Leonard Schulman

Leonard Schulman
Born September 14, 1963 (1963-09-14) (age 55)
Princeton, New Jersey
Nationality American
Alma mater Massachusetts Institute of Technology
Known for Algorithms, Information Theory, Coding Theory, Quantum Computation
Scientific career
Fields Computer Science, Applied mathematics
Institutions California Institute of Technology
Doctoral advisor Michael Sipser

Leonard J. Y. Schulman (born September 14, 1963) is Professor of Computer Science in the Computing and Mathematical Sciences Department at the California Institute of Technology. He is known for work on algorithms, information theory, coding theory, and quantum computation.

Personal Biography

Schulman is the son of theoretical physicist Lawrence Schulman.

Academic Biography

Schulman studied at the Massachusetts Institute of Technology, where he completed a BS degree in Mathematics in 1988 and a PhD degree in Applied Mathematics in 1992. He was a faculty member in the College of Computing at the Georgia Institute of Technology from 1995-2000 before joining the faculty of the California Institute of Technology in 2000.[1] He serves as the director of the Center for Mathematics of Information[2] at Caltech and also participates in the Institute for Quantum Information and Matter.[3]

Research

Schulman's research centers broadly around algorithms and information. He has made notable contributions to varied areas within this space including clustering, derandomization, quantum information theory, and coding theory. One example, which was named a Computing Reviews "Notable Paper" in 2012, is his work on quantifying the effectiveness of Lloyd-type methods for the k-means problem.[4]

Awards and honors

Schulman received the MIT Bucsela Prize in 1988, an NSF Mathematical Sciences Postdoctoral Fellowship in 1992 and an NSF CAREER award in 1999. His work received the IEEE S.A. Schelkunoff Prize in 2005.[5] He was named the editor-in-chief of the SIAM Journal on Computing in 2013. Schulman was also recognized for the ACM Notable Paper in 2012 and received the UAI Best Paper Award in 2016.

References

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