\,DC2023] that state is unnecessary to achieve terminating broadcast – the same process without any state or memory beyond the immediate receipt (hence\, called Amnesiac Flooding (AF))\, due to some still slightly mysterious properties of simple undirected graphs\, terminates in asymptotically optimal time on every graph.  Intriguingly\, we have recently discovered [DISC2025] that AF is Unique! i.e. under certain reasonable conditions\, AF is the one and only algorithm that achieves terminating broadcast. Are there other examples of Unique algorithms in literature\, and is counting the number of algorithms for solving a problem a concept we can reasonably postulate? \n\n\nAF on Wikipedia: https://en.wikipedia.org/wiki/Amnesiac_flooding \n\nBio: Amitabh Trehan is an associate professor at the department of Computer Science\, Durham University\, where he heads the NESTiD (Network Engineering\, Science\, and Theory in Durham] research group. He did his PhD in Computer Science from the University of New Mexico\, USA\, following a M.Tech. in Computer Applications from the Indian Institute of Technology\, Delhi.
URL:https://homecse.iitd.ac.in/event/amnesiac-flooding-and-the-curious-case-of-a-unique-algorithm-by-amitabh-trehan/
LOCATION:Bharti 501\, IIT Campus\, Hauz Khas\, New Delhi
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DTSTART;TZID=Asia/Kolkata:20251009T120000
DTEND;TZID=Asia/Kolkata:20251009T130000
DTSTAMP:20261012T003003
CREATED:20251007T162143Z
LAST-MODIFIED:20251007T162143Z
UID:2085-1760011200-1760014800@homecse.iitd.ac.in
SUMMARY:Incentives and Information in Algorithmics Economics by Dr. Divyarthi Mohan
DESCRIPTION:Venue: Bharti501/MS Teams \nAbstract: Digital markets and platforms have shaped the algorithmic landscape into a complex ecosystem of strategic\, self-interested entities. This has motivated the study and development of mechanisms or algorithms that are robust to strategic behaviour\, using tools from algorithms\, game theory and economics. Standard assumptions in mechanism design are too strong to capture the informational challenges present in many real scenarios\, from ad auctions where bidders’ values depend on competitors’ private market data\, to resource allocation where there is uncertainty about future demands. In this talk\, I will provide an overview of my recent work that tackles three important challenges—strategic behavior\, interdependence\, and online decision making—going beyond standard assumptions. In particular\, I will focus on my work establishing the first constant-approximation algorithms for prophet and secretary problems with interdependent values. \n  \nBio: Divyarthi Mohan is a postdoctoral researcher in the Faculty of Computing & Data Sciences at Boston University\, hosted by Kira Goldner. Her research broadly lies at the intersection of computer science and economics\, with a focus on algorithmic mechanisms design and the interplay of incentives and information. She obtained her PhD in Computer Science at Princeton University\, advised by Matt Weinberg\, and was previously a postdoctoral fellow at Tel Aviv University hosted by Michal Feldman. Her research has been recognized with the Simons-Berkeley Research Fellowship for Fall 2022\, the class of 2021 Siebel Scholarship\, and 2019 SEAS award for excellence at Princeton University\, and her work was invited to the Highlights Beyond EC 2024.
