I am an assistant professor of computer science at Amherst College. I also have an appointment as visiting faculty in Computer Science at Brown University. Previously, I spent some fantastic years as a research scientist in the Labs group at Two Sigma.
My research focuses on algorithms for knowledge discovery, data mining, and machine learning. I develop theory and methods to extract the most information from large datasets, as fast as possible and in a statistically sound way. The problems I study include pattern extraction, graph mining, and time series analysis. My algorithms often use concepts from statistical learning theory and sampling.
My Erdős number is 3 (Erdős → Suen → Upfal → Matteo), and I am a mathematical descendant of Eli Upfal, Eli Shamir (2nd generation), Jacques Hadamard (5th), Siméon Denis Poisson (9th), and Pierre-Simon Laplace (10th).
News
- ECML PKDD: CaDET, our algorithm for interpretable conditional density estimation using decision trees has been accepted to the special issue of Machine Learning for ECML PKDD'19. Joint work with thinker-extraordinaire PhD student Cyrus Cousins.
- ICERM: Happy to give a talk about pseudodimension for data analysis at the Data Science in Low-dimensional Spaces workshop taking place at ICERM. Even more excited to hear about exciting research from others.
- ACM KDD: SPuManTE, our work on unconditional multiple hypothesis testing for combinatorial patterns has been accepted to KDD'19. Joint work with thought leader Fabio Vandin and amazing Ph.D. student Leonardo Pellegrina.
- ACM KDD: Our tutorial on multiple hypothesis testing and statistically-sound pattern mining (2-pager) has been accepted to ACM KDD'19. See you in Anchorage, AK!
- KAIS: an extended version of ProSecCo: Progressive Sequence Mining with Convergence Guarantees was invited to the special issue of KAIS for the best papers of IEEE ICDM'18.
- ACM TKDD: an extended version of MiSoSouP was invited to the special issue of ACM TKDD for the best papers of KDD'18.
- News archive