Research

Research Philosophy

I believe the next generation of AI will draw deeply from physics, taking inspiration from biological systems not just as metaphors but as computational principles. My research asks: How can we build systems that learn as efficiently as nature, reason about the physical world, and run on hardware that respects thermodynamic limits?

Focus Areas

Spatial Intelligence & Robotics

A new direction in my work. Building AI that understands and reasons about 3D space, spatial perception, embodied reasoning, and the foundations of Physical AI for autonomous systems and robotics.

Bio-Inspired Energy-Efficient Learning & Inference

Traditional deep learning is energy-hungry by design. I worked on alternatives to backpropagation and probabilistic, brain-inspired computing paradigms including binary stochastic neural networks, Boltzmann machines, and learning rules derived from statistical physics that can achieve competitive accuracy at a fraction of the energy cost.

Physics-Inspired AI & Optimization

Physics is not just a domain for AI to learn about. It can be a substrate for computation itself. My work on quantum-inspired optimization and Ising machine solvers demonstrates how physical phenomena can be harnessed for optimization and inference at scales beyond what conventional hardware supports.

Hardware-Algorithm Co-Design

From custom CUDA kernels for MCMC-based optimization to FPGA-based Boltzmann machine accelerators. I have hands-on experience bridging algorithms and silicon.

Experience