I'm an Associate Professor at Purdue University, jointly appointed in the University Libraries, the School of Information Studies, and the School of Applied and Creative Computing, and affiliated with the Institute for Physical Artificial Intelligence (IPAI) and the Applied AI Research Center. I received my PhD from Purdue in 2021 and returned after five years as an Assistant Professor of Computer Engineering at Rochester Institute of Technology. I am an IEEE Senior Member and listed among the Stanford/Elsevier World's Top 2% Scientists.
My research asks how a model can learn the underlying structure of a specialized system, whether a cell, a material, or a patient, when the available data reveals that structure only partially, and how we can know when to trust what it has learned. One line of work builds domain foundation models and the meta-training methods that let them adapt to new tasks and environments with limited supervision. A second builds world models that capture how a system evolves and how it responds to intervention, so the model can reason about consequences rather than only recognize patterns. Both aim to move a model from fitting data toward understanding the system that generated it. We work mostly on scientific and healthcare problems, where data is specialized, fragmented, and expensive to collect, and where a model has to be reliable before anyone will act on it.
Outside of work, I enjoy traveling and playing tennis (3.0–3.5), and I share my home with two cats, Tiger and Meimei.