URL:https://homecse.iitd.ac.in/event/incentives-and-information-in-algorithmics-economics-by-dr-divyarthi-mohan/
LOCATION:Bharti 501\, IIT Campus\, Hauz Khas\, New Delhi
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BEGIN:VEVENT
DTSTART;TZID=Asia/Kolkata:20251013T120000
DTEND;TZID=Asia/Kolkata:20251013T130000
DTSTAMP:20261012T003003
CREATED:20251007T162719Z
LAST-MODIFIED:20251007T162719Z
UID:2088-1760356800-1760360400@homecse.iitd.ac.in
SUMMARY:Frontiers in Boolean Circuit Lower Bounds by Dr. Vaibhav Krishan
DESCRIPTION:Venue: Bharti501 \nAbstract:\nBoolean circuits provide a combinatorial representation of computation\, where the number of gates (the size) represents running time\, and the number of layers (the depth) capture parallel running time.\nThey form a framework for answering fundamental questions such as P vs NP: proving that some NP problem requires circuits of superpolynomial size would separate P from NP. \nWith limited progress on this question for general circuits\, early breakthroughs focused on restricted circuit classes.\nHåstad (STOC `86) proved that constant-depth circuits with AND\, OR\, and NOT gates require exponential size to compute the parity function (which determines whether the sum of inputs is even or odd).\nRazborov (Matematicheskie Zametki `87) and\, independently\, Smolensky (STOC `87)\, extended this to circuits augmented with parity or modular gates (for prime moduli)\, showing that such circuits require exponential size to compute the majority function (which determines whether the sum of inputs is at least half their number). \nFollowing these foundational results\, research has advanced along two principal directions\, though further progress has become increasingly challenging.\nIn this talk\, I will present some of my work contributing new advances at the frontier of both directions. \n______________________________________________________________________________________________________________________________________ \nPart I: Threshold Circuits. \nThe first part of the talk will focus on constant-depth threshold circuits\, circuits that can use majority gates\, or more generally\, threshold gates.\nThreshold gates can be seen as a simple abstraction for neurons\, and threshold circuits were among the earliest models studied to understand the computational power of neural networks.\nThese circuits are quite powerful; for instance\, they can efficiently implement integer arithmetic operations such as exponentiation and square root. \nIn joint work with Bajpai\, Kush\, Limaye\, and Srinivasan\, Algorithmica `21\, we study a generalization of threshold circuits\, called polynomial threshold circuits\, that use polynomial threshold gates.\nA polynomial threshold gate outputs a Boolean value based on the sign of a polynomial evaluated over the inputs.\nWe design an algorithm to count the number of assignments on which a polynomial threshold circuit outputs 1.\nFor any constant depth\, our algorithm runs faster-than-brute-force when the circuit size is slightly superlinear and the degree of each gate is bounded by a constant.\nPrior to our work\, no such algorithm was known even for a single polynomial threshold gate\, except in the special case of degree 2.\nFaster-than-brute-force algorithms are known to imply circuit lower bounds (Williams\, JACM `14)\, although the particular lower bounds implied by our work were already established by Kane\, Kabanets\, and Lu (STOC `17). \nOur work builds on a long line of research initiated by Impagliazzo\, Paturi\, and Saks (SIAM J. Comput. `97)\, who proved a tight lower bound for threshold circuits with a slightly superlinear number of wires computing the parity function.\nTheir core idea\, simplification of threshold circuits under random partial assignments\, has inspired a series of influential results\, leading to average-case lower bounds and satisfiability algorithms (Chen\, Santhanam\, and Srinivasan\, Theory Comput. `18)\, as well as pseudorandom generator constructions (Hatami\, Hoza\, Tal\, and Tell\, FOCS `22).\nEven seemingly small improvements to these results could lead to major breakthroughs in circuit complexity (Chen and Tell\, STOC `19)\, marking a central frontier for the community. \n______________________________________________________________________________________________________________________________________ \nPart II: Modular Circuits \nThe second part of my talk will focus on circuits with modular gates for general (not necessarily prime) moduli.\nHere\, in a joint work with S. Vishwanathan\, we develop an approach toward resolving a long-standing conjecture (Barrington\, JCSS `89)\, that constant-depth circuits with modular gates (the modulus does not grow with input size) require superpolynomial size to compute the majority function.\nThe classical lower-bound techniques of Håstad and Razborov-Smolensky fail to extend to this setting\, while the algorithms-to-lower bounds framework of Williams (JACM `14) does not apply to simple functions such as majority\, making new ideas necessary. \nVarious approaches to this conjecture have been proposed over the years.\nIn earlier work (Krishan\, CSR 2019)\, I showed that torus polynomials provide the most refined framework for tackling this problem.\nTorus polynomials were introduced by Bhrushundi\, Hosseini\, Lovett\, and Rao (ITCS `19) for proving lower bounds against such modular circuits.\nUsing torus polynomials\, we translate the lower bound conjecture to the task of finding feasible solutions to an infinite family of linear programs.\nThis reformulation allows for incremental progress\, by finding solutions for progressively larger sets from the family. \nWe find solutions for almost all of these programs\, leaving only a finite set unresolved.\nFinding feasible solutions for the remaining cases would lead to a resolution of the conjecture.\nTo make this task more tractable\, we show that the family of programs has far fewer degrees of freedom than initially expected\, and we describe a potential set of feasible solutions for some of the remaining cases.\nI will conclude the talk with open problems and directions for future progress. \n  \nBio: Vaibhav is a postdoctoral researcher at The Institute of Mathematical Sciences\, Chennai. He completed his Ph.D. at IIT Bombay under the supervision of Prof. Sundar Vishwanathan and Prof. Nutan Limaye. Before beginning his Ph.D.\, he worked as a quantitative researcher and a data scientist for four years.
