World's Best Scientists 2026 revealed!

D-Index & Metrics

Mathematics

D-Index
58
Citations
20947
World Ranking
618
National Ranking
311

Engineering and Technology

D-Index
54
Citations
22206
World Ranking
3105
National Ranking
918

Overview

Robert E. Kass is affiliated with Carnegie Mellon University in the United States. Their research spans neuroscience and computer science, with a strong focus on cognitive neuroscience and artificial intelligence.

The main fields of study for Robert E. Kass include:

  • Neuroscience
  • Computer Science

The subfields they explore encompass:

  • Cognitive Neuroscience
  • Artificial Intelligence
  • Statistical and Nonlinear Physics
  • Cellular and Molecular Neuroscience
  • Statistics and Probability

Their work covers various topics related to neural systems, including:

  • Neural dynamics and brain function
  • Functional Brain Connectivity Studies
  • Neural Networks and Applications
  • Stochastic dynamics and bifurcation
  • Visual perception and processing mechanisms
  • Neuroscience and Neural Engineering
  • Topological and Geometric Data Analysis

Robert E. Kass has contributed to several notable academic publications. Some of the recent papers include:

  • "TORUS GRAPHS FOR MULTIVARIATE PHASE COUPLING ANALYSISa," 2020, PubMed Central
  • "Population burst propagation across interacting areas of the brain," 2022, Journal of Neurophysiology
  • "Identification of interacting neural populations: methods and statistical considerations," 2023, Journal of Neurophysiology
  • "Cross-population coupling of neural activity based on Gaussian process current source densities," 2021, arXiv (Cornell University)
  • "Neuromatch Academy: a 3-week, online summer school in computational neuroscience," 2022, Journal of Open Source Education

Their frequent co-authors include:

  • Joshua H. Siegle
  • Natalie Klein
  • Heejong Bong
  • Motolani Olarinre
  • Josue Orellana

Publications by Robert E. Kass appear in venues such as:

  • arXiv (Cornell University)
  • Journal of Neurophysiology
  • PubMed Central
  • Journal of Open Source Education
  • Journal of Computational Neuroscience

Best Publications

  • Computing Bayes Factors by Combining Simulation and Asymptotic Approximations

    Thomas J. Diciccio;Robert E. Kass;Adrian Raftery;Larry Wasserman

  • The Selection of Prior Distributions by Formal Rules

    Robert E. Kass;Larry Wasserman

  • A Reference Bayesian Test for Nested Hypotheses and its Relationship to the Schwarz Criterion

    Robert E. Kass;Larry Wasserman

  • Observation of a broad structure in the pi(+)pi(-)J/psi mass spectrum around 4.26 GeV/c(2)

    B. Aubert;R. Barate;D. Boutigny;F. Couderc

  • Multiple neural spike train data analysis: state-of-the-art and future challenges.

    Emery N Brown;Robert E Kass;Partha P Mitra

  • Markov Chain Monte Carlo in Practice: A Roundtable Discussion

    Robert E. Kass;Bradley P. Carlin;Andrew Gelman;Radford M. Neal

  • Efficient and accurate extraction of in vivo calcium signals from microendoscopic video data

    Pengcheng Zhou;Shanna L Resendez;Jose Rodriguez-Romaguera;Jessica C Jimenez

  • Search for lepton flavor violation in the decay τ±→ e±γ

    B. Aubert;R. Barate;D. Boutigny;F. Couderc

  • The time-rescaling theorem and its application to neural spike train data analysis

    Emery N. Brown;Riccardo Barbieri;Valérie Ventura;Robert E. Kass

  • Approximate Bayesian Inference in Conditionally Independent Hierarchical Models (Parametric Empirical Bayes Models)

    Robert E. Kass;Duane Steffey

  • Bayesian curve-fitting with free-knot splines

    Ilaria Dimatteo;Christopher R. Genovese;Robert E. Kass

  • Fully Exponential Laplace Approximations to Expectations and Variances of Nonpositive Functions

    Luke Tierney;Robert E. Kass;Joseph B. Kadane

  • Geometrical Foundations of Asymptotic Inference

    Bruce G. Lindsay;Robert E. Kass;Paul W. Vos

  • Importance sampling: a review

    Surya T. Tokdar;Robert E. Kass

  • Automatic correction of ocular artifacts in the EEG: a comparison of regression-based and component-based methods

    Garrick L Wallstrom;Robert E Kass;Anita Miller;Jeffrey F Cohn

  • Statistical Issues in the Analysis of Neuronal Data

    Robert E. Kass;Valérie Ventura;Emery N. Brown

  • Functional network reorganization during learning in a brain-computer interface paradigm

    Beata Jarosiewicz;Steven M. Chase;Steven M. Chase;George W. Fraser;George W. Fraser;Meel Velliste;Meel Velliste

  • Shrinkage estimators for covariance matrices.

    Michael J. Daniels;Robert E. Kass

  • Recursive Bayesian Decoding of Motor Cortical Signals by Particle Filtering

    A. E. Brockwell;A. L. Rojas;R. E. Kass

  • The Geometry of Asymptotic Inference

    Robert E. Kass

  • Numerical Methods for Unconstrained Optimization and Nonlinear Equations.

    Robert E. Kass;John E. Dennis;Robert B. Schnabel

  • Nonlinear Regression Analysis and its Applications

    Robert E. Kass

Frequent Co-Authors

Uri T. Eden
Uri T. Eden Boston University
Andrew B. Schwartz
Andrew B. Schwartz University of Pittsburgh
Michael J. Tarr
Michael J. Tarr Carnegie Mellon University
Bin Yu
Bin Yu University of California, Berkeley
Carl R. Olson
Carl R. Olson Carnegie Mellon University
John R. Anderson
John R. Anderson Carnegie Mellon University
Liam Paninski
Liam Paninski Columbia University
Joseph B. Kadane
Joseph B. Kadane Carnegie Mellon University

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