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Allen School professor Yin Tat Lee earns Packard Fellowship to advance the fundamentals of modern computing

Yin Tat Lee, a professor in the Allen School’s Theory of Computation group and visiting researcher at Microsoft Research, has earned a Packard Fellowship for Science and Engineering for his work on faster optimization algorithms that are fundamental to the theory and practice of computing and many other fields, from mathematics and statistics, to economics and operations research. Each year, the David and Lucile Packard Foundation bestows this prestigious recognition upon a small number of early-career scientists and engineers who are at the leading edge of their respective disciplines. Lee is among just 20 researchers nationwide — and one of only two in the Computer & Information Sciences category — to be chosen as members of the 2020 class of fellows. 

“In a year when we are confronted by the devastating impacts of a global pandemic, racial injustice, and climate change, these 20 scientists and engineers offer us a ray of hope for the future,” Frances Arnold, Packard Fellowships Advisory Panel Chair and 2018 Nobel Laureate in Chemistry, said in a press release. “Through their research, creativity, and mentorship to their students and labs, these young leaders will help equip us all to better understand and address the problems we face.”

Lee’s creative approach to addressing fundamental problems in computer science became apparent during his time as a Ph.D. student at MIT, where he earned the George M. Sprowls Award for outstanding doctoral thesis for advancing state-of-the-art solutions to important problems in linear programming, convex programming, and maximum flow. Lee’s philosophy toward research hinges on a departure from the conventional approach taken by many theory researchers, who tend to view problems in continuous optimization and in combinatorial, or discrete, optimization in isolation. Among his earliest successes was a new general interior point method for solving general linear programs that produced the first significant improvement in the running time of linear programming in more than two decades — a development that earned him and his collaborators both the Best Student Paper Award and a Best Paper Award at the IEEE Symposium on Foundations of Computer Science (FOCS 2014). Around that same time, Lee also contributed to a new approximate solution to the maximum flow problem in near-linear time, for which he and the team were recognized with a Best Paper Award at the ACM-SIAM Symposium on Discrete Algorithms (SODA 2014). The following year, Lee and his colleagues once again received a Best Paper Award at FOCS, this time for unveiling a faster cutting plane method for solving convex optimization problems in near-cubic time.

Since his arrival at the University of Washington in 2017, Lee has continued to show his eagerness to apply techniques from one area of theoretical computer science to another in unexpected ways — often to great effect. 

“Even at this early stage in his career, Yin Tat is regarded as a revolutionary figure in convex optimization and its applications in combinatorial optimization and machine learning,” observed his Allen School colleague James Lee. “He often picks up new technical tools as if they were second nature and then applies them in remarkable and unexpected ways. But it’s at least as surprising when he uses standard tools and still manages to break new ground on long-standing open problems!”

One of those problems involved the question of how to optimize non-smooth convex functions in distributed networks to enable the efficient deployment of machine learning applications that rely on massive datasets. Researchers had already made progress in optimizing the trade-offs between computation and communication time for smooth and strongly convex functions in such networks; Lee and his collaborators were the first to extend a similar theoretical analysis to non-smooth convex functions. The outcome was a pair of new algorithms capable of achieving optimal convergence rates for this more challenging class of functions — and yet another Best Paper Award for Lee, this time from the flagship venue for developments in machine learning research, the Conference on Neural Information Processing Systems (NeurIPS 2018).

Since then, Lee’s contributions have included the first algorithm capable of solving dense bipartite matching in nearly linear time, and a new framework for solving linear programs as fast as linear systems for the first time. The latter work incorporates new techniques that are extensible to a broader class of convex optimization problems.

Having earned a reputation as a prolific researcher — he once set a record for the total number of papers from the same author accepted at one of the top theory conferences, the ACM Symposium on Theory of Computing (STOC), in one year — Lee also has received numerous accolades for the quality and impact of his work. These include a Sloan Research Fellowship, a Microsoft Research Faculty Fellowship, a National Science Foundation CAREER Award, and the A.W. Tucker Prize from the Mathematical Optimization Society.

“Convex optimization is the workhorse that powers much of modern machine learning, and therefore, modern computing. Yin Tat is not only a pivotal figure in the theory that underpins our field, but also one of the brightest young stars in all of computer science,” said Magdalena Balazinska, professor and director of the Allen School. “Combined with his boundless curiosity and passion for collaboration, Yin Tat’s depth of knowledge and technical skill hold the promise for many future breakthroughs. We are extremely proud to have him as a member of the Allen School faculty.”

Lee is the fifth Allen School faculty member to be recognized by the Packard Foundation. As one of the largest nongovernmental fellowships in the country supporting science and engineering research, the Packard Fellowship provides $875,000 over five years to each recipient to grant them the freedom and flexibility to pursue big ideas.

Read the Packard Foundation announcement here.

Congratulations, Yin Tat!

October 15, 2020