2026 Nicolai Meinshausen: Mathematics Researcher – H-Index, Publications & Awards

Discipline name	D-Index	World Ranking	Current World Ranking	National Ranking	Current National Ranking	Publications	Citations
Mathematics	38	2266	2007	38	36	96	16219

Nicolai Meinshausen publication distribution in Mathematics in 2026

The chart shows the distribution of publications by all Research.com ranked scientists in the field of Mathematics in 2026. The highlighted bar marks where Nicolai Meinshausen sits on this spectrum.

42 publications 537+

This scientist: 96 publications — 9th percentile

9% of scientists in this discipline score the same or lower.

The last bar groups every scientist with 537 publications or more.

Nicolai Meinshausen D-index placement in Mathematics in 2026

The chart shows the D-index (discipline H-index) distribution of Mathematics scientists ranked by Research.com in 2026. The highlighted bar marks where Nicolai Meinshausen sits on this spectrum.

30 D-Index 86+

This scientist: 38 D-Index — 37th percentile

37% of scientists in this discipline score the same or lower.

The last bar groups every scientist with 86 D-Index or more.

Research.com Recognitions

2016 - COPSS Presidents' Award For fundamental and ingenious contributions to high-dimensional inference, variable selection, multiple testing, graphical models, machine learning and causal inference for heterogeneous data.

Overview

Nicolai Meinshausen is affiliated with ETH Zurich in Switzerland and has a research profile that spans multiple scientific disciplines, primarily focused on computer science and mathematics. Their work incorporates substantial contributions to artificial intelligence, statistics and probability, as well as atmospheric science and global planetary change.

The scientific topics covered in Meinshausen's research include:

Statistical Methods and Inference
Statistical Methods and Bayesian Inference
Climate Variability and Models
Machine Learning in Healthcare
Meteorological Phenomena and Simulations
Bayesian Modeling and Causal Inference
Sepsis Diagnosis and Treatment

Recent research publications by Meinshausen and collaborators illustrate the breadth of their work across climate science, statistics, and healthcare. Notable papers include:

"The shared socio-economic pathway (SSP) greenhouse gas concentrations and their extensions to 2500" (2020), published in Geoscientific Model Development
"Climate change now detectable from any single day of weather at global scale" (2020), published in Nature Climate Change
"Anchor Regression: Heterogeneous Data Meet Causality" (2021), published in Journal of the Royal Statistical Society Series B (Statistical Methodology)
"Predicting sepsis using deep learning across international sites: a retrospective development and validation study" (2023), published in EClinicalMedicine
"Geological Net Zero and the need for disaggregated accounting for carbon sinks" (2024), published in Nature

Meinshausen collaborates frequently with several researchers, indicating a diverse network of co-authorship. These frequent co-authors include:

Peter Bühlmann
Sebastian Sippel
Reto Knutti
Nicolas Bennett
Drago Plečko

The scientist's work is regularly published in a variety of academic venues, with several recurring publication platforms:

arXiv (Cornell University)
Journal of the Royal Statistical Society Series B (Statistical Methodology)
Repository for Publications and Research Data (ETH Zurich)
Geoscientific Model Development
Nature

Meinshausen was awarded the COPSS Presidents' Award in 2016 for contributions to high-dimensional inference, variable selection, multiple testing, graphical models, machine learning, and causal inference for heterogeneous data.

Best Publications

High-dimensional graphs and variable selection with the Lasso

Nicolai Meinshausen;Peter Bühlmann

4599
Citations
Stability selection

Unknown

2852
Citations
Quantile Regression Forests

Nicolai Meinshausen

1826
Citations
LASSO-TYPE RECOVERY OF SPARSE REPRESENTATIONS FOR HIGH-DIMENSIONAL DATA

Nicolai Meinshausen;Bin Yu

1106
Citations
Causal inference by using invariant prediction: identification and confidence intervals

Jonas Peters;Peter Bühlmann;Nicolai Meinshausen

720
Citations
Historical greenhouse gas concentrations for climate modelling (CMIP6)

Malte Meinshausen;Malte Meinshausen;Elisabeth Vogel;Alexander Nauels;Katja Lorbacher

