Venue: Bharti501 Abstract: Understanding the algebraic complexity class VNP through alternative characterizations is a central theme in algebraic complexity theory, closely tied to the VP vs. VNP problem. While the permanent provides a canonical complete polynomial for VNP, identifying natural and combinatorial variants that lead to new structural insights remains an important challenge.In this talk,… Read More »A new characterization of VNP via colored determinant by Dr. Prasad Chaugule

Venue: Bharti501 Abstract: In a digraph, a dicut is a cut where all the arcs cross in one direction. A dijoin is a subset of arcs that intersects each dicut. Woodall conjectured in 1976 that in every digraph, the minimum size of a dicut equals to the maximum number of disjoint dijoins. By building connections… Read More »Approximately Packing Dijoins Via Nowhere-Zero Flows by Dr. Ravi

Venue: SIT001 Abstract: The development of new reaction methodology could become a tedious task demanding both time and resources. The application of machine learning (ML) approaches for reaction optimization and prediction can make a significant impact on efficient exploration of the high-dimensional chemical space. But the direct adaptation of ML as used in well-developed domains,… Read More »Molecular Machine Learning for Chemical Catalysis by Dr. Sukriti Singh

Venue: Bharti501 Abstract: Modern Internet-scale services must identify and mitigate customer-impacting incidents quickly. Despite the development of many Root Cause Analysis (RCA) algorithms—including recent LLM-assisted solutions—existing approaches struggle with the "long tail" of novel failure modes and the sheer scale of telemetry. In this talk, I argue that the path forward requires a paradigm shift from… Read More »Abstractions for expressive, extensible, and scalable root cause analysis by Vipul Harsh

Venue. Bharti 501 Abstract. We present TESSERA, a pixel-wise foundation model for multi-modal (Sentinel-1/2) earth observation time series that learns robust, label-efficient embeddings.  Our goal with TESSERA is to make manipulating global satellite intelligence as easy as LLMs did for natural language! Towards this we release global, annual, 10m, pixel-wise embeddings together with open weights and… Read More »TESSERA: Programming Petabytes of Earth Observations using Foundation Models by Prof. Anil Madhavapeddy

Teams Link: MS Teams Abstract: The talk will focus on enabling autonomous actors, such as digital agents or robots, to take deliberative actions towards achieving their long horizon goals, in the face of uncertainty and dynamic events. Today, dynamic events or failures often require human intervention, system restarts, retraining, or redesign, for example, when robots… Read More »Integration of Structured Reasoning and Data-driven Learning for Acting and Planning by Dr. Sunandita Patra

Venue: SIT001 Abstract : Developing intelligent negotiation dialogue systems that promote fair and sustainable outcomes is crucial for advancing automated negotiation for social good. Since effective negotiation requires balancing cooperation and competition while maintaining respect, we propose GENTEEL-NEGOTIATOR, a polite negotiation dialogue system for tourism and e-commerce domains. We introduce NEGOCHAT, a tourism negotiation dataset, and enrich it… Read More »Genteel-Negotiator: LLM-enhanced mixture-of-expert-based reinforcement learning approach for polite negotiation dialogue by Dr. Mauajama Firdaus

Venue: Bharti 501 Abstract: Logics and proof theories play a large role in the formal study and analysis of systems, especially for formal verification. The exact shape of the syntax and proof rules involved depend heavily on the systems being modelled and verified. In this talk, we will introduce a logical syntax for communicated messages… Read More »Logical explorations for security theory by Prof. Vaishnavi Sundararajan

Venue: SIT001 Abstract: This talk overviews recent results in the theoretical foundations of adversarially robust machine learning. Modern ML classifiers can fail spectacularly when subject to specially crafted input-perturbations, called adversarial examples. On the other hand, humans are quite robust for several tasks involving vision. Motivated by this contrast, in the first part of this… Read More »Machine Learning under Adversaries: How Structure in Data Helps by Ambar Pal