Roberto Molinari, PhD
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Short Bio
Roberto Molinari is an Assistant Professor of Statistics at Auburn University. After studies and professional experience in unrelated fields, he received his PhD in Statistics from the University of Geneva, where his dissertation focused on robust inference for random fields and latent models. Before joining Auburn in 2020, he held appointments as a Lindsay Assistant Professor at Penn State University and a Visiting Assistant Professor at the University of California, Santa Barbara. His research lies at the intersection of robust statistics, signal processing, differential privacy and machine learning, with applications spanning engineering, epidemiology, economics, and the biological sciences. He has published in journals across different fields such as the Journal of the American Statistical Association, Molecular Ecology, Current Biology, Scientific Reports and IEEE Transactions on Signal Processing. His work has been supported by the National Science Foundation and the Swiss National Science Foundation. He is also an active developer of statistical software, contributing several R packages for robust inference, time series analysis and differentially private methods.
Education
BSc Political Sciences LUISS Guido Carli (Italy) 2005
MSc International Relations LUISS Guido Carli (Italy) 2007
MSc Statistics University of Geneva (Switzerland) 2012
PhD Statistics University of Geneva (Switzerland) 2016
Professional Experience
Roberto Molinari has a broad professional experience at the intersection of academia, consulting, and international policy. Beyond his academic appointments at Auburn University, Penn State, and UC Santa Barbara, he has engaged in statistical consulting projects in Switzerland and Senegal, including collaborations with WHO and UNICEF on child protection and female health, as well as developing platforms for monitoring of official statistics. Earlier in his career, he worked with Ernst & Young on European Commission policy evaluations, and had experience in international organizations such as the United Nations Economic Commission for Europe and the Italian Permanent Mission to the OECD, focusing on energy, trade, and development policy. His professional trajectory also includes roles in the private sector in logistics, communications, and hospitality.
Innovation
Roberto Molinari’s research focuses on robust statistical inference, stochastic processes, Rashomon algorithms and differential privacy, with strong intersections in machine learning, signal processing, and multi-model inference. He has contributed both theoretical and methodological advances, including wavelet-based inference for time series, perturbed M-estimation for privacy-preserving analysis, and Rashomon algorithms for sparse model selection. His work also emphasizes practical applications across disciplines, ranging from engineering and geosciences (inertial sensor calibration, rainfall prediction) to biology and medicine (genomic biomarkers, honey bee colony loss, epidemiology).
Engagement
Aside from proposing new courses and developing existing ones,Roberto Molinari is actively engaged in outreach and community initiatives that extend statistical education and data science beyond the university. At Auburn, he serves as AI representative for the College of Sciences and Mathematics, promoting interdisciplinary dialogue on artificial intelligence, and regularly judges undergraduate and graduate research symposia. He has also lectured for the Alabama Prison Arts + Education Project (APAEP), bringing data literacy to incarcerated students.
