Xin Li

Xin Li, an associate professor of computer science, is known for his research in theoretical computer science, including pseudorandomness, complexity theory, distributed computing, and cryptography. He is a member of the Theory & Programming Languages research group within the Department of Computer Science.

Li’s work focuses on the use of randomness in computation, complexity theory, coding theory, and cryptography. His research has led to a number of breakthroughs in the field of theoretical computer science. For example, his studies on the explicit constructions of randomness extractors have resulted in an almost optimal solution to the long-standing open problem of constructing explicit two-source extractors. His discoveries have contributed to the development of tamper-resilient cryptography and error-correcting codes, as well as the creation of more efficient computing methods using limited resources.

The NSF has funded the bulk of Li’s research, about which he has authored more than 30 combined journal and conference publications. He is the recipient of a Simons Postdoctoral Fellowship, a Lucent Global Science Scholarship, and a 2017 Johns Hopkins Catalyst Award. He also received a 2019 NSF CAREER award, which recognizes early-stage scholars with high levels of excellence and promise. The award supports his project exploring pseudorandom objects and their applications in computer science.

Li earned his BS (2002) and MS (2005) in computer science from Tsinghua University in Beijing. He received his PhD (2011) from the University of Texas at Austin. Prior to joining Johns Hopkins in 2013, he spent two years as a Simons Postdoctoral Fellow at the University of Washington in Seattle and interned at Microsoft Research New England.