Â鶹ӳ»­

Assistant Professor, Bioinformatics and Computational Biology

Ye Zheng is an  and a tenure-track assistant professor in Bioinformatics and Computational Biology at MD Anderson. Dr. Zheng received her postdoctoral training at the Fred Hutchinson Cancer Center from both molecular biology and quantitative modeling perspectives, mentored by  and . She has also established close collaborations with  and  to decipher CAR T cell immunotherapy response variations. Before her postdoctoral training, Dr. Zheng received a Ph.D. in statistics from the University of Wisconsin-Madison under the supervision of  , and her dissertation was centered around statistical modelings of three-dimensional chromatin structure (3D genomics) for promoter-enhancer inference.

At MD Anderson, Dr. Zheng leads a quantitative research group dedicated to the development of a statistical modeling and computational pipeline. This pipeline uses bulk and single-cell transcriptomics, proteomics, epigenomics and 3D genomics data to address biological and clinical challenges. Her wet lab specializes in the epigenomic profiling of formalin-fixed, paraffin-embedded (FFPE) samples.

Victor Mak, Ph.D.
Research Investigator

Dr. Victor Mak is a research investigator and manager of the wet lab. With expertise in pathology and cancer research, he oversees laboratory operations and conducts research focusing on epigenetic mechanisms in cancer. His role combines hands-on experimental work with laboratory management responsibilities, ensuring efficient research operations while contributing to the advancement of cancer research through experimental studies.

Yiyang Niu
Research Computational Analyst

Yiyang Niu is a research computational analyst in the Zheng Lab. As a recent statistics graduate from Rice University with expertise in computational methods and data analytics, his journey spans from developing embedded systems at Hikvision, where he engineered user interfaces for ETC devices and implemented underlying transaction protocols, to conducting genomic research at MD Anderson, where he implemented machine learning models for clinical outcome prediction. His technical foundation in C/C++, Python and R, combined with experience in both industry and research environments, drives his aspiration to contribute meaningful solutions in computational biology and data science.

Gloria Song
Research Computational Analyst

Gloria Song is a research computational analyst in the Zheng Lab. With a background in data science, she develops scalable and reproducible workflows for analyzing high-dimensional biological data, with a particular focus on single-cell sequencing. Her research centers on integrating multi-omics data, including scRNA-seq, ATAC-seq and CUT&Tag, to investigate cellular heterogeneity, gene regulation and disease progression. She applies advanced statistical modeling, machine learning and data visualization methods to translate complex datasets into meaningful biological insights.

Her recent projects include dissecting T cell subpopulations and exploring chromatin accessibility dynamics in CAR T therapy and other immunological contexts. By working closely with experimental scientists, she bridges computational analysis and biological interpretation, contributing to a deeper understanding of cancer biology and the development of potential therapeutic strategies.

Tian Le
Research Computational Analyst

Tian Le is a research computational analyst in the Zheng Lab. He develops scalable, reproducible computational frameworks for high-dimensional biological data, especially single-cell multi-omics and cancer genomics. His work combines statistical modeling, machine learning and modern software engineering to build end-to-end data pipelines and tools. He also specializes in data science and large language models (LLMs), using them for literature mining, dataset annotation and hypothesis generation to accelerate discovery and inform potential therapeutic strategies.

Xusheng Ai
Graduate Research Assistant

Xusheng Ai is a graduate research assistant in the Zheng Lab and a Ph.D. candidate at Clemson University. He designs computational frameworks for ultra-sparse single-cell epigenomic data, with an emphasis on scCUT&Tag profiles from CAR T cells. By converting these data into high-resolution tile-by-cell matrices and applying advanced statistical and machine-learning techniques, he reveals biologically meaningful cell clusters. His work links chromatin patterns to CAR-T cell phenotypes, deepening our understanding of cancer biology and guiding the discovery of new therapeutic targets.

Yiying Wu
Graduate Research Assistant

Yiying Wu is a Ph.D. student in biostatistics at Â鶹ӳ»­ Health Science Center. As a graduate research assistant, she works on developing and applying statistical methods and machine learning approaches to analyze single-cell 3D genomics data. Her research focuses on understanding the three-dimensional organization of chromatin structure at the single-cell level and its implications for gene regulation and cellular function.

Zhikang Liu
Postbaccalaureate Student

Zhikang Liu is a postbaccalaureate student working in the Zheng Lab. His research focuses on the analysis of single-cell multiomics data using statistical modeling and machine learning approaches. Through his work, he contributes to the development and application of computational methods to understand complex biological systems at the single-cell level.

Aditya Parmar
Research Intern

Aditya Parmar is an undergraduate at the University of California San Diego studying bioinformatics. He works as a research intern in the Zheng Lab, developing novel computational methods to profile genomic copy number variation. His broader interests include machine learning and computational genomics.