World's Best Scientists 2026 revealed!

D-Index & Metrics

Computer Science

D-Index
48
Citations
12098
World Ranking
6085
National Ranking
2740

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

  • BOA: the Bayesian optimization algorithm

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

  • An introduction and survey of estimation of distribution algorithms

    Mark Hauschild;Martin Pelikan

  • Hierarchical Bayesian Optimization Algorithm

    Martin Pelikan

  • The Bivariate Marginal Distribution Algorithm

    Martin Pelikan;Heinz Muehlenbein

  • Linkage Problem, Distribution Estimation, and Bayesian Networks

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

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

    Unknown

  • Bayesian optimization algorithm: from single level to hierarchy

    David E. Goldberg;Martin Pelikan

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

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

  • Scalable Optimization via Probabilistic Modeling

    Martin Pelikan;Kumara Sastry;Erick Cantu-Paz

  • Escaping hierarchical traps with competent genetic algorithms

    Martin Pelikan;David E. Goldberg

  • Scalable optimization via probabilistic modeling : from algorithms to applications

    Martin Pelikan;Kumara Sastry;Erick Cantú-Paz

  • Hierarchical BOA solves ising spin glasses and MAXSAT

    Martin Pelikan;David E. Goldberg

  • Bayesian Optimization Algorithm

    Martin Pelikan

  • Evaluation-Relaxation Schemes for Genetic and Evolutionary Algorithms

    Kumara Sastry;Martin Pelikan;Prasanna Parthasarathy;Ravi Srivastava

  • Method for optimizing a solution set

    Martin Pelikan;David E. Goldberg

  • Scalability of the Bayesian optimization algorithm

    Martin Pelikan;Kumara Sastry;David E. Goldberg

  • Multi-objective bayesian optimization algorithm

    Nazan Khan;David E. Goldberg;Martin Pelikan

  • Multiobjective hBOA, clustering, and scalability

    Martin Pelikan;Kumara Sastry;David E. Goldberg

  • Bayesian optimization algorithm, population sizing, and time to convergence

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

  • Hierarchical problem solving and the Bayesian optimization algorithm

    Martin Pelikan;David E. Goldberg

Frequent Co-Authors

David E. Goldberg
David E. Goldberg University of Illinois at Urbana-Champaign
Kumara Sastry
Kumara Sastry University of Illinois at Urbana-Champaign
Martin V. Butz
Martin V. Butz University of Tübingen
Erick Cantú-Paz
Erick Cantú-Paz Amazon (United States)
Ashish Ghosh
Ashish Ghosh Indian Statistical Institute
Jürgen Branke
Jürgen Branke University of Warwick
Pier Luca Lanzi
Pier Luca Lanzi Polytechnic University of Milan
John A. Tainer
John A. Tainer The University of Texas MD Anderson Cancer Center
Matthias Troyer
Matthias Troyer Microsoft (United States)
James L. Kennedy
James L. Kennedy Centre for Addiction and Mental Health

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

Report an issue

We appreciate your kind effort to assist us to improve this page, it would be helpful providing us with as much detail as possible in the text box below:

Related Online Degrees & Career Pathways

Exploring online education opens up a wide range of possibilities for advancing your career in computer science and related fields. Many students start with the fastest associates degree options, enabling them to quickly gain marketable skills and enter the workforce or pursue further study.

Those looking for affordable and flexible graduate programs can benefit from the cheapest masters degrees available online. These programs often offer specializations in tech, data analytics, and business, making them suitable for aspiring computer science professionals.

For individuals eyeing leadership or academic positions, online doctoral programs have become increasingly popular. The organizational leadership PhD programs are ideal for those focusing on management and innovation, while an Ed D degree prepares graduates for educational or administrative roles in technology-driven environments.

Comparing these online pathways allows you to choose the academic route that best fits your goals, timeline, and budget, providing flexible opportunities for career advancement in computer science or related sectors.

Best Scientists Citing Martin Pelikan

Trending Scientists