Mehdi Maadooliat

Dr. Mehdi Maadooliat is an Associate Professor in the Department of Mathematical and Statistical Sciences at Marquette University. His research focuses on statistical machine learning, bioinformatics, functional data analysis, and skewed distribution modeling. He holds a Ph.D. in Statistics from Texas A&M University (2011) and has held postdoctoral positions at Texas A&M and KAUST. He leads the Machine Learning and Bioinformatics Lab (MLAB) and advises multiple Ph.D. students. His work has led to software packages like Rfssa and GWID, and he has been recognized with the Way Klingler Young Scholar Award (2016).

Education:

• Ph.D., Statistics, Texas A&M University, 2011
• M.Sc., Mathematics & Statistics, Marquette University, 2006
• B.Sc., Applied Mathematics, Sharif University of Technology, 2003

Research Interests: Dr. Maadooliat specializes in developing statistical methods for bioinformatics, functional data analysis, and spectral density estimation. His work bridges theory and application, with contributions to protein structure modeling, genome-wide analysis, and medical data science. Recent efforts include functional singular spectrum analysis and regularized multivariate functional PCA.

Grants & Awards:

• National Institute of Health (NIH) R01 grants (2020–2027) for fatigability studies
• Marshfield Clinic Research Institute grant (2018–2020) for genetic analysis
• NVIDIA GPU Grant (2017)

Teaching & Service: Teaches advanced courses in statistical machine learning and computational probability. Served as Graduate Chair and co-director of Bioinformatics programs at Marquette. Editor for Journal of Statistical Theory and Applications and Journal of the Iranian Statistical Society.

Labs & Collaborations: His MLAB develops statistical tools for bioinformatics and functional data analysis. Collaborates with biomedical researchers on projects like aortic coarctation modeling and protein loop prediction.