2026 Martin Pelikan: Computer Science Researcher – H-Index, Publications & Awards

Discipline name	D-Index	World Ranking	Current World Ranking	National Ranking	Current National Ranking	Publications	Citations
Computer Science	48	6086	5912	2741	2643	142	12098

Martin Pelikan publication distribution in Computer Science in 2026

The chart shows the distribution of publications by all Research.com ranked scientists in the field of Computer Science in 2026. The highlighted bar marks where Martin Pelikan sits on this spectrum.

32 publications 991+

This scientist: 142 publications — 23rd percentile

23% of scientists in this discipline score the same or lower.

The last bar groups every scientist with 991 publications or more.

Martin Pelikan D-index placement in Computer Science in 2026

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

30 D-Index 131+

This scientist: 48 D-Index — 58th percentile

58% of scientists in this discipline score the same or lower.

The last bar groups every scientist with 131 D-Index or more.

Overview

Martin Pelikan is affiliated with the University of Missouri in the United States and contributes primarily to the field of Computer Science. Their research spans several subfields, including Artificial Intelligence, Signal Processing, and Statistics and Probability.

The main topics covered in their work include:

Privacy-Preserving Technologies in Data
Speech Recognition and Synthesis
Speech and Audio Processing
Music and Audio Processing
Statistical Methods in Clinical Trials
Bayesian Methods and Mixture Models
Statistical Distribution Estimation and Applications

Martin Pelikan has frequently published in the venue arXiv (Cornell University), with at least five papers hosted there. The papers authored or coauthored by them are:

Enabling Differentially Private Federated Learning for Speech Recognition: Benchmarks, Adaptive Optimizers and Gradient Clipping (2023, arXiv)
Importance of Smoothness Induced by Optimizers in FL4ASR: Towards Understanding Federated Learning for End-to-End ASR (2023, arXiv)
An Application of a Multivariate Estimation of Distribution Algorithm to Cancer Chemotherapy (2022, arXiv)
Population Expansion for Training Language Models with Private Federated Learning (2023, arXiv)
pfl-research: simulation framework for accelerating research in Private Federated Learning (2024, arXiv)

Collaboration plays a notable role in their research. Frequent coauthors include:

Sheikh Shams Azam
Jan Silovský
Tatiana Likhomanenko
Congzheng Song
Mona Chitnis

The body of Martin Pelikan's work emphasizes differential privacy and federated learning, especially in the context of speech recognition and language model training. Their investigations extend to the optimization techniques underpinning federated learning systems and their applications in clinical and biomedical domains.

Best Publications

A Survey of Optimization by Building and Using Probabilistic Models

Martin Pelikan;David E. Goldberg;Fernando G. Lobo

2179
Citations
BOA: the Bayesian optimization algorithm

Martin Pelikan;David E. Goldberg;Erick Cantú-Paz

1418
Citations
An introduction and survey of estimation of distribution algorithms

Mark Hauschild;Martin Pelikan

538
Citations
Hierarchical Bayesian Optimization Algorithm

Martin Pelikan

509
Citations
The Bivariate Marginal Distribution Algorithm

Martin Pelikan;Heinz Muehlenbein

470
Citations
Linkage Problem, Distribution Estimation, and Bayesian Networks

Martin Pelikan;David E. Goldberg;Erick E. Cantú-paz

452
Citations
Hierarchical Bayesian Optimization Algorithm - Toward a new Generation of Evolutionary Algorithms

Unknown

339
Citations
Bayesian optimization algorithm: from single level to hierarchy

David E. Goldberg;Martin Pelikan

262
Citations
Hierarchical Bayesian Optimization Algorithm: Toward a New Generation of Evolutionary Algorithms

M. Pelikan;D.E. Goldberg;S. Tsutsui

252
Citations
Scalable Optimization via Probabilistic Modeling

Martin Pelikan;Kumara Sastry;Erick Cantu-Paz

252
Citations
Escaping hierarchical traps with competent genetic algorithms

Martin Pelikan;David E. Goldberg

244
Citations
Scalable optimization via probabilistic modeling : from algorithms to applications

Martin Pelikan;Kumara Sastry;Erick Cantú-Paz

239
Citations
Hierarchical BOA solves ising spin glasses and MAXSAT

Martin Pelikan;David E. Goldberg

188
Citations
Bayesian Optimization Algorithm

Martin Pelikan

179
Citations
Evaluation-Relaxation Schemes for Genetic and Evolutionary Algorithms

Kumara Sastry;Martin Pelikan;Prasanna Parthasarathy;Ravi Srivastava

166
Citations
Method for optimizing a solution set

Martin Pelikan;David E. Goldberg

156
Citations
Scalability of the Bayesian optimization algorithm

Martin Pelikan;Kumara Sastry;David E. Goldberg

156
Citations
Multi-objective bayesian optimization algorithm

Nazan Khan;David E. Goldberg;Martin Pelikan

145
Citations
Multiobjective hBOA, clustering, and scalability

Martin Pelikan;Kumara Sastry;David E. Goldberg

142
Citations
Bayesian optimization algorithm, population sizing, and time to convergence

Martin Pelikan;David E. Goldberg;Erick Cantu-Paz

142
Citations
Hierarchical problem solving and the Bayesian optimization algorithm

Martin Pelikan;David E. Goldberg

140
Citations

Frequent Co-Authors

David E. Goldberg University of Illinois at Urbana-Champaign

Kumara Sastry University of Illinois at Urbana-Champaign

Martin V. Butz University of Tübingen

Erick Cantú-Paz Amazon (United States)

Ashish Ghosh Indian Statistical Institute

Jürgen Branke University of Warwick

Pier Luca Lanzi Polytechnic University of Milan

John A. Tainer The University of Texas MD Anderson Cancer Center

Matthias Troyer Microsoft (United States)

James L. Kennedy Centre for Addiction and Mental Health

External Links

Personal website of Martin Pelikan Google Scholar page

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