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PhD Candidate in Computer Science at UMass Amherst | Statistical ML and Privacy-Preserving ML | Ex-Google & Meta Research Intern, LinkedIn AI/ML PhD intern | Fulbright Scholar
I’m a PhD candidate in computer science at UMass Amherst. My research interests lie broadly in the area of statistical machine learning, with a specific interest in differential privacy and privacy-preserving technologies. My journey into machine learning and privacy began with an undergraduate degree in advanced economics and statistics in my home country, Italy. I later pursued a research-based MS in Computational Design at Carnegie Mellon University, supported by a Fulbright Scholarship. Upon graduating from CMU, I took a break from academia to work in the industry as a Machine Learning Software Engineer at Cadence Design Systems, Pittsburgh. More recently, I've worked as a Research Intern at Google (Summer 2021), as a Research Engineering Intern at Meta in the Stats and Privacy team (Summer 2022), and as AI/ML PhD Intern at LinkedIn (Summer 2025). I expect to graduate from my PhD program in Spring 2026.
University of Massachusetts Amherst
Doctor of Philosophy (PhD), Computer Science
January 1, 2019 – January 1, 2026
Carnegie Mellon University
Master of Science (MS), Computational Design, focus on Machine Learning
January 1, 2016 – January 1, 2018
Politecnico di Torino
BS+MS (advanced undergraduate degree), Architectural Engineering
January 1, 2010 – January 1, 2015
Collegio Carlo Alberto
Honors BA+MA, Economics & Statistics
January 1, 2009 – January 1, 2016
Liceo Classico Statale "V. Gioberti" Torino
Maturità Classica
January 1, 2004 – January 1, 2009
AI/ML PhD Intern
May 1, 2025 – August 1, 2025
New York, United States
Meta
Research Intern
May 1, 2022 – August 1, 2022
New York, United States
Research Intern
May 1, 2021 – August 1, 2021
New York, New York, United States
University of Massachusetts Amherst
Research Assistant
August 1, 2019 – Present
Cadence Design Systems
Machine Learning Software Engineering - Graduate Intern
June 1, 2018 – May 1, 2019
Greater Pittsburgh Area
Procore Technologies
Quantitative UX Research Intern
May 1, 2017 – July 1, 2017
Santa Barbara, California Area
Carnegie Mellon University
Research Assistant
April 1, 2017 – May 1, 2018
Fulbright Association
Fulbright Scholar
August 1, 2016 – May 1, 2019
Greater Pittsburgh Area
HP-Intel NASA Design Challenge "Life In Space"
March 1, 2017 – April 1, 2017
One of the major challenges astronauts face in space is muscle atrophy. MuscleMaximus is a self-sustaining, purely mechanical exoskeleton designed to create resistance to movement. It would help astronauts exercising their muscles while doing their normal space routine, therefore reducing the risk of muscle atrophy.
The Harmonograph
September 1, 2016 – December 1, 2016
Final Project for 15-112 Fundamentals of Programming and Computer Science. Prof. David Kosbie Awarded the 2nd place among the best projects in the course.
TOEFL iBT - 120/120
ETS
June 24, 2026 – Present
Cultural Fit Analysis
The candidate's diverse academic background, including economics, statistics, and architectural engineering, alongside computer science, suggests a broad perspective and interdisciplinary approach. Internships at LinkedIn, Meta, and Google, combined with university research roles, indicate an ability to thrive in high-performance, research-driven environments. The focus on AI/ML and research aligns well with an ML Engineer role, particularly one that values innovation and deep technical contributions. The lack of detailed project descriptions makes it challenging to assess collaboration or leadership styles.
Soft Skills & Operational Fit
The candidate's extensive research background and internships at leading tech companies suggest strong problem-solving, analytical thinking, and adaptability. The academic achievements and research publications indicate a high level of intellectual curiosity and dedication. The project descriptions are concise but lack detail on personal contributions or challenges, making it difficult to fully assess operational fit beyond technical capabilities.