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… 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… 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… 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… 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… 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… 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… 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… Read More »Relational Transformer: Toward Zero-Shot Foundation Models for Relational Data by Rishabh Ranjan