632
Citations
p-Values for High-Dimensional Regression

Nicolai Meinshausen;Lukas Meier;Peter Bühlmann

497
Citations
High-Dimensional Inference: Confidence Intervals, $p$-Values and R-Software hdi

Ruben Dezeure;Peter Bühlmann;Lukas Meier;Nicolai Meinshausen

346
Citations
Estimating the proportion of false null hypotheses among a large number of independently tested hypotheses

Nicolai Meinshausen;John Rice

243
Citations
MONTE CARLO METHODS FOR THE VALUATION OF MULTIPLE‐EXERCISE OPTIONS

Nicolai Meinshausen;B.M. Hambly

225
Citations
Hierarchical testing of variable importance

Nicolai Meinshausen

205
Citations
Invariant Causal Prediction for Nonlinear Models

Christina Heinze-Deml;Jonas Peters;Nicolai Meinshausen

201
Citations
Causal Structure Learning

Christina Heinze-Deml;Marloes H. Maathuis;Nicolai Meinshausen

180
Citations
Methods for causal inference from gene perturbation experiments and validation.

Nicolai Meinshausen;Alain Hauser;Joris M. Mooij;Jonas Peters

147
Citations
Anchor regression: heterogeneous data meets causality

Dominik Rothenhäusler;Nicolai Meinshausen;Peter Bühlmann;Jonas Peters

144
Citations
Node harvest

Nicolai Meinshausen

134
Citations
Stability Selection

Nicolai Meinshausen;Peter Buehlmann

122
Citations
Conditional variance penalties and domain shift robustness

Christina Heinze-Deml;Nicolai Meinshausen

110
Citations
Discussion: A tale of three cousins: Lasso, L2Boosting and Dantzig

N. Meinshausen;G. Rocha;B. Yu

109
Citations
Anchor regression: heterogeneous data meets causality

Dominik Rothenhäusler;Peter Bühlmann;Nicolai Meinshausen;Jonas Peters

105
Citations
Uncovering the Forced Climate Response from a Single Ensemble Member Using Statistical Learning

Sebastian Sippel;Nicolai Meinshausen;Anna Merrifield;Flavio Lehner

95
Citations
Sign-constrained least squares estimation for high-dimensional regression

Nicolai Meinshausen

91
Citations
CAUSALITY FROM A DISTRIBUTIONAL ROBUSTNESS POINT OF VIEW

Nicolai Meinshausen

54
Citations

Frequent Co-Authors

Peter Bühlmann ETH Zurich

Malte Meinshausen University of Melbourne

Jonas Peters ETH Zurich

Flavio Lehner Cornell University

Reto Knutti ETH Zurich

David J. Frame Victoria University of Wellington

Erich M. Fischer ETH Zurich

Myles R. Allen University of Oxford

John S. Daniel National Oceanic and Atmospheric Administration

Stephen A. Montzka National Oceanic and Atmospheric Administration

External Links

Personal website of Nicolai Meinshausen Google Scholar page

If you think any of the details on this page are incorrect, let us know.

Related Online Degrees & Career Pathways

For students studying Mathematics in the USA, exploring related online degrees can open diverse career opportunities. Many professionals look to complement their mathematical skills with business knowledge. Programs like the easiest MBA online offer a flexible path to gain essential management expertise without overwhelming coursework.

Those interested in advancing to senior leadership roles might consider a Doctor of Business Administration (DBA). Finding the cheapest online DBA can make this degree more accessible, helping students balance cost with quality education.

Mathematics graduates who want to venture into finance can benefit from specialized degrees, such as a master’s in finance. Numerous programs provide options with a strong focus on quantitative analysis, with some of the masters in finance online programs standing out for their affordability and practical curriculum.

For those aiming to accelerate their career trajectory, accelerated online MBA programs allow students to earn their degree in less time, blending rigorous business training with the analytical skills gained from a mathematics background.