Ke Deng Associate Professor

Research Areas: statistical modeling, statistical computation, bioinformatics, text mining, social network analysis

Office: Room 209-C, Weiqing Building, Tsinghua University


Phone: +86-10-62782453


Email: kdeng@tsinghua.edu.cn


Academic Position
  • Director, Center for Statistical Consulting, Tsinghua University, 03/2017–present.
  • Associate Professor, Center for Statistical Science, Tsinghua University, 12/2016 - present.
  • Assistant Professor, Center for Statistical Science, Tsinghua University, 10/2015 – 12/2016.
  • Deputy Director, Center for Statistical Science, Tsinghua University, 06/2014–present
  • Assistant Professor, Yau Mathematical Sciences Center, Tsinghua University, 09/2013 – 10/2015.
Education
  • D in Statistics, Peking University (2008)
  • S. in Applied Mathematics, Peking University (2003) 
Research and Visiting Experience
  • 10/2011–08/2013 Department of Statistics, Harvard University, Research Associate
  • 10/2011–08/2013 CBDB Project, Harvard University, Statistics Consultant
  • 08/2008–09/2011, Department of Statistics, Harvard University, Postdoc
  • 09/2007–03/2008, Department of Statistics, Harvard University, Visiting Fellow.
  • 07/2006–02/2007, Department of ITEE, ADFA, University of New South Wales, Visiting Fellow .
Research Interests
  • Bayesian Statistics
  • Monte Carlo methods
  • Bioinformatics,
  • Network Tomography
  • Text Analysis
  • Causal Inference
  • Medical Data Analysis
Publications
  • Wu Q., Tang Y., Wang S., Li L., Deng K., Tang G., Liu K., Ding D. and Zhang H. (2020) Developing a statistical model to explain the observed decline of atmospheric mercury. Submitted to Environmental Science & Technology.
  • Wang P., Li Q., Gao Y., Sun N., Liu J.S., Deng K.* and He J.* (2020) Inference of individual-specific miRNA-mRNA interactions with a random contact model. Submitted to Briefings of Bioinformatics. (Co-corresponding author)
  • Yang Y., Ji C. and Deng K.* (2020) Rapid Design of Metamaterials via Multi-target Bayesian Optimization. Submitted to Annals of Applied Statistics.
  • Zhu W., Jiang Y., Liu J.S. and Deng K.* (2019) Extension of the Mallows Model to Differentiate Ranker Qualities and Ranked Entities. Submitted to Journal of the American Statistical Association.
  • Jiang Y., Ji C., Liu J.S. and Deng K.* (2019) Bayesian Sufficient Dimension Reduction via Modeling Joint Distributions. Submitted to Journal of the American Statistical Association.
  • Zhang J., Wang F., Deng K., and Liu J.S. (2019), Bayesian Text Classification and Summarization via A Class-Specified Topic Model. Invited revision by Journal of Machine Learning Research.
  • Teng Y.*, Zhu W., Bi D., Kou Z., Wang T., Deng K.* (2019) Mathematical modeling of Zika virus dynamics reveals protective role of Dengue exposure to subsequent Zika virus infection. Submitted to PLoS (Co-corresponding author)
  • An W., Deng K., and Meng C. (2019) Social Network of Top Chinese Politicians. Technical report.
  • Yang Y., Deng K. and Zhu M. (2019) Multi-level Training and Bayesian Hierarchical Modeling for Economical Hyperparameter Optimization. Technical report.
  • Deng K., Jiang Y., Zhu L., Zhao X. and Liu J.S. (2019) Total-effect test is superfluous for establishing complementary mediation. To appear in Statistica Sinica.
  • Ji C., Liu B., Jiang Y. and Deng K. (2019) Sequential Learning for Dirichlet Process Mixtures. In the Proceedings of the 2nd Symposium on Advances in Approximate Bayesian Inference.
  • Zhu W., Ji C., and Deng K.* (2019) Recent Progress of Approximate Bayesian Computation and Its Applications. Applied Mathematics and Mechanics, 40 (11), 1179-1203.
  • Yang Y., Li Q., Liu Z., Ye F., and Deng K.* (2019) Understanding Traditional Chinese Medicine via Statistical Learning of Expert-Specific Electronic Medical Records. Online published in Quantitative Biology.
  • Zhao X.S., Feng G.C., Liu J.S., and Deng K. (2018) We Once Agreed to Measure Agreement - Redefining Reliability De-justifies Krippendorff’s Alpha. China Media Research, 14 (2), 1-15.
