Justin Solomon, an Associate Professor in the MIT Department of Electrical Engineering and Computer Science, is revolutionizing geometry by applying it to myriad cutting-edge fields including machine learning, computer graphics, and artificial intelligence (AI). Drawing on the ancient geometric principles of Greek mathematician Euclid, Solomon explores how geometric structures could be used to understand and analyze complex, large-scale data sets and improve generative AI models.
Half of Solomon’s team, known as the Geometric Data Processing Group, focuses on two- and three-dimensional geometric data issues, such as aligning 3D organ scans in medical imaging, or assisting autonomous vehicles to identify pedestrians using spatial data from LiDAR sensors. The other half focuses on high-dimensional statistical research using geometric tools to improve advanced AI models.
Born and raised in Northern Virginia, Solomon’s fascination with math and computer graphics led him to intern at a research lab where he developed algorithms for 3D face recognition. This experience further fueled his interest, leading to a double major in math and computer science at Stanford University, and summer internships at Pixar Animation Studios. Solomon honed his skills in physical simulation of cloth and fluids and various rendering techniques which all reflect the unique mathematical challenges posed by computer graphics.
Upon deciding to pursue an academic career, Solomon remained at Stanford to obtain a Ph.D. in computer science. His research primarily revolved around optimal transport, a concept involving the most efficient movement from one distribution to another, applicable in many areas like logistics and finance.
Committed to spreading knowledge of geometric research, Solomon founded the Summer Geometry Initiative, a research program geared towards engaging students from underrepresented backgrounds. Since its inception, the initiative has brought about changes in the composition of incoming Ph.D. classes, not only at MIT but at other universities.
The field of geometric research under Solomon’s guidance attracts vibrant and diverse talents, allowing for the exploration of a variety of machine learning and statistics problems with geometric techniques. Solomon’s future goals include using geometric tools to enhance unsupervised machine learning models, models that learn to identify patterns without having labeled training data.
In his personal life, Solomon is a keen musician who plays piano and cello, and firmly believes that music’s analytical nature complements computer graphics research. Solomon takes advantage of the correlation between these fields for mutual benefit. His passion extends to not only exploring the interdisciplinary boundaries of geometric research but also to opening the field to a wide array of talents from diverse backgrounds.
