PhdFit
Resume-aware faculty matching

Find professors who actually fit you

Review faculty evidence in public, then use the workspace to turn your background into a shortlist, outreach, and meeting prep.

Profile-awarePaper evidenceSix agents
Robert E Kass

Robert E Kass

· Maurice Falk Professor of Statistics and Computational Neuroscience

Carnegie Mellon University · Machine Learning Department

Active 1982–2026

h-index71
Citations38.7k
Papers39919 last 5y
Funding$15.1M1 active

Academic metrics are sourced from OpenAlex and public funding records; values may differ from Google Scholar.

Faculty pageLab page
See your match with Robert E Kass — sign in to PhdFit.Sign in

About

Robert E. Kass is the Maurice Falk University Professor of Statistics & Computational Neuroscience at Carnegie Mellon University. He is affiliated with the Department of Statistics & Data Science, the Machine Learning Department, and the Neuroscience Institute. His research focuses on computational neuroscience, statistical inference, and neural data analysis. Kass has authored significant works including the books 'Analysis of Neural Data' and 'Geometrical Foundations of Asymptotic Inference,' which reflect his contributions to the understanding of neural data and statistical theory. His professional interests encompass statistical models of the brain, effective statistical practice, and scientific storytelling, emphasizing the application of statistical methods to neuroscience and related fields.

Research topics

  • Computer Science
  • Artificial Intelligence
  • Statistics
  • Mathematics
  • Neuroscience
  • Psychology
  • Geometry
  • Medicine

Selected publications

  • TORUS GRAPHS FOR MULTIVARIATE PHASE COUPLING ANALYSISa

    The Annals of Applied Statistics · 2020 · 15 citations

    Senior authorCorresponding

    Angular measurements are often modeled as circular random variables, where there are natural circular analogues of moments, including correlation. Because a product of circles is a torus, a d-dimensional vector of circular random variables lies on a d-dimensional torus. For such vectors we present here a class of graphical models, which we call torus graphs, based on the full exponential family with pairwise interactions. The topological distinction between a torus and Euclidean space has severa…

    PublisherDOI

  • Latent Dynamic Factor Analysis of High-Dimensional Neural Recordings.

    PubMed · 2020 · 13 citations

    Senior authorCorresponding

    High-dimensional neural recordings across multiple brain regions can be used to establish functional connectivity with good spatial and temporal resolution. We designed and implemented a novel method, Latent Dynamic Factor Analysis of High-dimensional time series (LDFA-H), which combines (a) a new approach to estimating the covariance structure among high-dimensional time series (for the observed variables) and (b) a new extension of probabilistic CCA to dynamic time series (for the latent varia…

    PublisherOA PDF

  • Identification of interacting neural populations: methods and statistical considerations

    Journal of Neurophysiology · 2023-07-19 · 10 citations

    reviewOpen access1st authorCorresponding

    As improved recording technologies have created new opportunities for neurophysiological investigation, emphasis has shifted from individual neurons to multiple populations that form circuits, and it has become important to provide evidence of cross-population coordinated activity. We review various methods for doing so, placing them in six major categories while avoiding technical descriptions and instead focusing on high-level motivations and concerns. Our aim is to indicate what the methods c…

    PublisherDOI

  • Population burst propagation across interacting areas of the brain

    Journal of Neurophysiology · 2022 · 10 citations

    Senior authorCorresponding

    We developed a novel statistical method for identifying coordinated propagation of activity across populations of spiking neurons, with high temporal accuracy. Using simultaneous recordings from three visual areas we document precise timing relationships on a trial-by-trial basis, and we show how previously existing techniques can fail to discover coordinated activity in cases where the new approach finds very strong cross-area correlation.

    PublisherDOI

  • Cross-population coupling of neural activity based on Gaussian process current source densities

    arXiv (Cornell University) · 2021-04-20 · 7 citations

    articleOpen accessSenior authorCorresponding

    Because local field potentials (LFPs) arise from multiple sources in different spatial locations, they do not easily reveal coordinated activity across neural populations on a trial-to-trial basis. As we show here, however, once disparate source signals are decoupled, their trial-to-trial fluctuations become more accessible, and cross-population correlations become more apparent. To decouple sources we introduce a general framework for estimation of current source densities (CSDs). In this frame…

    PublisherOA PDFDOI

Recent grants

Frequent coauthors

  • J. Ocariz

    Université Paris Cité

    122 shared

  • Ph. Leruste

    101 shared

  • V. Tisserand

    Centre National de la Recherche Scientifique

    95 shared

  • M. Carpinelli

    90 shared

  • S. Bettarini

    88 shared

  • R. Stroili

    88 shared

  • R. J. Wilson

    Colorado State University

    88 shared

  • E. Paoloni

    Istituto Nazionale di Fisica Nucleare, Sezione di Pisa

    87 shared

Labs

Similar researchers at Carnegie Mellon University

  • Resume-aware match score
  • Save to shortlist
  • AI-drafted outreach

See your match with Robert E Kass

PhdFit ranks faculty by your research interests, methods, and publications — grounded in their actual work, not templates.

Sign in to PhdFitHow it works
  • Free to start
  • No credit card
  • 30-second signup