About

Matteo Riondato is an associate professor of computer science at Amherst College, where he leads the Data* Mammoths, a research and learning group composed of undergraduate students. He also serves as the founding director of the Data Science Initiative at Amherst. In addition to his role at Amherst College, he holds an appointment as visiting faculty in Computer Science at Brown University, where he advises PhD students. Prior to his academic positions, he worked as a research scientist in the Labs group at Two Sigma. His research focuses on algorithms for knowledge discovery, data mining, and machine learning. He develops theory and methods aimed at extracting the most information from large datasets as quickly as possible while maintaining statistical soundness. The problems he studies include pattern extraction, graph mining, and time series analysis. His algorithms often incorporate concepts from statistical learning theory and sampling. His research has received support from the National Science Foundation, including an NSF CAREER Award and other NSF awards. Matteo Riondato's academic lineage includes notable mathematicians such as Eli Upfal, Eli Shamir, Jacques Hadamard, Siméon Denis Poisson, and Pierre-Simon Laplace.

Research topics

• Computer science
• Algorithm
• Data mining
• Theoretical computer science
• Mathematics

Selected publications

• DSP: A Statistically-Principled Structural Polarization Measure

  2026-02-16

  articleOpen access

  Social and information networks may become polarized, leading to echo chambers and political gridlock. Accurately measuring this phenomenon is a critical challenge. Existing measures often conflate genuine structural division with random topological features, yielding misleadingly high polarization scores on random networks, and failing to distinguish real-world networks from randomized null models. We introduce DSP, a Diffusion-based Structural Polarization measure designed from first principles to correct for such biases. DSP removes the arbitrary concept of 'influencers' used by the popular Random Walk Controversy (RWC) score, instead treating every node as a potential origin for a random walk. To validate our approach, we introduce a set of desirable properties for polarization measures, expressed through reference topologies with known structural properties. We show that DSP satisfies these desiderata, being near-zero for non-polarized structures such as cliques and random networks, while correctly capturing the expected polarization of reference topologies such as monochromatic-splittable networks. Our method applied to U.S. Congress datasets uncovers trends of increasing polarization in recent years. By integrating a null model into its core definition, DSP provides a reliable and interpretable diagnostic tool, highlighting the necessity of statistically-grounded metrics to analyze societal fragmentation.

  PublisherDOI

• Source Code and Replication Data for: HomeRun: Performing Curveball Trades quasi in Streaming for Fast Null Modeling of Graphs, Hypergraphs, and Binary Matrices

  Harvard Dataverse · 2026-01-20

  datasetOpen access1st authorCorresponding

  See the README.md file.

  PublisherDOI

• HomeRun: Performing Curveball Trades quasi in Streaming for Fast Null Modeling of Graphs, Hypergraphs, and Binary Matrices

  2026-04-12

  articleOpen accessSenior author
  PublisherDOI

• DSP: A Statistically-Principled Structural Polarization Measure

  ArXiv.org · 2025-12-03

  preprintOpen access

  Social and information networks may become polarized, leading to echo chambers and political gridlock. Accurately measuring this phenomenon is a critical challenge. Existing measures often conflate genuine structural division with random topological features, yielding misleadingly high polarization scores on random networks, and failing to distinguish real-world networks from randomized null models. We introduce DSP, a Diffusion-based Structural Polarization measure designed from first principles to correct for such biases. DSP removes the arbitrary concept of 'influencers' used by the popular Random Walk Controversy (RWC) score, instead treating every node as a potential origin for a random walk. To validate our approach, we introduce a set of desirable properties for polarization measures, expressed through reference topologies with known structural properties. We show that DSP satisfies these desiderata, being near-zero for non-polarized structures such as cliques and random networks, while correctly capturing the expected polarization of reference topologies such as monochromatic-splittable networks. Our method applied to U.S. Congress datasets uncovers trends of increasing polarization in recent years. By integrating a null model into its core definition, DSP provides a reliable and interpretable diagnostic tool, highlighting the necessity of statistically-grounded metrics to analyze societal fragmentation.

  PublisherOA PDFDOI

• Source Code and Replication Data for: VaLUH: Fast Algorithms for the Configuration Model of Vertex-Labeled Undirected Hypergraphs

  Harvard Dataverse · 2025-12-03

  datasetOpen access1st authorCorresponding

  See the README.md file.

