Leonard Schulman
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.
Leonard Schulman | |
|---|---|
| Born | September 14, 1963 (age 56) 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 |
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 to 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.