URL:https://homecse.iitd.ac.in/event/frontiers-in-boolean-circuit-lower-bounds-by-dr-vaibhav-krishan/
LOCATION:Bharti 501\, IIT Campus\, Hauz Khas\, New Delhi
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DTSTART;TZID=Asia/Kolkata:20251014T120000
DTEND;TZID=Asia/Kolkata:20251014T130000
DTSTAMP:20261012T003003
CREATED:20251007T161139Z
LAST-MODIFIED:20251007T161139Z
UID:2079-1760443200-1760446800@homecse.iitd.ac.in
SUMMARY:Expanding the Frontiers of Computer Vision: From Robotics to Wildlife and Beyond by Ilan Shimshoni
DESCRIPTION:Abstract: In my talk I will describe in general how to cooperate with people from various fields of research in computer vision research projects and then describe three research projects that I was involved in in the last few years. \nIn the first project which is the field of archaeology we studied scarabs. Scarabs are seals whose origin is from ancient Egypt (2000 BC) but were also found in Israel. The dataset we obtained from an archaeologist consisted of pairs of a photograph and a drawing of a scarab made by an archaeological artist. We developed models for classifying the scarabs according to their etchings and according to the era when they were produced. The drawings are naturally of higher quality than the photographs. During training the model was fed with the photograph and the drawing and during inference only photographs were given as input\, since they are naturally more common. The algorithm also generated a drawing of the scarab. \nIn the second project\, which is in the field of agriculture\, a camera was placed above a drinking facility for sheep. The facility measures the amount of water the sheep drinks and its weight. A video of each sheep was recorded and its face\, back and legs were detected. The results of all these detections were fed into classifiers and the identity of the sheep was returned as a combination of the results from the single classifiers.  The process was basically automatic without human interaction. This algorithm can be used to monitor the condition of each sheep and report to the farmer if it seems that its medical condition has deteriorated. \nIn the last project\, which is in the field of ecology\, a colony of over a thousand terns on a small island was monitored. The terns fly from Europe to Africa and back and stay for some time on the island in Israel. The colony includes two types of terns: common terns and small terns. The whole island was scanned automatically using two PTZ cameras. Using Yolo the types of terns and whether they are brooding or not were classified. In a second stage the results improved since the actual size of the terns\, their motion pattern and their population statistics were taken into account. The results are very accurate and also include their geographic position on the island. This method can now be used to monitor the colony population over time. \nBio: Ilan Shimshoni has been working in the fields of computer vision\, computer graphics and machine learning for more than thirty years. He has been working on various problems in computer vision and applying them to applications in robotics and computer graphics. In recent years he has also been interested in addressing important problems in other fields which are challenging for researchers in my fields of research. He has been working for example on problems in medical rehabilitation\, geography\, agriculture\, and archaeology. One of my main fields of interest is developing algorithms in computer vision addressing challenges in the study of animals (wildlife\, pets\, and domestic animals). This include automatic detection of pain in cats and rabbits\, emotion in dogs\, and identification of individual sheep on a farm. In the realm of wildlife\, He has been working detecting flocks of birds from weather radars\, and counting terns of two types and identifying whether they are brooding or not. This helps ecologists estimate their number in one of the main places they stop in Israel while migrating from Europe to Africa.
URL:https://homecse.iitd.ac.in/event/expanding-the-frontiers-of-computer-vision-from-robotics-to-wildlife-and-beyond-by-ilan-shimshoni/
LOCATION:SIT 001\, Amar Nath and Shashi Khosla School of Information Technology\, IIT Delhi\, Hauz Khas\, New Delhi 110016\, India\, Delhi\, Delhi\, 110016\, India
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DTSTART;TZID=Asia/Kolkata:20251027T120000
DTEND;TZID=Asia/Kolkata:20251027T130000
DTSTAMP:20261012T003003
CREATED:20251023T103521Z
LAST-MODIFIED:20251024T205651Z
UID:2117-1761566400-1761570000@homecse.iitd.ac.in
SUMMARY:Sketching and Uncertainity: Through the Geometric Lens by Prof. Sujoy
DESCRIPTION:Venue: Bharti501\nAbstract: In many modern applications\, including machine learning\, robotics\, distributed systems\, and network design\, the input\, often represented as points in a finite metric space\, can bve massive in size. Efficient proceesing of such data requires compact representations that preserve the essential structural properties of the underlying space. Metric sketching provides a principled way to achieve this compression. Among the most fundamental sketching primitives are spanners and tree covers\, which capture distance relationships in a concise form. \nIn the first part of the talk\, I will discuss recent advances in geometric sketching. Traditional algorithmic models assume complete knowledge of the input in advance; however\, this assumption often fails in dynamic scenarios where inpiuts evolve over time. In such settings\, algorithms must adapt to changes while maintaining strong performance guarantees. \nIn the second part\, I will explore the dynamic aspects of metric sketching part\, related problems\, and highlight emerging directions that connect geometry with uncertainity.
