His work is focused on developing a prescriptive foundation for building intelligent systems. This work involves a mix of methods, empiricism, theory, and applications, often concerning deep neural networks, Gaussian processes, large language models, Bayesian methods, uncertainty representation, and scientific applications. He has been EXPO Chair, Tutorial Chair, Workshop Chair, and Senior Area Chair at the main machine learning conferences. He has also received several awards, including the NSF CAREER Award, the Amazon Research Award, and best paper, reviewer, area chair, and dissertation awards. Outside of work, Andrew is a classical pianist.

I wish to understand the foundations of learning and decision making towards developing intelligent systems. My approach forges connections between different disciplines, and is often focused on discovering scientifically interpretable structure in data. I am particularly engaged in building methods for probabilistic deep learning, scalable Gaussian processes, physics-inspired machine learning, AI alignment, kernel learning, and training of deep neural networks. I have applied my work to time
series, vision, spatial statistics, NLP, counterfactual inference, public policy, medicine, and physics.I also believe in open and reproducible research, and have introduced several software libraries.