World's Best Scientists 2026 revealed!

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Computer Science

D-Index
42
Citations
6650
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8469
National Ranking
3620

Mathematics

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41
Citations
6557
World Ranking
1924
National Ranking
822

Overview

Alessandro Rinaldo is affiliated with The University of Texas at Austin in the United States. Their research primarily focuses on mathematics, with a particular emphasis on statistics and probability.

The main fields of study that shape their work include:

  • Mathematics

Their research spans several subfields, including:

  • Statistics and Probability
  • Artificial Intelligence
  • Management Science and Operations Research
  • Statistics, Probability and Uncertainty
  • Molecular Biology

Alessandro Rinaldo's research covers a variety of topics, such as:

  • Statistical Methods and Inference
  • Statistical Methods and Bayesian Inference
  • Advanced Statistical Methods and Models
  • Advanced Statistical Process Monitoring
  • Statistical Methods in Clinical Trials
  • Advanced Bandit Algorithms Research
  • Random Matrices and Applications

The scientist has published numerous papers, among the recent notable works are:

  • "Univariate mean change point detection: Penalization, CUSUM and optimality" (2020) in Electronic Journal of Statistics
  • "Optimal change point detection and localization in sparse dynamic networks" (2021) in The Annals of Statistics
  • "Optimal Nonparametric Multivariate Change Point Detection and Localization" (2021) in IEEE Transactions on Information Theory
  • "Optimal covariance change point localization in high dimensions" (2020) in Bernoulli
  • "A note on online change point detection" (2023) in Sequential Analysis

Their publications are frequently presented in venues such as:

  • arXiv (Cornell University)
  • Warwick Research Archive Portal (University of Warwick)
  • Electronic Journal of Statistics
  • IEEE Transactions on Information Theory
  • SIAM Journal on Mathematics of Data Science

Alessandro Rinaldo collaborates regularly with several coauthors, including:

  • Yi Yu
  • Daren Wang
  • Oscar Hernán Madrid Padilla
  • Arun Kumar Kuchibhotla
  • Aaditya Ramdas

Best Publications

  • Consistency of spectral clustering in stochastic block models

    Jing Lei;Alessandro Rinaldo

  • Distribution-Free Predictive Inference for Regression

    Jing Lei;Max G'Sell;Alessandro Rinaldo;Ryan J. Tibshirani

  • Confidence sets for persistence diagrams

    Brittany Therese Fasy;Fabrizio Lecci;Alessandro Rinaldo;Larry Wasserman

  • CONSISTENCY UNDER SAMPLING OF EXPONENTIAL RANDOM GRAPH MODELS

    Cosma Rohilla Shalizi;Alessandro Rinaldo

  • On the asymptotic properties of the group lasso estimator for linear models

    Yuval Nardi;Alessandro Rinaldo

  • Properties and refinements of the fused lasso

    Alessandro Rinaldo

  • Generalized density clustering

    Alessandro Rinaldo;Larry Wasserman

  • Autoregressive process modeling via the Lasso procedure

    Y. Nardi;A. Rinaldo

  • On the geometry of discrete exponential families with application to exponential random graph models

    Alessandro Rinaldo;Stephen E. Fienberg;Yi Zhou

  • Differential privacy for functions and functional data

    Rob Hall;Alessandro Rinaldo;Larry Wasserman

  • Stochastic Convergence of Persistence Landscapes and Silhouettes

    Frédéric Chazal;Brittany Terese Fasy;Fabrizio Lecci;Alessandro Rinaldo

  • Robust Topological Inference: Distance To a Measure and Kernel Distance

    Frédéric Chazal;Brittany Terese Fasy;Fabrizio Lecci;Bertrand Michel

  • Maximum likelihood estimation in log-linear models

    Stephen E. Fienberg;Alessandro Rinaldo

  • Characterization of multilocus linkage disequilibrium.

    Alessandro Rinaldo;Silviu-Alin Bacanu;B. Devlin;Vibhor Sonpar

  • Three centuries of categorical data analysis: Log-linear models and maximum likelihood estimation

    Stephen E. Fienberg;Alessandro Rinaldo

  • Differential privacy and the risk-utility tradeoff for multi-dimensional contingency tables

    Stephen E. Fienberg;Alessandro Rinaldo;Xiaolin Yang

  • Uniform asymptotic inference and the bootstrap after model selection

    Ryan J. Tibshirani;Alessandro Rinaldo;Robert Tibshirani;Larry Wasserman

  • Subsampling Methods for Persistent Homology

    Frederic Chazal;Brittany Fasy;Fabrizio Lecci;Bertrand Michel

  • A conformal prediction approach to explore functional data

    Jing Lei;Alessandro Rinaldo;Larry Wasserman

  • Stochastic convergence of persistence landscapes and silhouettes

    Frédéric Chazal;Brittany Terese Fasy;Fabrizio Lecci;Alessandro Rinaldo

  • Statistical Inference For Persistent Homology: Confidence Sets For Persistence Diagrams

    Brittany Terese Fasy;Fabrizio Lecci;Alessandro Rinaldo;Larry Wasserman

Frequent Co-Authors

Larry Wasserman
Larry Wasserman Carnegie Mellon University
Aarti Singh
Aarti Singh Carnegie Mellon University
Stephen E. Fienberg
Stephen E. Fienberg Carnegie Mellon University
Frédéric Chazal
Frédéric Chazal French Institute for Research in Computer Science and Automation - INRIA
Ryan J. Tibshirani
Ryan J. Tibshirani University of California, Berkeley
Rebecca Willett
Rebecca Willett University of Chicago
Steffen L. Lauritzen
Steffen L. Lauritzen University of Copenhagen
Barnabás Póczos
Barnabás Póczos Carnegie Mellon University
Robert Tibshirani
Robert Tibshirani Stanford University
Timothy Verstynen
Timothy Verstynen Carnegie Mellon University

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