معرفی
Pei Hao is a Professor at the Institut Pasteur, Shanghai, Chinese Academy of Sciences, where he leads the Lab for Pathogen Big Data. He currently serves as Principal Investigator at the Shanghai Institute of Immunity and Infection, CAS, focusing on pathogen big data analysis since 2024. Previously, he was Associate Professor at the Shanghai Institutes for Biological Sciences, CAS from 2009 to 2012.
Dr. Hao earned his Ph.D. in Bioinformatics from Fudan University between 2003 and 2008. His research spans pathogen genomics, immune system profiling, and computational biology with particular emphasis on viral pathogens and RNA analysis.
His research interests center on Pathogen Big Data, RNA Sequencing, and Immune System Profiling with applications in viral pathogen analysis, cancer immunotherapy, and CRISPR technology development. His work integrates computational approaches with experimental validation to address fundamental questions in host-pathogen interactions.
Analysis of his recent publications (2019-2025) reveals a strong focus on viral pathogen analysis (particularly SARS-CoV-2), immune cell profiling using single-cell technologies, and cancer immunotherapy development. His work increasingly incorporates machine learning approaches for RNA modification detection and pathogen identification.
Dr. Hao has published extensively in high-impact journals including Nature Communications, Nature, and PLoS Biology, demonstrating consistent research productivity across multiple domains of molecular and computational biology.
He has advised numerous researchers as evidenced by his extensive co-authorship network across immunology, virology, and computational biology projects. His laboratory has secured significant research funding for pathogen genomics and immune profiling projects, though specific grant details are not provided in the available information.
Dr. Hao leads the Lab for Pathogen Big Data at Institut Pasteur, Shanghai, which focuses on developing computational frameworks for pathogen surveillance, immune response characterization, and therapeutic development using large-scale genomic and transcriptomic datasets.
