Zhiliang Bai

Dr. Zhiliang Bai is an incoming Assistant Professor in the Department of Biological Engineering at MIT and an intramural member of the Koch Institute for Integrative Cancer Research. He earned his Ph.D. in Instrument Science and Technology from Tianjin University in China and completed his postdoctoral research training with Prof. Rong Fan in the Department of Biomedical Engineering at Yale University. His expertise lies in microfluidic device engineering, cancer immunotherapy and spatial omics technology development. His most significant contributions to science and technology include uncovering the critical role of type-2 immunity in sustaining long-term durable immunotherapy and developing Patho-DBiT, a first-of-its-kind technology that enables the exploration of rich RNA biology in clinically archived FFPE tissues. Dr. Bai has received several accolades, including the Joan and Tom Steitz RNA Fellow, SITC Young Investigator Award, FOCIS Clinical Immunology Award, BMES-CMBE Postdoctoral Researcher Award, ASH Abstract Achievement Award, and the Committee of 100 Leadership Scholarship. Beyond the lab, he is passionate about hosting community events, engaging in fitness challenges, and fostering growth through teaching and mentorship.

Before joining MIT, Dr. Bai has designed microfluidic devices for active single-cell trapping (ACS Nano 2020) and high-plex immune-serolomic measurement (Small Methods 2023), leveraged single-cell multi-omics to identify molecular determinants of 8-year leukemia remission after CAR T cell therapy (JITC 2021; Science Advances 2022; Nature 2024), immuno-engineered novel cytokines to boost durable immunotherapy against solid tumors (Nature 2024), and developed a first-of-its-kind spatial whole transcriptome sequencing technology for archival formalin-fixed paraffin-embedded tissues (Cell 2024). Moving forward, the Bai Lab at MIT will integrate spatial multi-omics innovation with RNA biology, digital pathology, and AI to advance research in cancer and aging—translating omics-driven insights into novel diagnostic, preventive, and therapeutic strategies.