Jun Liu – NAS

Biosketch

Dr. Jun Liu is a Chair Professor of Tsinghua University, and Professor Emeritus of Harvard University. Liu received his BS degree in mathematics in 1985 from Peking University and Ph.D in statistics in 1991 from the University of Chicago. He held Assistant, Associate, and Full professorship at Stanford University from 1994 to 2004, and Full professorship at Harvard University from 2000 to 2025. Liu won the COPSS Presidents’ Award in 2002, the Morningside Gold Medal in Applied Mathematics in 2010, the Pao-Lu Hsu Award by the International Chinese Statistical Association in, and the Jerome Sacks Award in 2017. He was selected as a Medallion Lecturer of Institute of Mathematical Statistics (IMS) in 2002, a Bernoulli Lecturer of the Bernoulli Society in 2004, the Kuwait Lecturer of Cambridge University in 2008, the Ghosh lecturer of Purdue University in 2022, and the Pao-Lu Hsu lecturer of Peking University in 2025. He was elected to Fellow of the IMS in 2004, Fellow of the American Statistical Association in 2005, and Fellow of the International Society of Computational Biology in 2022. Liu has served as the co-editor for the flagship statistics journal JASA from 2011-2014, as associate editor for numerous leading statistical journals, and as a committee chair or member for government grant review panels. He was elected to the National Academy of Sciences of the USA in 2025.

Research Interests

Liu’s research interests are: Bayesian methodologies, bioinformatics and computational biology, statistical machine learning, Monte Carlo methods, statistical foundations, applications, and extensions of artificial intelligence. Liu pioneered sequential Monte Carlo (SMC) and invented novel MCMC techniques. His theoretical and methodological studies on SMC and Markov chain Monte Carlo (MCMC) algorithms have a broad impact in machine learning. Liu and his collaborators introduced the statistical missing data formulation and Gibbs sampling strategies (a form of MCMC methods) for biological sequence analyses in the early 1990s. The resulting algorithms for protein sequence alignments, gene regulation analyses, and genetic studies have been adopted by many researchers. Liu has also led the exploration of novel Bayesian modeling techniques for discovering nonlinear and interactive effects in high-dimensional data and theoretical and methodological advances for sufficient dimension reduction in high-dimensions.