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Xin Li is an Associate Professor in the Department of Computer Science at Johns Hopkins University. His research primarily focuses on theoretical computer science, particularly in areas such as pseudorandomness, complexity theory, distributed computing, and cryptography. Li's significant contributions include studies on explicit constructions of randomness extractors that solved long-standing open problems in the field. His discoveries have greatly advanced the development of tamper-resilient cryptography and efficient computing methods with limited resources. Funded by the National Science Foundation, he has authored over 30 publications in journals and conferences. He has also received prestigious awards, including the Simons Postdoctoral Fellowship, the Lucent Global Science Scholarship, and the 2019 NSF CAREER Award, which recognizes promising early-stage scholars in computer science. Li earned his BS in 2002 and MS in 2005 from Tsinghua University in Beijing, and received his PhD from the University of Texas at Austin in 2011. Prior to joining Johns Hopkins in 2013, he was a Simons Postdoctoral Fellow at the University of Washington in Seattle and interned at Microsoft Research New England.
Johns Hopkins University • Baltimore, MD
Teaching and conducting research in theoretical computer science, focusing on pseudorandomness, complexity theory, distributed computing, and cryptography.
University of Washington • Seattle, WA
Postdoctoral research in theoretical computer science.
Department of Pathology - PhD in Pathobiology. GRE is not required.