Lixin Zhang, PhD

Assistant Professor of Epidemiology and Biostatistics

Department of Epidemiology and Biostatistics
909 Wilson Road Room B601
East Lansing, MI 48824
517.884.2076 (Lab; B44 Food Safety and Toxicology)

Curriculum Vitae

Dr. Zhang is an Assistant Professor jointly appointed to the Department of Epidemiology & Biostatistics and the Department of Microbiology & Molecular Genetics. Dr. Zhang earned his Bachelor's degree in Engineering studying fermentation and bioremediation, MS degree in Molecular and Cellular Biology, and PhD in Epidemiology. Dr. Zhang joined the Michigan State University in Fall 2014. Prior to this position, Dr. Zhang was a Research Assistant Professor at the University of Michigan School of Public Health.

Dr. Zhang’s general research interests lie in the infectious disease epidemiology, pathogen genomics and bioinformatics. Specifically, he is interested in understanding the emergence, transmission and maintenance of pathogens and antimicrobial resistance in both hospital and community settings by incorporating molecular data into population-level analyses, and in turn, developing effective prevention strategies. Over the years, Dr. Zhang has studied various bacterial pathogens associated with diarrhea, tuberculosis, otitis media, COPD, sepsis, and urinary tract infections.

Dr. Zhang’s current research activities focus on: 1) investigating the transmission patterns of enteric pathogens across remote landscape in rural Ecuador by studying the diversity and genetic structure of E. coli pathogens using high throughput genotyping and whole genome sequencing; 2) examining the impact of agriculture use of antibiotics and likely mode of resistance genes spread between bacteria of human and animal origins; 3) understanding the development of multidrug-resistant, including extended-spectrum beta-lactamase producing, Enterobacteriaceae in the health care settings; and 4) deciphering the genomic and phenotypic factors of bacteria such as Haemophilus influenzae that contribute to their disease causing potential using molecular epidemiological approach.



Liu, R., Li, C., and Lu, Q. Neural-Network Transformation Models for Counting Processes. Statistical Analysis and Data Mining, in press.