I am a machine learning scientist with a track record of creating high-impact results in real-world applications across autonomous decision making, video processing, robotics, and energy systems domains. Currently, I work on the ML research team at Nissan, where I lead efforts developing multimodal deep learning and foundation models for autonomous decision-making and personalized user experiences in energy marketplaces.

My research interests include:

• Risk-aware forecasting, optimization, and control: distributional robustness, distributional shifts, reinforcement learning, learning MPC, convex relaxations, and stochastic optimal control.

• Information theory: optimal experiment design, representation learning: disentanglement learning, efficient machine learning, and state estimation.

• Applications: autonomous vehicles, video processing, lithium-ion batteries, electric vehicles, vehicle grid integration, and energy management systems.

I completed my PhD with a National Science Foundation Graduate Fellowship at the Energy, Controls, and Applications Lab (eCal) at UC Berkeley with Professor Scott Moura.  My research focuses on information-theoretic aspects of deep learning, risk-aware reinforcement learning, and scientific machine learning in renewable energy and smart city applications.

I love woodworking and sculpture, and am an avid luthier in my free time.  My portfolio primarily includes electric guitars.  I specialize in building multiscale and extended range musical instruments.

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