I currently work in the Neuro-Symbolic Computing and Intelligence Research Group at SRI International. Before joining SRI I completed my PhD in Computer Science at the University of Massachusetts Amherst in Professor Ben Marlin's Robust & Efficient Machine Learning lab, with a thesis on uncertainty quantification for computer vision under resource constraints. My recent work extends these themes to large language models and agentic systems, working towards more well-calibrated and trustworthy AI. This work has given me broad experience in probabilistic machine learning, including diffusion models, simulation-based inference, and Bayesian post-training of large models.
Working on multi-sensor and distributed AI throughout my PhD has given me strong engineering skills, building on the systems-heavy CS culture during my undergrad at the University of Wisconsin–Madison. I work natively from the command line and have deployed AI across a wide spectrum of hardware — from microcontrollers and edge GPUs to massively parallel research clusters. I can get AI to work in the real world, not just on benchmarks.