URL:https://homecse.iitd.ac.in/event/sketching-and-uncertainity-through-the-gerometric-lens-by-prof-sujoy/
LOCATION:Bharti 501\, IIT Campus\, Hauz Khas\, New Delhi
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DTSTART;TZID=Asia/Kolkata:20251028T120000
DTEND;TZID=Asia/Kolkata:20251028T130000
DTSTAMP:20261012T003003
CREATED:20251016T070350Z
LAST-MODIFIED:20251024T203402Z
UID:2115-1761652800-1761656400@homecse.iitd.ac.in
SUMMARY:Adaptive Human-Robot Interaction: Human Inspired Handovers and Robotic Failure Explanations. (An Intersection of Robotics and Machine Learning) by Dr. Parag Khanna
DESCRIPTION:Venue: Bharti-501/MS Teams \nAbstract: \nAs robots become more advanced\, they are expected to be increasingly present among humans\, engaging frequently in physical and social interactions. Among these interactions\, handovers—the transfer of an object from one individual to another—play a vital role in daily life. This talk focuses on my research on enhancing human-robot interaction (HRI) by drawing inspiration from human-human handovers and utilizing handovers to resolve robotic failures by providing explanations for these failures as well as adapting these explanations based on human behavioral responses. \nFor physical interaction\, I present my work on formulating a weight-adaptive robot grip release strategy that determines when to release an object as a human recipient begins to take it and adapts to variations in object weight. I recorded and published datasets of human-human handovers to develop data-driven (LSTM\, VAE-LSTM based) grip release strategies\, which were experimentally validated in user studies. I also present how object weight affects human motion during handovers\, enabling robots to observe changes in human motion to estimate object weights and adapt their motions to convey weight information. Lastly\, I present my research on the use of non-touch modalities\, such as EEG brain signals and gaze tracking\, to discern human intentions during HRI\, differentiating between motions intended for handovers and those that are not. \nFor social interaction\, I explored how different levels of explanation content impact collaborative performance of human-robot teams and human satisfaction. I present my research on explanation variation strategies for repeated failures and adapting explanations by predicting user confusion. I further present a context-specific explanation generation system using behavior tree representation of collaborative tasks combined with Large Language Models (LLMs). This system was implemented as a failure communication module that enabled adapting explanation levels based on user queries and behavior\, effectively improving failure resolution rates for collaborative tasks\, as evaluated in user studies. \nBy this talk\, I aim to demonstrate how human-inspired approaches and machine learning can enhance both physical and social aspects of HRI\, and to outline my future research directions for adaptive HRI. \n  \nBiosketch:\nDr. Parag Khanna is a postdoctoral researcher at the Division of Robotics\, Perception\, and Learning (RPL) at KTH Royal Institute of Technology\, Sweden. As a collaborative roboticist\, his research combines insights from human behavior\, cognitive science\, and robotics to design intuitive and explainable robotic systems. His key research topics include physical and social human-robot interaction (HRI)\, analyzing and learning from human behavior\, data-driven and human-inspired robotic strategies\, and adaptive explanations for robotic failures. He is passionate about bringing robotics from the lab to everyday life through adaptive\, user-centered solutions that make robots more effective\, safer\, and easier to work with in real-world environments. \nHe received his PhD from KTH in 2025\, focusing on human-robot interaction—specifically\, developing adaptive techniques for seamless handovers between robots and humans. He holds dual M.Sc. degrees from the Erasmus Mundus European Masters in Advanced Robotics (EMARO+) program— from École Centrale de Nantes\, France\, and from the University of Genoa\, Italy (2019). He earned his B.Tech. in Mechanical Engineering from Visvesvaraya National Institute of Technology (VNIT)\, Nagpur\, India\, in 2017\, where his bachelor’s thesis on an autonomous snake robot reconfigurable into a quadcopter led to an Indian patent filed in 2017 (granted in 2025). \nFrom 2019 to 2021\, he worked as a research engineer at the French National Center for Scientific Research (CNRS) in Nantes\, France\, designing and controlling a bio-inspired tensegrity manipulator. \nHe has also organized workshops at the IEEE Humanoids conferences (2024–25) and the IEEE IROS 2025 conference\, and serves as a program chair for the HRI Pioneers Workshop at the HRI 2026 conference and as a Associate Editor for the IEEE/SICE System Integration (SII) 2026 conference. \nDr. Khanna’s accomplishments include the Honorable Mention for Best Short Contribution Paper Award and selection as a HRI Pioneer at the ACM/IEEE HRI conference 2025\, Best Poster Awards at KTH EECS Research and Impact Day 2025 and the Human Agent Interaction conference (HAI) 2023\, and representation of VNIT at the National Innovation Club member meeting at the Rashtrapati Bhavan in 2017.
