PNNL

Henry Kvinge received his PhD in mathematics from with University of California – Davis where his research focused on the intersection of representation theory, algebraic combinatorics, and category theory. After two years as a postdoc in the Department of Mathematics at Colorado State University, where he worked on the compressive sensing-based algorithms underlying single-pixel cameras, he joined Pacific Northwest National Laboratory as a senior data scientist. His current work focuses on leveraging ideas from geometry, topology, and abstract algebra to build more robust and adaptive deep learning models and frameworks.

• PhD, Mathematics, University of California – Davis
• BA, Biochemistry, University of Washington
• BS, Mathematics, University of Washington