Tyler W. Hughes

I like simulating the physical world with computers and using those simulations to design and optimize useful things. My focus is differentiable simulation: writing physics solvers that can compute not just what a system does, but exactly how it responds to changes, enabling gradient-based optimization over designs with enormous numbers of parameters.

I'm currently Head of Photonics at Flexcompute Inc., where I lead a technical team building AI-driven simulation products. A major part of my focus is on the agentic entrypoints to these tools: we've built an MCP server that exposes our electromagnetic solver to AI agents, and I'm deeply involved in shaping how that gets used to solve real photonic design and optimization problems. I'm drawn to this boundary where AI learns about and reasons over the physical world, both as a way to accelerate scientific work and as a rich domain for fundamental AI research.

Earlier at Flexcompute, I was the principal developer of Tidy3D (330 ★ on GitHub), our open-source Python framework for cloud-based, GPU-accelerated electromagnetic simulation. I was in the weeds on everything: writing solver code, building and maintaining the Python client, and leading community and documentation efforts. My deepest technical contribution was making the entire simulation framework differentiable: users can now differentiate through a full 3D Maxwell's equations solve running on a cloud GPU cluster in a single adjoint pass, compatible with JAX and PyTorch, enabling large-scale gradient-based inverse design with hundreds of thousands of parameters.

Before Flexcompute, I completed my PhD in Applied Physics at Stanford, advised by Prof. Shanhui Fan. My PhD work established some deep connections between physics and computation: I showed that wave physics maps exactly to recurrent neural networks, proposed and experimentally demonstrated backpropagation implemented in light for training optical neural networks, and developed adjoint methods for electromagnetic inverse design.

I studied physics at the University of Michigan and now live in New York City. When I'm not building things I'm usually traveling, cooking, or running around Central Park. Resume here, last updated November 2025.