Greater than 2,000 years in the past, the Greek mathematician Euclid, recognized to many as the daddy of geometry, modified the way in which we take into consideration shapes.

Constructing off these historic foundations and millennia of mathematical progress since, Justin Solomon is utilizing fashionable geometric methods to unravel thorny issues that usually appear to have nothing to do with shapes.

As an illustration, maybe a statistician desires to check two datasets to see how utilizing one for coaching and the opposite for testing may affect the efficiency of a machine-learning mannequin.

The contents of those datasets may share some geometric construction relying on how the info are organized in high-dimensional house, explains Solomon, an affiliate professor within the MIT Division of Electrical Engineering and Pc Science (EECS) and a member of the Pc Science and Synthetic Intelligence Laboratory (CSAIL). Evaluating them utilizing geometric instruments can convey perception, for instance, into whether or not the identical mannequin will work on each datasets.

“The language we use to speak about information typically includes distances, similarities, curvature, and form — precisely the sorts of issues that we’ve been speaking about in geometry perpetually. So, geometers have loads to contribute to summary issues in information science,” he says.

The sheer breadth of issues one can clear up utilizing geometric methods is the rationale Solomon gave his Geometric Information Processing Group a “purposefully ambiguous” title.

About half of his group works on issues that contain processing two- and three-dimensional geometric information, like aligning 3D organ scans in medical imaging or enabling autonomous automobiles to determine pedestrians in spatial information gathered by LiDAR sensors.

The remainder conduct high-dimensional statistical analysis utilizing geometric instruments, corresponding to to assemble higher generative AI fashions. For instance, these fashions be taught to create new pictures by sampling from sure components of a dataset full of instance pictures. Mapping that house of pictures is, at its core, a geometrical drawback.

“The algorithms we developed concentrating on functions in laptop animation are virtually instantly related to generative AI and chance duties which can be well-liked as we speak,” Solomon provides.

Entering into graphics

An early curiosity in laptop graphics began Solomon on his journey to turn out to be an MIT professor.

As a math-minded highschool scholar rising up in northern Virginia, he had the chance to intern at a analysis lab outdoors Washington, the place he helped to develop algorithms for 3D face recognition.

That have impressed him to double-major in math and laptop science at Stanford College, and he arrived on campus eager to dive into extra analysis tasks. He remembers charging into the campus profession truthful as a first-year and speaking his approach right into a summer season internship at Pixar Animation Studios.

“They lastly relented and granted me an interview,” he remembers.

He labored at Pixar each summer season all through faculty and into graduate faculty. There, he centered on bodily simulation of material and fluids to enhance the realism of animated movies, in addition to rendering methods to vary the “look” of animated content material.

“Graphics is a lot enjoyable. It’s pushed by visible content material, however past that, it presents distinctive mathematical challenges that set it other than different components of laptop science,” Solomon says.

After deciding to launch an instructional profession, Solomon stayed at Stanford to earn a pc science PhD. As a graduate scholar, he finally centered on an issue often called optimum transport, the place one seeks to maneuver a distribution of some merchandise to a different distribution as effectively as attainable.

As an illustration, maybe somebody desires to search out the most affordable method to ship baggage of flour from a set of producers to a set of bakeries unfold throughout a metropolis. The farther one ships the flour, the costlier it’s; optimum transport seeks the minimal value for cargo.

“My focus was initially narrowed to solely laptop graphics functions of optimum transport, however the analysis took off in different instructions and functions, which was a shock to me. However, in a approach, this coincidence led to the construction of my analysis group at MIT,” he says.

Solomon says he was interested in MIT due to the chance to work with sensible college students, postdocs, and colleagues on complicated, but sensible issues that might have an effect on many disciplines.

Paying it ahead

As a school member, he’s captivated with utilizing his place at MIT to make the sphere of geometric analysis accessible to individuals who aren’t normally uncovered to it — particularly underserved college students who typically don’t have the chance to conduct analysis in highschool or faculty.

To that finish, Solomon launched the Summer time Geometry Initiative, a six-week paid analysis program for undergraduates, principally drawn from underrepresented backgrounds. This system, which gives a hands-on introduction to geometry analysis, accomplished its third summer season in 2023.

“There aren’t many establishments which have somebody who works in my area, which may result in imbalances. It means the standard PhD applicant comes from a restricted set of colleges. I’m making an attempt to vary that, and to ensure people who’re completely sensible however didn’t have the benefit of being born in the appropriate place nonetheless have the chance to work in our space,” he says.

This system has gotten actual outcomes. Since its launch, Solomon has seen the composition of the incoming lessons of PhD college students change, not simply at MIT, however at different establishments, as properly.

Past laptop graphics, there’s a rising checklist of issues in machine studying and statistics that may be tackled utilizing geometric methods, which underscores the necessity for a extra various area of researchers who convey new concepts and views, he says.

For his half, Solomon is wanting ahead to making use of instruments from geometry to enhance unsupervised machine studying fashions. In unsupervised machine studying, fashions should be taught to acknowledge patterns with out having labeled coaching information.

The overwhelming majority of 3D information will not be labeled, and paying people to hand-label objects in 3D scenes is commonly prohibitively costly. However refined fashions incorporating geometric perception and inference from information may also help computer systems determine complicated, unlabeled 3D scenes, so fashions can be taught from them extra successfully. 

When Solomon isn’t pondering this and different knotty analysis quandaries, he can typically be discovered enjoying classical music on the piano or cello. He’s a fan of composer Dmitri Shostakovich.

An avid musician, he’s made a behavior of becoming a member of a symphony in no matter metropolis he strikes to, and presently performs cello with the New Philharmonia Orchestra in Newton, Massachusetts.

In a approach, it’s a harmonious mixture of his pursuits.

“Music is analytical in nature, and I’ve the benefit of being in a analysis area — laptop graphics — that may be very intently linked to creative apply. So the 2 are mutually useful,” he says.