My name is Roberto Molinari and I’m an Assistant Professor in Statistics

This website is under construction.

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Experience

A quick overview of my different experiences inside and outside academia up to now

 
 
 
 
 

Assistant Professor

Auburn University

Aug 2020 – Present Auburn (AL), USA
 
 
 
 
 

Lindsay Assistant Professor

Penn State University

Aug 2018 – Jun 2020 State College (PA), USA
 
 
 
 
 

Pro Bono Consultant

Global Advocacy and Research Group

Sep 2017 – Mar 2018 Dakar, Senegal
 
 
 
 
 

Visiting Assistant Professor

University of California, Santa Barbara

Sep 2016 – Jun 2017 Santa Barbara (CA), USA
 
 
 
 
 

Teaching and Research Assistant

University of Geneva

Sep 2012 – Aug 2020 Geneva, Switzerland
 
 
 
 
 

Statistical Consultant

Life Store SA

Jun 2012 – Feb 2016 Geneva, Switzerland
 
 
 
 
 

Internship

BeeOne Communications SA

Jun 2011 – Feb 2012 Geneva, Switzerland
 
 
 
 
 

Internship

International Trade Center

Feb 2011 – May 2011 Geneva, Switzerland
 
 
 
 
 

Advisor - Government & Non-Profit

Ernst & Young SpA

Oct 2007 – Aug 2009 Rome, Italy
 
 
 
 
 

Internship

Organization for Economic Cooperation and Development

Jul 2007 – Sep 2007 Paris, France
 
 
 
 
 

Internship

United Nations Economic Commission for Europe

Sep 2006 – Jan 2007 Geneva, Switzerland

Projects

This part is under construction

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Interpretable Network Selection

Many machine/statistical-learning techniques are tailored for accurate prediction but not necessarily for ease of interpretation. This project aims to achieve both.

Random Fields Inference

Using a wavelet-decompositon of a random field, the aim is to efficiently estimate (latent) spatial models.

Statistical Inference for Private Data

Given the increased push for data privacy, this project aims at delivering appropriate statistical tools for reliable inference in these settings.

Stochastic Sensor Calibration

There is considerable research on improving navigation accuracy of unmanned (aerial) vehicles. This direction of research focuses on improving stochastic sensor calibration to integrate navigation filters and produce more accurate navigation performance.

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