About

I am a fifth year PhD student in the department of Statistics & Data Science at Carnegie Mellon University. My primary research interests are statistical machine learning methodologies driven by real world problems and complex datasets, especially from the natural sciences. I am very fortunate to be advised by Professor Ann B. Lee. I am an active member and website maintainer of the Statistical Methods for the Physical Sciences (STAMPS).

Prior to CMU, I have worked for a couple of years in London as Analyst for Zynga (NaturalMotion) after pursuing an M.Sc. in Statistics at Imperial College London, where I have worked on bayesian hierarchical models for cosmology under the guidance of Dr. Daniel Mortlock and Dr. Stephen Feeney. Before that, everything started with a B.Sc. in Mathematics and a Diploma in Government and Natural Sciences from University of Turin, Italy.

• January 2021: I’ll be at the AAAI Workshop on Knowledge Discovery from Unstructured Data in Financial Services presenting our paper on “PayVAE: A Generative Model for Financial Transactions“
• December 2020: I’ll be at the NeurIPS 2020 Tackling Climate Change with Machine Learning Workshop presenting:
  • “Structural Forecasting for Tropical Cyclone Intensity Prediction: Providing Insight with Deep Learning” (Spotlight Talk)
  • “HECT: High-Dimensional Ensemble Consistency Testing for Climate Models“
• August 2020: At AISTATS 2020 presenting our paper “Validation of Approximate Likelihood and Emulator Models for Computationally Intensive Simulations” (online talk)
• July 2020: At ICML 2020 presenting our paper “Confidence Sets and Hypothesis Testing in a Likelihood-Free Inference Setting” (online talk)
• June 2020 - Aug. 2020: Summer Research Associate, AI & Machine Learning, J.P.Morgan AI Research
• February 2020: Poster presentation at the Astronomical Data Science Workshop at Texas A&M University, TX
• December 2019: NeurIPS 2019 Workshops in Vancouver, Canada:
  • AI for Humanitarian Assistance and Disaster Response (Spotlight Talk)
  • Tackling Climate Change with ML (Spotlight Talk)
  • “Do the right thing”: machine learning and causal inference for improved decision making. (Poster)
• July 2019: Presentation and poster at the 2019 Joint Statistical Meeting (JSM) in Denver, CO
• June 2019: Poster presentation at the Climate Change: How can AI help? ICML 2019 workshop in Long Beach, CA
• December 2018: Poster presentation at the Critiquing and Correcting Trends in Machine Learning NeurIPS 2018 workshop in Montreal, Canada
• May 2018 - Aug. 2018: AI Research Intern at IBM Research in Yorktown Heights, NY
• November 2017: National finalist at the Citadel Data Open in New York, NY
• September 2017: Winner of the Citadel Data Open at Carnegie Mellon University, with fellow students Alan Mishler, Kwangho Kim and Chirag Nagpal

• 2019 Statistics & Data Science TA of the Year
  For the services offered as TA of 36-650\750 (Statistical Computing).
  
• 2017 Citadel Data Open Datathon at Carnegie Mellon University
  Competed and won with fellow students Alan Mishler, Kwhangho Kim, and Chirag Nagpal (550+ student applications, around 125 students selected to compete).
  
• 2012 Alfaclass Mathematics Team Competition
  First prize across 100 students selected based on GPA across mathematics and engineering department at University of Turin.
  
• Full Scholarship - 2011, 2012, 2013
  Scholarship covering tuition fees, annual bursary and living expenses via the Scuola of Studi Superiori ‘Ferdinando Rossi’ and ‘Compagnia di San Paolo’