URL:https://homecse.iitd.ac.in/event/adaptive-human-robot-interaction-human-inspired-handovers-and-robotic-failure-explanations-an-intersection-of-robotics-and-machine-learning-by-dr-parag-khanna/
LOCATION:Bharti 501\, IIT Campus\, Hauz Khas\, New Delhi
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DTSTART;TZID=Asia/Kolkata:20251030T110000
DTEND;TZID=Asia/Kolkata:20251030T120000
DTSTAMP:20261012T003003
CREATED:20251028T055031Z
LAST-MODIFIED:20251028T055031Z
UID:2136-1761822000-1761825600@homecse.iitd.ac.in
SUMMARY:Towards Reliable LLM Reasoning: Coordinated Agents\, Variance-Aware Evaluation\, and Lean Inference by Prof. Akhil Arora
DESCRIPTION:Abstract: Large language models (LLMs) are increasingly deployed as reasoning engines\, yet their practical use remains constrained by three persistent challenges: achieving high-quality reasoning at low cost\, measuring performance reliably\, and ensuring efficient\, reproducible deployment. In this talk\, I will present a research agenda addressing these challenges through new methods\, benchmarks\, and systems for practical LLM reasoning. I begin with Next\, I turn to Finally\, I focus on Together\, these contributions chart a path toward LLM reasoning that is not only more powerful\, but also leaner\, more reliable\, and environmentally responsible.\n\n  \n\nBio: Akhil Arora is a Tenure-Track Assistant Professor of Computer Science at Aarhus University\, where he heads the CLAN for AI Research on Language and Networks (or “CLAN” for short). He is a fellow of the Copenhagen Center for Social Data Science (SODAS)\, an affiliate of the Pioneer Centre for AI and ELLIS\, and a formal collaborator of the Wikimedia Foundation\, the non-profit organization that manages Wikipedia and related projects. Akhil’s research lies broadly in human-centered AI with a focus on improving human knowledge-seeking\, bridging knowledge gaps\, and promoting knowledge equity on the Web. To this end\, he devises methods and tools blending techniques from NLP\, AI\, Graph ML\, and Computational social science. Recently\, his group has been devising robust\, trustworthy\, accessible\, and efficient LLM inference strategies.\nAkhil received his PhD in Computer Science from EPFL (2024) in Switzerland\, his MS from IIT Kanpur (2013)\, and his undergraduate degree from NCU Gurgaon (2010). In days of yore\, he spent close to five years in the industry working with the research labs of Xerox and American Express as a Research Scientist. His work on influence maximization has been recognized as the 8th most influential paper of SIGMOD 2017 by Paper Digest and received the 2018 ACM SIGMOD Most Reproducible Paper Award. He is a recipient of the prestigious EDIC Doctoral Fellowship\, an alumnus of the coveted Heidelberg Laureate Forum\, and a DAAD AINet fellow on human-centered AI. Akhil is a director of the P1-programs on
URL:https://homecse.iitd.ac.in/event/towards-reliable-llm-reasoning-coordinated-agents-variance-aware-evaluation-and-lean-inference-by-prof-akhil-arora/
LOCATION:Bharti 501\, IIT Campus\, Hauz Khas\, New Delhi
CATEGORIES:Seminars
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