I am a Computer Science Ph.D. candidate at Brown University with a research focus in Theoretical Computer Science, where I am fortunate to be advised by Eli Upfal. In Spring 2024, my work has been supported supported by the Kanellakis Fellowship. In Fall 2023, I was a co-instrucor for Advanced Introduction to Probability for Computing and Data Science (CS145). In Summer 2023, I was an intern at Yahoo! Research on the Scalable Machine Learning team.

My main research area is Machine Learning Theory. I am broadly interested in learning settings where there is access to a small amount of data for the target task. In my work, I developed theoretically sound methods that can quantify and use the knowledge provided by different sources other than labeled data for weak supervision. Recently, I worked on the problem of learning with distribution drift, where we are given a sequence of samples from a distribution that gradually changes in time, and we want to solve a learning task with respect to the current distribution.

Prior to coming to Brown, I completed a Master in Computer Science and a Bachelors in Information Engineering from University of Padua in Italy.