I grew up in a small town pulling apart Pentium 4 beige desktop towers in Bhusawal, India, convinced that the right piece of silicon could unlock new worlds. When I finally saved for a GPU in 2009 (NVIDIA 9400 GT), games became experiments: sliders turned into differential equations, frame rates hinted at dynamics, and I drifted toward pure math just to keep up with my own questions about motion and intelligence.
That restlessness pulled me into interdisciplinary labs, where I translated biological physics into software. I began by building chemical computing agents and CUDA-accelerated reaction-kinetics simulators, then explored in-silico DNA computers that implemented AI-like algorithms. During my PhD, I focused on scaling numerical methods and active-matter/living fluids solvers that matched high-resolution experimental observations and hinted why some biological cells adopt spherical shapes. Later, at Harvard CSE-Lab, I shipped GPU-heavy reinforcement learning tooling and continued publishing on living-fluid physics, because building systems kept teaching me more than describing them.
Today I am pointing all of that at one problem: reducing catastrophic risk from biology. The tools that make life easier to engineer also raise the stakes of misuse and accidents, and our defenses have to keep up. I am moving into this field full-time, learning bench work through DNA strand-displacement experiments, and building simulators scored against measured data, to find where a mix of biology, simulation, and AI evaluation helps most. This work began with initial support from the Revival Fund.