Professor Justin Solomon, who leads the Geometric Data Processing group in MIT CSAIL, sees a broadly applicable, versatile toolbox for applied geometry as the solution to these and other problems. To solve growing challenges in shape analysis, his work advances the theory and practice of geometric data processing.
Before joining the MIT faculty as a professor of EECS, Prof. Solomon received a PhD in computer science from Stanford University and worked at Pixar Animation Studios; he also completed postdoctoral research in the Princeton Program in Applied and Computational Mathematics. His textbook Numerical Algorithms covers numerical methods for geometry, graphics, robotics, and other computational areas.
His group aims to widen the scope of applied geometry to benefit anyone using computers to analyze complex shapes, networks, maps, datasets, and other modalities. Central areas of his research include transitioning optimal transport from theory to practice, addressing both theoretical and algorithmic challenges in 3D shape analysis, and developing architectures for learning from geometric data.
Prof. Solomon and his group respond to challenges at the intersection of geometry and computation in a broad range of applications as technology emerges – making sure that robots and autonomous vehicles can navigate their environments safely and reliably, that political redistricting practices are established fairly, that physical systems can be simulated virtually with high fidelity, and that medical diagnoses are responsive to subtle changes in shape.