  • Zhang R., Hu M., Zhu Y., Qin Z.S., and Deng K., and Liu J.S. (2018) Inferring Spatial Organization of Individual Topologically Associated Domains via Piecewise Helical Model. To appear in IEEE/ACM Transactions on Computational Biology and Bioinformatics.
  • Miao Z., Deng K., Wang X.W, and Zhang X.G. (2018) DEsingle for Detecting Three Types of Differential Expression in Single-Cell RNA-Seq Data. Bioinformatics, 34(18), 2018, 3223–3224.
  • Sun Z., Wang T., Deng K., Wang X.F., Lafyatis R., Ding Y., Hu M., and Chen W. (2018) DIMM-SC: a Dirichlet Mixture Model for Clustering Droplet-Based Single Cell Transcriptomic Data. Bioinformatics. 34(1), 139-146.
  • Ji C., Yang L., Zhu W., Liu Y., and Deng K. (2018) On Bayesian Inference for Continuous-time Autoregressive Models Without Likelihood. Accepted by The 21st International Conference on Information Fusion.
  • Guo X., He Y., Zhu B., Yang Y., Deng K., Liu R. and Ji C. (2017) Bayesian Uncertainty Quantification for Functional Response. In Uncertainty Quantification and Model Calibration. Edited by Jan Peter Hessling, InTech.
  • Deng K.*, Chen M., Jin F., Jiao Y., Cong L., Luo J., and Yin J. (2016) Statistical Methods for Assessing Safety Risk of Imported Foods in China. Journal of Applied Statistics and Management, 35(5), 761-769.
  • Deng K., Bol P.K., Li K.J., and Liu J.S. (2016) On the Unsupervised Analysis of Domain-Specific Chinese texts. Proceedings of the National Academy of Sciences of USA, 113(22), 6154-6159.
  • Zang C, Wang T., Deng K., et al (2016) High-Dimensional Genomic Data Bias Correction and Data Integration Using MANCIE. Online published in Nature Communications. DOI: 10.1038/ncomms11305.
  • Deng K., Li Y., Zhu W., and Liu J.S. (2016) Fast Parameter Estimation in Loss Tomography for Networks of General Topology. Annals of Applied Statistics, 10(1), 144-164.
  • Deng K., Han S., Li J.K., and Liu J.S. (2014) Bayesian Aggregation of Order-Based Rank Data. Journal of the American Statistical Association, 109(507), 1023-1039.
  • Deng K., Geng Z., and Liu J.S. (2014) Association Pattern Discovery via Theme Dictionary Models. Journal of the Royal Statistical Society, Series B, 76(2), 319–347.
  • Hu M., Deng K., Qin Z.S., and Liu J.S. (2013) Understanding spatial organizations of chromosomes via quantitative analysis of Hi-C data. Quantitative Biology, 1(2), 156-174. (Co-first author)
  • Hu M., Deng K., Selvaraj S., Qin Z.S., Ren B., and Liu J.S. (2013) HiCNorm: removing biases in Hi-C data via Poisson regression. Bioinformatics, 28(23), 3131-3133.
  • Hu M., Deng K., Qin Z.S., Dixon J., Selvaraj S., Fang J., Ren B, and Liu J.S. (2013) Bayesian Inference of Spatial Organizations of Chromosomes. PLoS Computational Biology, 9(1): e1002893.
  • Zhu W., Deng K. (2013) Scalable Delay Tomography, International Journal of Digital Content Technology and its Applications, 7(8), 58-67.
  • Zhao X., Liu J.S., and Deng K. (2013) Assumptions behind inter-coder reliability indices. Annals of the International Communication Association, 36(1), 419-480.
  • Deng K., Li Y., Zhu W., Geng Z., and Liu J.S. (2012) On Delay Tomography: Fast Algorithms and Spatially Dependent Models. IEEE Transactions on Signal Processing, 60(11), 5685-5697.
  • He P., Deng K., Liu Z., Liu D., Liu J.S., and Geng Z. (2012) Discovering Herbal Functional Groups of Traditional Chinese Medicine. Statistics in Medicine, 31(7): 636-642. (Co-first author)
  • Zhao, X., Deng K., Feng G.C., Zhu L., and Chan K.C. (2012) Two Liberal-Conservative Hierarchies for Indices of Inter-coder Reliability. 2012 Conference of International Communication Association.
  • Deng K., Ji C., Bol P.K., and Liu J.S. (2011) Statistical Models for Mining Chinese Text. In Frontiers of Mathematical Sciences. Edited by Gu B.L., Yau S.T., et al, International Press of Boston, 263-275.
  • Zhu W. and Deng K. (2006) A Top Down Approach to Estimate Network Loss Rate. In Proceedings of The First International Conference on Communications and Networking in China.
