Michele Guindani
Short Bio
Dr. Guindani received his Ph.D. degree in Statistics from Università Bocconi (Milan, Italy) in 2005 under the supervision of Sonia Petrone (Department of Decision Sciences, Università Bocconi, Italy) and Alan E. Gelfand (Department of Statistical Sciences, Duke University).
From 2005 to 2007, Dr. Guindani was a postdoctoral fellow in the Department of Biostatistics at the University of Texas MD Anderson Cancer Center, working with Peter Mueller and Gary L. Rosner.
Dr. Guindani obtained his first faculty position as an Assistant Professor in Statistics in the Department of Mathematics and Statistics at the University of New Mexico (2007–2010).
He then held a position as Assistant Professor (with the award of tenure) in the Department of Biostatistics at the University of Texas MD Anderson Cancer Center (2010-2016).
From 2016 to June 2022, he held a position as Professor in the Department of Statistics of the Donald Bren School of Information and Computer Sciences at the University of California, Irvine (UCI).
Honors and Awards
Dr. Guindani is a Fellow of the American Statistical Association (ASA) and the International Society for Bayesian Analysis (ISBA).
Main Professional Commitments
- Associate Editor, Biometrics (2016-present)
- Associated Editor, Econometrics and Statistics (2018-present)
- Editor-in-Chief of the journal Bayesian Analysis (2019-2021)
- Member, Board of Directors (2018-2020), International Society of Bayesian Analysis (ISBA)
- Associate Editor, Computational Statistics and Data Analysis (2015-2018)
- Program chair of the Scientific Committee of the ISBA 2016 World Meeting (Forte Village, Sardinia, Italy, June 13-17th, 2016)
Pledge to Diversity
Dr. Guindani is committed to fostering a diverse, equitable, and inclusive doctoral experience for all students.
Education
- PhD, Statistics, Università Bocconi, Milano, 2005
Areas of Interest
Biostatistics, Data Science, Machine Learning, Statistical decision-making under Uncertainty, Multiple comparison problems, Statistical Imaging, Clinical Trials, Study Design, Clustering, Bayesian modeling, and Nonparametric Bayesian models.
Main domains of Application: Analysis of high-dimensional data in medical and basic research. Expert in handling functional data, time series, data from the Neurosciences, Radiomics, Genomics (including microbiome data), and integrative multilevel/multimodal approaches to data science.