· Personal Profile

Current position: Home > Personal Profile

Jing Zhang, Professor at Beijing Advanced Innovation Center for Biomedical Engineering, Beihang University.


Dr. Zhang obtained his bachelor degree in Automatic Control Engineering and his master degree in Computer Application from Harbin University of Science and Technology in 2002 and 2005, respectively. He received his Ph.D in Pattern Recognition and Intelligent Systems from Institute of Automation, Chinese Academy of Sciences, in 2009. He worked as Assistant Professor in 2009 and promoted to be Associate Professor in 2014, in Beijing Institute of Genomics, Chinese Academy of Sciences. In 2014, he was elected to be a member of the Youth Innovation Promotion Association, Chinese Academy of Sciences, and was awarded as “Lujiaxi Young Talent". He also earned  "Outstanding Young Scientists Award, Chinese Academy of Sciences" . In 2015, he moved to New York and worked as Associate Research Scientist with Dr. Antonio Iavarone and Dr. Anna Lasorella at Columbia University. In 2019, he joined the faculty in Beihang University, holding the position of Professor.


Dr. Zhang has been working on developing and applying datamining, machine learning, artificial intelligence methods to investigate the epigenetic dynamics of early embryo development for model animals, and tumorigenesis and prognosis of solid tumors, especially, glioblastoma and clear cell renal cell carcinoma. He has published many peer-reviewed articles in high impact journals such as Cell, Nature, Annals of Oncology, and Neuro-Oncology. His on-going researches focus on new machine learning methodology development and their applications in solid tumors and early embryo development including (1) develop new deep learning methods to understand how the tumor microenvironments influence the progression of solid tumors; (2) apply machine learning methods to whole genome sequencing data such as single-cell sequencing data to investigate the interactions between immune system and tumor cell proliferation; (3) investigate key molecular biological mechanisms identified from high-throughput datamining in vitro and vivo; (4) develop new dataming methods to investigate the interactions between environments and embro development of model animals; (5) construct new computational methods based diagnostic and treatment platforms.