  • Deng K., Liu D., Gao S., and Geng Z. (2005) Structural Learning of Graphical Models and its Applications to Traditional Chinese Medicine. Lecture Notes in Artificial Intelligence, 3614 (2), 362-367.
Teaching
  • Graduate courses
  • Advanced Topics in Modern Statistics (I). (2019/Fall)
  • Statistical Consulting. (2017-2019/Spring)
  • Advanced Statistical Computing. (2017-2019/Spring)
  • Experimental Design & Data Analysis. (2014-2015/Spring)
  • Selected Topics in Applied Statistics. (2014-2015/Fall)
  • Applied Statistics. (2014/Fall)
  • Undergraduate courses
  • Introduction to Statistics. (2018/Fall)
  • Graduate & Undergraduate courses
  • Causal Inference & Graphical Models.( 2016/Fall)
  • Bayesian Methods & Computation. ( 2016/ Spring)
Service
  • 03/2019-03/2023 Vice President, Chinese Association of Data Science & Artificial Intelligence
  • 12/2018-12/2022 Vice President, Beijing Biometrics Association
  • 12/2018-12/2022 Vice President, Chinese Association of Youth Statisticians
  • 12/2018-12/2022 Council Member, Beijing Association of Statistics
  • 08/2018 Member of Young Investigator Award Committee for ICSA 2019
  • 01/2018-12/2022 Council Member, Asian Regional Section of International Association of Statistics Computing
  • 11/2017-11/2021 Council Member, Chinese Association of Applied Statistics
  • 04/2017-04/2020 Standing Director, Chinese Association of Environmental Statistics
  • 03/2017-03/2021 President, Chinese Association of Statistics Computing
  • 09/2015-Present Council Member, Beijing Society of Big Data Research
  • 08/2015-08/2020 Vice President, Chinese Association of Artificial Intelligence in Medicine
  • 11/2014-Present Council Member, Chinese Association of Uncertainty Artificial Intelligence
  • 10/2014-04/2018 Council Member, Chinese Society of Probability & Statistics
  • 04/2012-04/2017 Member of Scientific Committee, Shenzhen Key Lab of Data Science & Modeling
Organizing Conference
  • 08/2019 Co-Organizer, Harvard-Tsinghua-NUS Workshop on Foundation of Computational Sciences
  • 11/2018 Co-President, The Join Meeting of IASC-ARS 25th Anniversary Conference & CASC 2nd Annual Conference
  • 10/2018 Co-President, The 12th China Forum on Health Technology Assessment
  • 04/2018 Co-Chair, 2018 Beijing International Forum on Medical Big Data & Health Technology Assessment
  • 11/2017 Co-Chair, 2017 Hangzhou International Statistical Symposium
  • 12/2016 Chair, 2016 Tsinghua Symposium on Statistics & Data Science for Young Scholars
  • 07/2016 PC Member, The 3rd Taihu International Statistics Forum
  • 01/2016 Co-Chair, Harvard-Beida-Tsinghua International Conference on Digitalized Humanity
  • 06/2015 Co-Chair, 2015 Tsinghua Summer Workshop on Modern Statistics
  • 12/2014 Co-Chair, 2014 Tsinghua-Sanya Workshop on Big Data Research
  • 10/2013Co-Chair, Harvard-Radcliffe Workshop on Text Mining in Literary Chinese
Journal Reviewing
  • On the editorial board of various technical journals including: Statistica Sinica (Assoicate Editor), Applied Probability & Statistics, Digitalized Humanity Research
  • Reviewers for various technical journals including: Journal of American Statistics Association, Journal of Machine Learning Research, Statistics in Medicine, Statistica Sinica, Statistics & Its Interface , Statistics & Probability Letters, Statistical Analysis and Data Mining, Computational Statistics and Data Analysis, IEEE/ACM Transactions on Computational Biology and Bioinformatics, Science China, Mathematical Statistics & Management, ACTA Mathematica Sinica
Funding
  • Functional Data Analysis with Complicated Structures. NSFC, 2020-2024, Co-PI.
  • Learning Classic Chinese Literature with Big Data Technology. National Social Science Foundation of China, 2019-2022, Co-PI.
  • Statistical Models for Mining Big Medical Texts. NSFC, 2018-2021, PI.
  • Statistical Models and Inference for Unsupervised Chinese Text Mining. NSFC 2015-2017, PI.