Matteo Riondato

Contact info

Head shot of Matteo
					Riondato by Andrea Podestà

I am an assistant professor of computer science at Amherst College, where I lead the Data* Mammoths, a research&learning group of brilliant undergraduate students. 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 research is supported, in part, by NSF Award #2006765.

My Erdős number is 3 (ErdősSuenUpfal → 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).


  • DMKD/DAMI (ECML PKDD'22): Another work by the Data* Mammoths has been accepted: Steedman and Stefan developed SPEck, a set of Monte-Carlo procedures for efficiently mining statistically-significant sequential patterns according to different null models, using exact sampling, rather than approximate sampling. It appears in the DMKD/DAMI special issue for ECML PKDD'22.
  • MDS'22: My talk on Scalable Algorithms for Hypothesis Testing has been accepted for presentation at the "new" (as the 2020 edition was canceled) SIAM Conference on Mathematics of Data Science, which has a super strong program.
  • TKDD: the extended version of MCRapper was accepted to ACM TKDD. An elegant algorithm to compute Rademacher averages for families of function with a natural poset structure, which are omnipresent in pattern mining. Nice collab with Leonardo, Cyrus, and Fabio.
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