I develop deep reinforcement learning methods for complex systems design, with particular focus on autonomous microfluidic device optimization. My approach uses CFD simulations as the training environment, where DRL agents learn to propose increasingly effective designs based on fluid dynamics feedback. I'm the author of RLMatrix [https://rlmatrix.com/], a high-performance reinforcement learning framework built for seamless integration with engineering software.