Justin Solomon

Justin Solomon is an associate professor in MIT’s Department of Electrical Engineering and Computer Science and a principal investigator at the Computer Science and Artificial Intelligence Laboratory, where he heads the Geometric Data Processing Group. His group aims to widen the scope of applied geometry in computing to analyze complex shapes, networks, maps, datasets, and other modalities. Areas of focus include transitioning optimal transport from theory to practice, addressing theoretical and algorithmic challenges in 3D shape analysis, and developing architectures for learning from geometric data. 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.

Solomon worked at Pixar Animation Studios and was a postdoc in the Princeton Program in Applied and Computational Mathematics. His textbook Numerical Algorithms covers numerical methods for geometry, graphics, robotics, and other computational areas. He earned a PhD in computer science at Stanford University.

Publications

• Claici, S., Genevay, A., Solomon, J. (2020). Wasserstein Measure Coresets. ArXiv: 1805.07412.
• Monteiller, P., Claici, S., Chien, E., Mirzazadeh, F., Solomon, J., Yurochkin, M. (2019). Alleviating Label Switching with Optimal Transport. Thirty-third Conference on Neural Information Processing Systems (NeurIPS).
• Yurochkin, M., Claici, S., Chien, E., Mirzazadeh, F., Solomon, J. (2019). Hierarchical Optimal Transport for Document Representation. Thirty-third Conference on Neural Information Processing Systems (NeurIPS).
• Frogner, C., Mirzazadeh, F., Solomon, J. (2019). Learning Entropic Wasserstein Embeddings. Seventh International Conference on Learning Representations (ICLR).
• Frogner, C., Claici, S., Chien, E., Solomon, J. (2019). Incorporating Unlabeled Data into Distributionally Robust Learning. ArXiv: 1912.07729.

Media

• December 20, 2019: MIT News, Finding a good read among billions of choices.
• October 21, 2019: MIT News, Deep learning with point clouds.
• October 2, 2019: MIT News, Using algorithms to build a map of the placenta.
• September 27, 2019: MIT News, Using math to blend musical notes seamlessly.