Develop a scalable hybrid quantum–classical framework for electronic-structure simulation of large and strongly correlated molecular systems using sample-based quantum diagonalization and localized active-space techniques.

Develop a scalable hybrid quantum–classical framework for electronic-structure simulation of large and strongly correlated molecular systems using sample-based quantum diagonalization and localized active-space techniques.

The goal is to explore causal inference in time series and see if foundation models can help in the task.

Integration of profiling into torch.compile workflow with inductor backend, by identifying the following: 1. Which metrics to collect - timing, register access counters, etc 2. Where to insert hooks - Fx graph, Inductor IR, Triton IR, etc 3. Areas

Build notebooks to increase open-source adoption of TSFM models

1. To build an AI-driven evaluation system where AI agents assess and improve the performance of other AI agents. 2. To implement a multi-layer coaching pipeline (Primary Generator → Lightweight Coach → Heavy Coach) for scalable, reliable, self-eval

To produce an embedding model that responds well to user provided instructions

Explore AI-assisted techniques to modernize Assembly programs into high-level language like C. Leverage recent advances in Large Language Models (LLMs) to generate human-readable, maintainable code from legacy systems. Evaluate how intermediate rep

This project proposes the development of AdaptiveGraphMAS, a reinforcement learning-driven framework for autonomous construction and optimization of multi-agent system (MAS) topologies. Inspired by the MasHost paradigm, this research aims to advance