Matteo Sesia

Matteo Sesia is an assistant professor in the department of Data Sciences and Operation, at the USC Marshall School of Business. His research is focused on developing data science methods combining the power of machine learning algorithms with the reliability of rigorous statistical guarantees. While pursuing this goal, he enjoys dividing his time between theoretical, methodological, computational, and applied work. His doctoral research earned the Jerome H. Friedman Applied Statistics Dissertation Award from the Stanford Statistics Department in 2020.

Education

• Doctor of Philosophy (PhD) Stanford University (2015 — 2020)
• Master of Science (MS) Politecnico di Torino (2013 — 2015)
• Master of Arts (M.A.) Collegio Carlo Alberto (2011 — 2015)
• Bachelor of Science (BS) Politecnico di Torino (2010 — 2013)
• British Columbia (Canada) Certificate of Graduation Oak Bay High School (2009 — 2010)

Companies

• Assistant Professor of Data Sciences and Operations University of Southern California - Marshall School of Business (2020)
• Research And Teaching Assistant Stanford University (2015 — 2020)
• Data Science Research Intern Adobe (2017 — 2017)
• Research Intern in Machine Learning École Normale Supérieure de Cachan (2015 — 2015)
• Research Intern in Statistical Physics and Inference Collegio Carlo Alberto (2012 — 2013)

Publications

• S. Li, M. Sesia, Y. Romano, E. Cand`es, C. Sabatti. Searching for robust associations with a multienvironment knockoff filter. Biometrika (2021, recently accepted).
• M. Sesia, Y. Romano. Conformal histogram regression. NeurIPS (spotlight) (2021, recently accepted).
• M. Sesia, S. Bates, E. Cand`es, J. Marchini, C. Sabatti. False discovery rate control in genome-wide association studies with population structure. Proc. Natl. Acad. Sci. U.S.A., 118 (40) (2021).
• C. Chia,∗ M. Sesia,∗ C.-S. Ho, S. Jeffrey, J. Dionne, E. Cand`es, R. Howe. Interpretable Classification of Bacterial Raman Spectra with Knockoff Wavelets. IEEE J. Biomed. Health. Inform. (2021).
• Y. Romano,∗ M. Sesia,∗ E. Cand`es. Classification with valid and adaptive coverage. NeurIPS (spotlight) (2020).
• S. Bates, M. Sesia, C. Sabatti, E. Cand`es. Causal inference in genetic trio studies. Proc. Natl. Acad. Sci. U.S.A., 117 (39) 24117-24126 (2020).
• M. Sesia, E. Katsevich, S. Bates, E. Cand`es, C. Sabatti. Multi-resolution localization of causal variants across the genome. Nature Commun., 11, 1093 (2020).
• M. Sesia, E. Cand`es. A comparison of some conformal quantile regression methods. Stat, 9:e261 (2020).
• Y. Romano,∗ M. Sesia,∗ E. Cand`es. Deep knockoffs. J. Am. Stat. Assoc. (2019).
• M. Sesia, C. Sabatti, E. Cand`es. Rejoinder: “Gene hunting with hidden Markov model knockoffs”. Biometrika, 106, 35–45 (2019).
• M. Sesia, C. Sabatti, E. Cand`es. Gene hunting with hidden Markov model knockoffs. Biometrika, 106, 1–18 (2019).

Patents

• M. Sesia and Y. Abbasi-Yadkori (Adobe Inc). “Recommendation system using linear stochastic bandits and confidence interval generation”. US 11,100,559. August 24, 2021.

Awards

• Jerome H. Friedman Applied Statistics Dissertation Award (2020).
• International Master’s Scholarship, Universit´e Paris-Saclay (2014–2015).
• Allievi Honors Program, Collegio Carlo Alberto (2011–2015).