About MeI am an Assistant Professor of Computer Science at the University of Illinois Chicago. My research focuses on machine learning, with an emphasis on deep generative models, probabilistic modeling, and scientific machine learning. I am particularly interested in developing generative and probabilistic methods that can reason over structured, multimodal, and scientific data, including applications in language modeling, machine translation, multimodal learning, neural PDE solvers, and physics-informed machine learning.My recent work explores how deep generative models can be used beyond data generation, including as priors for scientific problems, tools for uncertainty-aware prediction, and mechanisms for improving the robustness and reliability of machine learning systems. I also study large language model reasoning, interpretability, and model steering, with a focus on improving reliability, safety, and controllability. In education, I investigate how large language models can provide meaningful, educator-guided feedback in computer science learning environments. Before joining UIC, I was a postdoctoral researcher at the University of Massachusetts Amherst, where I worked with Andrew McCallum on structured prediction energy networks. I received my Ph.D. in Computer Science from the University of Oregon, where I worked with Daniel Lowd on tractable probabilistic models and probabilistic circuits. | |
Research InterestDeep Generative ModelsProbabilistic Machine Learning Probabilistic Graphical Models and Probabilistic Circuits Scientific Machine Learning Physics-Informed Machine Learning Large Language Model Reasoning Interpretability and Model Steering Structured Prediction Multimodal Learning AI for Education |  |
