Educational background

2014-2019 New Jersey Institute of Technology, Ph.D. in Computer Science.

2012-2014 New York University Tandon School of Engineering, M.S. in Computer Science.

2009-2012 Chinese Academy of Science, Graduate student in Biology.

2005-2009 Wuhan University, B.S. in Biology.

Professional experience 

2024-now School of Computer Science, Wuhan University, Professor.

2019-2023 University of Pennsylvania, Children’s Hospital of Philadelphia, PostDoc Fellow.

My research interest is artificial intelligence algorithms for biological big data. Focusing on key computational challenges in biological omics data—including high noise levels, high dimensionality, multi-omics integration, and batch effect variability—I have developed innovative AI algorithms and achieved impactful academic results. My work also applies these advanced algorithms to experimental data mining and analysis, aiding domain experts in addressing significant biomedical challenges. In recent years, I have published over 30 SCI-indexed papers, including 12 first-author research articles in leading international bioinformatics journals such as Nature Methods, Nature Machine Intelligence, Nature Communications, Genome Research, and EBioMedicine. Collaborative contributions have also been published in prestigious journals like Nature Medicine, Cancer Discovery, and PNAS. I serve as an editor for Frontiers in Genetics and BMC Bioinformatics.

Publication list：https://scholar.google.com/citations?view_op=list_works&hl=en&hl=en&user=Ut593V8AAAAJ

Contact: tiantianwhu@163.com .

Selected publications

[1] T. Tian, Z. Wei, X. Chang, Y. Liu, RE Gur, PMA Sleiman, H. Hakonarson, The Long Noncoding RNA Landscape in Amygdala Tissues from Schizophrenia Patients, EBioMedicine (2018) 34, 171181

[2] T. Tian, J. Wan, Q. Song, Z. Wei, Clustering Single-cell RNA-seq Data with A Model-based Deep Learning Approach, Nature Machine Intelligence (2019) 1 (4), 191–198.

[3] T. Tian, J. Zhang, X. Lin, Z. Wei, H. Hakonarson. Model-based Deep Embedding for Constrained Clustering Analysis of Single-cell RNA-seq Data. Nature Communications (2021) 12 (1), 1-12.

[4] T. Tian, C. Zhong, X. Lin, Z. Wei, H. Hakonarson. Complex hierarchical structures in single-cell genomics data unveiled by deep hyperbolic manifold learning. Genome Research (2023) 33 (2), 232-246.

[5] T. Tian, J. Zhang, X. Lin, Z. Wei, H. Hakonarson. Dependency-aware deep generative models for multitasking analysis of spatial omics data. Nature Methods (2024).

[6] F. Tan *, T. Tian* , X. Hou, X. Yu, L. Gu, F. Mafra, B. Gregory, Z. Wei, H. Hakonarson, Elucidation of DNA Methylation on N6 -Adenine with Deep Learning, Nature Machine Intelligence (2020) 2 (8), 466-475. (* co-first authors).

[7] L. Xiang* , T. Tian* , Z. Wei, H. Hakonarson. Clustering of single-cell multi-omics data with a multimodal deep learning method. Nature Communications (2022) 13(1), 1-18. (* co-first authors).

[8] P. Jia*, T. Tian*, Z. Li, Y. Wang, Y. Lin, W. Zeng, Y. Ye, M. He, X. Ni, J. Pan, X. Dong, J. Huang,C. Li, D. Guo, P. Hou. CCDC50 promotes tumor growth through regulation of lysosome homeostasis. EMBO reports (2023) 24 (10), e56948. (*co-first authors)