Leaderboards/Gaussian Processes and Bayesian Inference

Gaussian Processes and Bayesian Inference

Last updated July 7, 2026

Rising Stars

New and hot

Rising Stars Researchers

1
Oriol Abril
Unknown
2
Venkat Pavan Nemani
Iowa State University
3
Anja Butter
Centre National de la Recherche Scientifique
4
Zi-Lu Ouyang
Shanghai Jiao Tong University
5
Michael Lutter
Technische Universität Darmstadt
6
Chuizheng Meng
University of Southern California
7
Caterina Buizza
Imperial College London
8
Charlotte Fraza
Radboud University Nijmegen
9
James Donnelly
University of Warwick
10
Wodan Ling
Fred Hutch Cancer Center

Rising Stars Papers

1
When physics meets machine learning: a survey of physics-informed machine learning
Machine learning for computational science and engineering|2025
153 cit.
2
Precision-machine learning for the matrix element method
SciPost Physics|2024
20 cit.
3
Jet diffusion versus JetGPT – Modern networks for the LHC
SciPost Physics Core|2025
20 cit.
4
Kicking it off(-shell) with direct diffusion
SciPost Physics Core|2024
18 cit.
5
Back to the formula - LHC edition
SciPost Physics|2024
15 cit.
6
Wave height forecast method with uncertainty quantification based on Gaussian process regression
Journal of Hydrodynamics|2024
7 cit.
7
Variance-tuned ensemble Gaussian process regression for learning and prediction of ship maneuvering motion
Journal of Ocean Engineering and Science|2025
6 cit.
8
Unraveling the link between CNVs, cognition and individual neuroimaging deviation scores from a population-based reference cohort
Nature Mental Health|2024
4 cit.
9
Generative unfolding with distribution mapping
SciPost Physics|2025
4 cit.
10
Deciphering Gut Microbiome in Colorectal Cancer via Robust Learning Methods
Genes|2025
3 cit.

Emerging Leaders

Gaining momentum

Emerging Leaders Researchers

1
Allen Riddell
Indiana University
2
Soumik Mandal
The Ohio State University
3
Paris Perdikaris
University of Pennsylvania
4
Zhanhao Zhang
China Railway Group (China)
5
Ruth Angus
American Museum of Natural History
6
Stephan Mandt
Irvine University
7
Yun Yang
University of Maryland, College Park
8
Ruosong Wang
Carnegie Mellon University
9
L. Gouskos
John Brown University
10
Yarin Gal
Science Oxford

Emerging Leaders Papers

1
Auto-Encoding Variational Bayes
Cambridge Explorations in Arts and Sciences|2024
1k cit.
2
Detecting hallucinations in large language models using semantic entropy
Nature|2024
626 cit.
3
AI models collapse when trained on recursively generated data
Nature|2024
583 cit.
4
Respecting causality for training physics-informed neural networks
Computer Methods in Applied Mechanics and Engineering|2024
269 cit.
5
Technology readiness levels for machine learning systems
Nature Communications|2022
164 cit.
6
A foundation model for the Earth system
Nature|2025
132 cit.
7
Improved Architectures and Training Algorithms for Deep Operator Networks
Journal of Scientific Computing|2022
118 cit.
8
Long-time integration of parametric evolution equations with physics-informed DeepONets
Journal of Computational Physics|2022
114 cit.
9
Deep Deterministic Uncertainty: A New Simple Baseline
Unknown Journal|2023
86 cit.
10
Supervised Compression for Resource-Constrained Edge Computing Systems
2022 IEEE/CVF Winter Conference on Applications of Computer Vision (WACV)|2022
75 cit.

Current Senior Leaders

Established & currently active

Current Senior Leaders Researchers

1
Donald B. Rubin
Tsinghua University
2
Max Welling
University of California, Irvine
3
Yoshua Bengio
Université de Montréal
4
Adam Lerer
OpenAI (United States)
5
Carl Edward Rasmussen
University of Cambridge
6
George Em Karniadakis
John Brown University
7
ChopinNicolas
Centre de Recherche en Économie et Statistique
8
Anthony O’Hagan
University of Sheffield
9
J. Nathan Kutz
University of Washington Applied Physics Laboratory
10
Michael I. Jordan
University of California, Berkeley

Current Senior Leaders Papers

1
Maximum Likelihood from Incomplete Data Via the <i>EM</i> Algorithm
Journal of the Royal Statistical Society Series B (Statistical Methodology)|1977
49.7k cit.
2
The central role of the propensity score in observational studies for causal effects
Biometrika|1983
31k cit.
3
Latent dirichlet allocation
Journal of Machine Learning Research|2003
27k cit.
4
Statistical Analysis With Missing Data
Journal of the American Statistical Association|1989
17.5k cit.
5
Physics-informed neural networks: A deep learning framework for solving forward and inverse problems involving nonlinear partial differential equations
Journal of Computational Physics|2018
16.9k cit.
6
Inference from Iterative Simulation Using Multiple Sequences
Statistical Science|1992
16.7k cit.
7
Auto-Encoding Variational Bayes
UvA-DARE (University of Amsterdam)|2013
15.6k cit.
8
Generative adversarial networks
Communications of the ACM|2020
13.6k cit.
9
Understanding the difficulty of training deep feedforward neural networks
Unknown Journal|2010
12.7k cit.
10
Automatic differentiation in PyTorch
Unknown Journal|2017
11.1k cit.

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