Venue: Bhart501 Abstract: There is a growing interest in developing artificial neural networks through the training of large models. But what lies hidden in an overparameterized neural network with random weights? If the distribution is properly scaled, such a network contains a subnetwork that can perform well without ever modifying its weights. The number of… Read More »Can a Bucket of Water Translate? Exploring the Encoding–Decoding Ability of Randomly Initialized Neuro-Symbolic Transformers by Dr. Arghya Pal

Venue: Bharti501 Abstract: A central question in algebraic complexity theory is understanding the behaviour of polynomial computation models under basic algebraic operations. While closure under addition and multiplication holds for most of the standard models like algebraic circuits, closure under factorisation remains subtle. In this talk, we will discuss a new result which proves that… Read More »Non-Closure properties in algebraic complexity by Dr. Prateek Dwivedi

Venue: Bharti501 Abstract: Polynomial Identity Testing (PIT) is the problem of checking whether a given algebraic circuit computes the zero polynomial. The PIT problem has a myriad of applications, such as algorithms for the perfect matching problem, primality testing, and learning algorithms for sparse polynomials. While there are efficient randomized algorithms for PIT, there is… Read More »Rank bounds and Polynomial Identity Testing by Prof. Akash

Venue: Bharti501 Abstract: In the graph stream model of computation, an algorithm processes the edges of an n-vertex input graph in one or more sequential passes while using a memory that is sublinear in the input size. The streaming model poses significant challenges for algorithmically constructing long paths. Many known algorithms that are tasked with… Read More »Constructing Long Paths in Graphs Streams by Chhaya Trehan

Venue: Bhart501 Abstract: Quantum computing is approaching an inflection point. Global investment is scaling up, hardware platforms are maturing - and the software stack is beginning to form. But what exactly is this stack? Who is building it, and what remains to be done? In the first part of this talk, I will give an overview… Read More »Building the Quantum Software Stack – and Verifying It by Prof. Ramanathan S. Thinniyam

Venue: SIT001 Online Link: https://teams.microsoft.com/meet/4492945053702?p=Gz8szja9ebX7NoIitC Abstract: "In an era where AI can synthesize hyper-realistic faces and voices, does the axiom 'seeing is believing' still hold water?" This question strikes at the very foundation of digital trust. Over a two-decade journey in biometrics, contributing to large-scale systems like India's Aadhaar and national security applications, I have watched the challenges evolve… Read More »The End of “Seeing is Believing” – Securing Identity in the Age of Generative AI by Richa Singh

Venue: SIT001 Abstract: Large Language Models (LLMs) demonstrate strong general reasoning ability, yet still suffer from hallucination, limited faithfulness, and a lack of interpretability—especially in knowledge-intensive or domain-specific settings. Knowledge Graphs (KGs), on the other hand, provide structured, explicit, and verifiable representations of facts, but are incomplete and lack linguistic flexibility. This talk presents recent… Read More »Unifying Large Language Models and Knowledge Graphs for Faithful and Interpretable Reasoning by Gholamreza (Reza) Haffari

Venue: Bharti501 Abstract: Pretrained transformers readily adapt to new sequence modeling tasks via zero-shot prompting, but relational domains still lack architectures that transfer across datasets and tasks. The core challenge is the diversity of relational data, with varying heterogeneous schemas, graph structures and functional dependencies. In this talk, I will present the Relational Transformer (RT) architecture,… Read More »Relational Transformer: Toward Zero-Shot Foundation Models for Relational Data by Rishabh Ranjan