CV

Harvard University – Visiting Graduate Student

• Affiliation: Department of Biostatistics at Harvard T.H. Chan School of Public Health and Harvard Data Science Initiative.
• Project: Causal Inference for Machine Learning.
• @NSAPH: Interpretable Inference of Heterogeneous Treatment Effects. Working on 1 methodological paper, 3 applied papers, 1 software paper (P.I.: Francesca Dominici).

EPFL – M.Sc. in Data Science

• Conferences: CISBAT 2021 (Lausanne), NeurIPS 2021 (remote), CVPR 2022 (New Orleans) and NeurIPS (New Orleans).
• Summer Schools: Mediterrean Machine Learning Summer School 2020 (remote), Neurosymbolic Programming Summer School (Los Angeles) and Mediterrean Machine Learning Summer School 2022 (remote).
• @VITA: Introducing the Causal (Representation) formalism and a Robust and Adaptive modular architecture for Motion Forecasting [NIPS21],[CVPR22] (P.I.: Alexandre Alahi).
• @LESO-PB: Introducing a U‐Net (FCNN) based model for detection of available rooftop area to install photovoltaic panels from satellite images [JOP21] (P.I.: Roberto Castello).

Politecnico di Milano – B.Sc. in Mathematical Engineering

• Grade: 110 (110 scale).
• Associations: PoliMI Data Scientists, Associazione Ingegneri Matematici.
• Thesis: Mathematical Programming for activity planning in an Oncology Day-Hospital (P.I.: Giuliana Carello).

Research Assistant at @NSAPH at Harvard

• Working on development and release of Bayesian Causal Forest with Instrumental Variable algorithm package and its software paper (P.I.: Francesca Dominici).

Machine Learning Researcher at Schlumberger-Doll Research in Cambridge (MA) (6-months Internship)

• Deep Learning for Causal Modeling and interpretation of acoustic (sonic and ultrasonic) subsurface data for anomaly detection and hazard prevention.

Research Assistant at @Intelligent Global Health at EPFL (Summer Internship)

• Developing a mobile app for (non-invasive) upper body posture detection using Deep Learning (P.I.: Martin Jaggi).

Machine Learning Engineer at L.O.L. Consulting Group (Freelance)

• Detecting available rooftop area to install photovoltaic panels from high quality satellite images using Deep Learning.

Teaching Assistant at EPFL.

• Professor: J. Brea.
• Course: Introduction to Machine Learning for Bioengineers (BIO-322).

Student Assistant at Politecnico di Milano.

• Task: Teacher (merit based selection) for individual students and small groups.
• Courses: Calculus 1 and Linear Algebra.

Olympic Math Teacher at BrixiAmaTe.

• Task: Teacher in a Math Summer School explaining mathematical concepts, methods and techniques for Mathematical Olympiad.
• Courses: Combinatorics and Probability.

• Generali S.p.a. - Data Challenge 2020: (individual) 1st place out of 280+ participants for best model report on a Churn Classification Task and 2° (7°) best performance on public (private) test set.

• EPFL - Higgs Boson Challenge 2020: (in a team of 3) 2nd place* out of 290+ teams in the AICrowd final challenge of Machine Learning course at EPFL about Higgs Boson collisions prediction.

• Politecnico di Milano - Machine Learning for Networking Contest 2019: (individual) 1st place in the Kaggle final challenge of Machine Learning for Networking course at Politecnico di Milano about churn classification.

• Oracle Labs and Politecnico di Milano - Graph Machine Learning Contest 2019: (in a team of 2) 1st place in the Kaggle final challenge of High Performance and Graph Analytics course at Politecnico di Milano in partnership with Oracle Labs.

• International competition for mathematical and logical games: 5th national place (ITA), class L2 (Under21).

• Grand Prix of Applied Mathematics: 5th national place (ITA) out of 7500+ students.

• Grand Prix of Applied Mathematics: 6th national place (ITA) out of 7500+ students.