Ph.D. Statistics, University of California, Los Angeles, 2005

M.S. Biochemistry & Molecular Biology, University of California, Los Angeles, 2004

M.S. Biochemistry & Molecular Biology, Tsinghua University, 2000

B.S. Biological Sciences and Technology, Tsinghua University, 1997

Prof. Tianwei Yu is an Associate Dean and Professor at the School of Data Science, The Chinese University of Hong Kong, Shenzhen since March 2020. Prof. Tianwei Yu graduated at the Biology Department of Tsinghua University in 1997, obtained his master’s degree of Biochemistry & Molecular Biology in 2000 at Tsinghua University and another master’s degree of Biochemistry & Molecular Biology in 2004 at University of California, Los Angeles, then earned his Ph.D. of statistics at University of California, Los Angeles in 2005. From 2006 till Feb 2020, Yu taught in the Department of Biostatistics and Bioinformatics at Emory University. He was a tenured professor before leaving the post to join CUHK-Shenzhen. Yu’s research is focused on bioinformatics, statistics and machine learning. He is interested in applications in metabolomics, pharmacogenomics, and systems biology. In his collaborative research, he works on environmental health, virology/vaccinology, nutrition, and cancer. 

Prof. Tianwei Yu is an Editorial Board Member of Scientific Reports, and Biology. He was former Associate Editor for Current Metabolomics and Systems Biology, and Frontiers in Genetics (Bioinformatics section). In 2021, he won the second prize (participation) for China‘s Ministry of Education Prize for Excellent Achievement in Scientific Research. The students he directed have won David P. Byar Young Investigator Travel Award and ENAR Distinguished Student Paper Award.

Recent representative publications

1. Cai Q*, Fu Y*, Lyu C*, Wang Z, Rao S, Alvarez JA, Bai Y, Kang J#, Yu T#. (2024) A New Framework for Exploratory Network Mediator Analysis in Omics Data. Genome Research. (Accepted)

2. Ma G, Kang J#, Yu T#. (2024) Bayesian Functional Analysis for Untargeted Metabolomics Data with Matching Uncertainty and Small Sample Sizes. Briefings in Bioinformatics. 25(3), bbae141.

3. Tian L, Yu T (2023) An integrated deep learning framework for the interpretation of untargeted metabolomics data. Briefings in Bioinformatics. 24(4):bbad244.

4. Yu T. (2022) AIME: Autoencoder-based integrative multi-omics data embedding that allows for confounder adjustments. PLoS Computational Biology. 18(1):e1009826.

5. Jin Z, Kang J#, Yu T#. (2022) Feature Selection and Classification over the network with Missing Node Observations. Statistics in Medicine. 41(7):1242-1262.

6. Tian L, Li Z, Ma G, Zhang X, Tang Z, Wang S, Kang J, Liang D#, Yu T#. (2022) Metapone: a Bioconductor package for joint pathway testing for untargeted metabolomics data. Bioinformatics.38(14):3662-3664.

7. Kong Y, Yu T (2020) forgeNet: a graph deep neural network model using tree-based ensemble classifiers for feature graph construction. Bioinformatics. 36(11):3507-3515.

8. Fei T, Yu T (2020) scBatch: Batch Effect Correction of RNA-seq Data through Sample Distance Matrix Adjustment. Bioinformatics. 36(10):3115-3123.

9. Kong Y, Yu T (2019) A hypergraph-based method for large-scale dynamic correlation study at the transcriptomic scale. BMC Genomics. 20:397.

10. Yu T (2018) A new dynamic correlation algorithm reveals novel functional aspects in single cell and bulk RNA-seq Data. PLoS Computational Biology. 14(8):e1006391.

11. Kong Y, Yu T (2018) A graph-embedded deep feedforward network for disease outcome classification and feature selection using gene expression data. Bioinformatics. 34(21):3727-3737.

12. Yu T (2018) Nonlinear variable selection with continuous outcome: a fully nonparametric incremental forward stagewise approach. Statistical Analysis and Data Mining. 11(4):188-197.

13. Fei T, Zhang T, Shi W#, Yu T# (2018) Mitigating the adverse impact of batch effects in sample pattern detection. Bioinformatics. 34, 2634-2641.

14. Jin Z, Kang J#, Yu T# (2018) Missing value imputation for LC-MS metabolomics data by incorporating metabolic network and adduct ion relations. Bioinformatics. 34(9):1555-1561.

15. Liao P, Wu H#, Yu T# (2017) ROC Curve Analysis in the Presence of Imperfect Reference Standards. Statistics in Biosciences, 9(1):91-104.