Getting into graphics
An early interest in computer graphics started Solomon on his journey to become an MIT professor.
As a math-minded high school student growing up in northern Virginia, he had the opportunity to intern at a research lab outside Washington, where he helped to develop algorithms for 3D face recognition.
That experience inspired him to double-major in math and computer science at Stanford University, and he arrived on campus keen to dive into more research projects. He remembers charging into the campus career fair as a first-year and talking his way into a summer internship at Pixar Animation Studios.
“They finally relented and granted me an interview,” he recalls.
He worked at Pixar every summer throughout college and into graduate school. There, he focused on physical simulation of cloth and fluids to improve the realism of animated films, as well as rendering techniques to change the “look” of animated content.
“Graphics is so much fun. It is driven by visual content, but beyond that, it presents unique mathematical challenges that set it apart from other parts of computer science,” Solomon says.
After deciding to launch an academic career, Solomon stayed at Stanford to earn a computer science PhD. As a graduate student, he eventually focused on a problem known as optimal transport, where one seeks to move a distribution of some item to another distribution as efficiently as possible.
For instance, perhaps someone wants to find the cheapest way to ship bags of flour from a collection of manufacturers to a collection of bakeries spread across a city. The farther one ships the flour, the more expensive it is; optimal transport seeks the minimum cost for shipment.
“My focus was originally narrowed to only computer graphics applications of optimal transport, but the research took off in other directions and applications, which was a surprise to me. But, in a way, this coincidence led to the structure of my research group at MIT,” he says.
Solomon says he was attracted to MIT because of the opportunity to work with brilliant students, postdocs, and colleagues on complex, yet practical problems that could have an impact on many disciplines.
Paying it forward
As a faculty member, he is passionate about using his position at MIT to make the field of geometric research accessible to people who aren’t usually exposed to it — especially underserved students who often don’t have the opportunity to conduct research in high school or college.
To that end, Solomon launched the Summer Geometry Initiative, a six-week paid research program for undergraduates, mostly drawn from underrepresented backgrounds. The program, which provides a hands-on introduction to geometry research, completed its third summer in 2023.
“There aren’t many institutions that have someone who works in my field, which can lead to imbalances. It means the typical PhD applicant comes from a restricted set of schools. I’m trying to change that, and to make sure folks who are absolutely brilliant but didn’t have the advantage of being born in the right place still have the opportunity to work in our area,” he says.
The program has gotten real results. Since its launch, Solomon has seen the composition of the incoming classes of PhD students change, not just at MIT, but at other institutions, as well.
Beyond computer graphics, there is a growing list of problems in machine learning and statistics that can be tackled using geometric techniques, which underscores the need for a more diverse field of researchers who bring new ideas and perspectives, he says.
For his part, Solomon is looking forward to applying tools from geometry to improve unsupervised machine learning models. In unsupervised machine learning, models must learn to recognize patterns without having labeled training data.
The vast majority of 3D data are not labeled, and paying humans to hand-label objects in 3D scenes is often prohibitively expensive. But sophisticated models incorporating geometric insight and inference from data can help computers figure out complex, unlabeled 3D scenes, so models can learn from them more effectively.
When Solomon isn’t pondering this and other knotty research quandaries, he can often be found playing classical music on the piano or cello. He’s a fan of composer Dmitri Shostakovich.
An avid musician, he’s made a habit of joining a symphony in whatever city he moves to, and currently plays cello with the New Philharmonia Orchestra in Newton, Massachusetts.
In a way, it’s a harmonious combination of his interests.
“Music is analytical in nature, and I have the advantage of being in a research field — computer graphics — that is very closely connected to artistic practice. So the two are mutually beneficial,” he says.Kindly read our copyright disclaimer here: https://cere-sync.com/dmca-copyrights-disclaimer/