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 carried me into interdisciplinary labs where I translated biological physics—the equations of life—into code. I started by building chemical computing agents and developing CUDA-accelerated reaction kinetics simulators. On the side, I built in-silico DNA nanomachines implementing AI-like algorithms. My PhD work focused on scaling numerical algorithms and solvers for active matter that matched experimental data and hinted at why biological cells could be spherical. Later I shipped GPU-heavy reinforcement learning tooling at Harvard CSE-Lab and published research on living-fluid physics, because building the system always taught me more than describing it.
Today that philosophy is becoming Functoris. An agentic framework that takes natural language description of the physics and converts it into high-performance simulation kernels. Ultimately this is infrastructure for automated science—letting ideas move from prompts to rigorous experiments without the handoffs that slow discovery today.