Kristofer Reyes is an Assistant Professor in the Department of Materials Design and Innovation, University at Buffalo, and holds a joint appointment as an applied mathematician at Brookhaven National Laboratory. He develops machine learning and artificial intelligence methods for problems in materials science. He is particularly interested in making these methods relevant in the regime of sparse and noisy data – a regime in which much of materials science research is conducted. A major thrust of his work is autonomous science. In this area, he develops methods for designing optimal experiments based on limited information, algorithms for the characterization of rich and complex data resulting from experiments, and techniques for learning and leveraging physics-based and domain-expert knowledge. He received his Ph.D. in applied mathematics from the University of Michigan, where he modeled the synthesis of nanostructures grown by multiphase methods. His postdoctoral training was at the Department of Operations Research and Financial Engineering at Princeton University, where he developed Bayesian models and algorithms for problems in materials science, chemistry, and physics.