  PublisherDOI

• DiNgHy: Null Models for Non-degenerate Directed Hypergraphs

  Lecture notes in computer science · 2025-10-03

  article
  PublisherDOI

• <scp>Polaris:</scp> Sampling from the Multigraph Configuration Model with Prescribed Color Assortativity

  2025-02-26 · 2 citations

  article
  PublisherDOI

• ClaveNet: Generating Afro-Cuban Drum Patterns through Data Augmentation

  2024-09-11

  articleOpen accessSenior author

  We present ClaveNet: a generative MIDI model for Afro-Cuban percussion. We adapt the Monotonic Groove Transformer (MGT) —originally trained on the Groove MIDI Dataset (GMD)— to generate Afro-Cuban-influenced MIDI drum grooves. As Afro-Cuban drum MIDI data is scarce in the GMD and overall, we devise a data augmentation scheme to enrich MIDI percussion datasets with Afro-Cuban-inspired drum grooves by mixing examples with “seed patterns” rudimentary to Afro-Cuban percussion. To validate the effectiveness of our data augmentation algorithm at creating drum grooves infused with Afro-Cuban patterns, we trained MGT models on variants of the Groove MIDI Dataset augmented with our algorithm, and compared them to a baseline model trained on a non-augmented dataset. Our results show that MGT models trained with our augmented datasets are able to generate drum grooves whose rhythmic features are cumulatively closer to those from an evaluation set of real Afro-Cuban examples. We explore the effects of different hyperparameters to our system, discuss individual generated samples of selected models, and assess their faithfulness to Afro-Cuban styles. We hope this project fosters more research on developing music co-creation systems that encompass diverse musical styles outside those found in publicly available datasets.

  PublisherDOI

• Polaris: Sampling from the Multigraph Configuration Model with Prescribed Color Assortativity

  arXiv (Cornell University) · 2024-09-02

  preprintOpen access

  We introduce Polaris, a network null model for colored multi-graphs that preserves the Joint Color Matrix. Polaris is specifically designed for studying network polarization, where vertices belong to a side in a debate or a partisan group, represented by a vertex color, and relations have different strengths, represented by an integer-valued edge multiplicity. The key feature of Polaris is preserving the Joint Color Matrix (JCM) of the multigraph, which specifies the number of edges connecting vertices of any two given colors. The JCM is the basic property that determines color assortativity, a fundamental aspect in studying homophily and segregation in polarized networks. By using Polaris, network scientists can test whether a phenomenon is entirely explained by the JCM of the observed network or whether other phenomena might be at play. Technically, our null model is an extension of the configuration model: an ensemble of colored multigraphs characterized by the same degree sequence and the same JCM. To sample from this ensemble, we develop a suite of Markov Chain Monte Carlo algorithms, collectively named Polaris-*. It includes Polaris-B, an adaptation of a generic Metropolis-Hastings algorithm, and Polaris-C, a faster, specialized algorithm with higher acceptance probabilities. This new null model and the associated algorithms provide a more nuanced toolset for examining polarization in social networks, thus enabling statistically sound conclusions.

  PublisherOA PDFDOI

• Impossibility result for Markov chain Monte Carlo sampling from microcanonical bipartite graph ensembles

  Physical review. E · 2024-05-13 · 2 citations

  articleSenior author

  Markov Chain Monte Carlo (MCMC) algorithms are commonly used to sample from graph ensembles. Two graphs are neighbors in the state space if one can be obtained from the other with only a few modifications, e.g., edge rewirings. For many common ensembles, e.g., those preserving the degree sequences of bipartite graphs, rewiring operations involving two edges are sufficient to create a fully connected state space, and they can be performed efficiently. We show that, for ensembles of bipartite graphs with fixed degree sequences and number of butterflies (k_{2,2} bicliques), there is no universal constant c such that a rewiring of at most c edges at every step is sufficient for any such ensemble to be fully connected. Our proof relies on an explicit construction of a family of pairs of graphs with the same degree sequences and number of butterflies, with each pair indexed by a natural c, and such that any sequence of rewiring operations transforming one graph into the other must include at least one rewiring operation involving at least c edges. Whether rewiring this many edges is sufficient to guarantee the full connectivity of the state space of any such ensemble remains an open question. Our result implies the impossibility of developing efficient, graph-agnostic, MCMC algorithms for these ensembles, as the necessity to rewire an impractically large number of edges may hinder taking a step on the state space.

  PublisherDOI

Recent grants

• CAREER: Statistically-Sound Knowledge Discovery from Data

  NSF · $483k · 2023–2028

• III: Small: RUI: Scalable and Iterative Statistical Testing of Multiple Hypotheses on Massive Datasets

  NSF · $373k · 2020–2024

Frequent coauthors

• Eli Upfal

  61 shared

• Mert Akdere

  Brown University

  14 shared

• Fabio Vandin

  University of Padua

  14 shared

• Uğur Çetintemel

  14 shared

• Stanley B. Zdonik

  Brown University

  12 shared

• Cyrus Cousins

  10 shared

• Gianmarco De Francisci Morales

  10 shared

• Giulia Preti

  Centre d'Imagerie BioMedicale

  8 shared

Labs

• Data* MammothsPI

  Research on Algorithms for Knowledge Discovery, Data Mining, and Machine Learning

Education

• Ph.D., Computer Science

  Brown University

  2014

• Sc.M., Computer Science

  Brown University

  2010

• Laurea Specialistica (M.Sc.), Information Engineering

  Università degli Studi di Padova

  2009

• Laurea (B.Sc.), Information Engineering

  Università degli Studi di Padova

  2007