Jun S. Liu
From Wikipedia, the free encyclopedia
COPSS' Award (2002)
Morningside Gold Medal (2010)
Pao-Lu Hsu Award (2016)
Jerome Sacks Award (2017)
Mitchell Prize (2000)
IMS Medallion Lecture (2002)
Bernoulli Lecture (2004)
IMS Fellow (2004)
ASA Fellow (2005)
ISCB Fellow (2022)
Jun Liu | |
|---|---|
| Born | April 26, 1965 |
| Education | Peking University (BS) Rutgers University University of Chicago (PhD) |
| Awards | NSF CAREER Award (1995) COPSS' Award (2002) Morningside Gold Medal (2010) Pao-Lu Hsu Award (2016) Jerome Sacks Award (2017) Mitchell Prize (2000) IMS Medallion Lecture (2002) Bernoulli Lecture (2004) IMS Fellow (2004) ASA Fellow (2005) ISCB Fellow (2022) |
| Scientific career | |
| Fields | Statistical Machine Learning Monte Carlo Methods Bayesian statistics Computational biology High-dimensional statistics[1] |
| Institutions | Harvard University Stanford University Tsinghua University |
| Thesis | Correlation Structure and Convergence Rate of the Gibbs Sampler (1986) |
| Doctoral advisor | Wing Hung Wong Augustine Kong[2] |
| Doctoral students | |
| Website | www |
Jun S. Liu (Chinese: 刘军; pinyin: Liú Jūn; born 1965) is a Chinese-American statistician focusing on Bayesian statistical inference, statistical machine learning, and computational biology.[4] He was assistant professor of statistics at Harvard University from 1991 to 1994. From 1994 to 2004, he was Assistant, Associate, and full Professor of Statistics (promoted while being on leave) at Stanford University. In 2000, Liu returned to Harvard as Professor of Statistics in the Department of Statistics and also held a courtesy appointment at Harvard T.H. Chan School of Public Health. In September 2025, Liu left the United States and moved to Tsinghua University for a full-time appointment.[5]
Liu has written many research papers and a book[6] about Markov chain Monte Carlo algorithms, including their applications in biology. He is also co-author of several early software on biological sequence motif discovery.:[1] MACAW, Gibbs Motif Sampler, BioProspector, Motif regressor, MDScan, Tmod; on genetic data analysis: BLADE, HAPLOTYPER, PL-EM, BEAM; and more recently on, genome structure, gene expression and cell type analysis: HiCNorm, BACH, CLIME, RABIT, CLIC, TIMER, and PhyloAcc.
