Dr. David Wittenberg

 

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Office hours:		 01/263 (ReWi I)
+49 6131 39 - 27556
+49 6131 39 - 22185
wittenberg@uni-mainz.de
Tuesday, 11:00 a.m. - 12:00 p.m.Linkedin Profile
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Last update: August 16, 2023

Research Interests

  • Machine learning, in particular Artificial Neural Networks
  • Genetic Programming

Teaching

  • Lecturer of the preparation course for the SAP Business Process Integration with S/4HANA consultant certification - Sept'/Oct'20, Sept'/Oct'21,
  • Tutor for the lecture "Computational Intelligence" - ST'19, ST'20
  • Coordinate the tutorial for the lecture "Introduction to Business Informatics" - WT'19/20, WT'20/21, WT'21/22, WT'22/23
  • Coordinate the tutorial for the lecture "Web technologies and E-Business" - WT'18/19, WT'19/20
  • Supervise Bachelor and Master theses as well as Bachelor and Master seminar work- since WT'18/19
  • Tutor for the lecture "Mathematics" – WT'17/18

CV

  • Since 07/2018
 Researcher and Doctoral Candidate at the Chair of Information Systems & Business Administration, Prof. Dr. Franz Rothlauf (JGU Mainz, Germany)
  • 04/2022 - 06/2022
 Visiting Researcher at the Chair of Artificial Intelligence, Prof. Dr. Christian Gagné (Université Laval, Quebéc City, Canada)
  • 10/2015 - 05/2018
 M.Sc. Management (JGU Mainz, Germany)
  • 01/2017 - 04/2017
 Intern at BASF Ludwigshafen, Germany
  • 09/2016 - 12/2016
 Non-profit volunteer at coffee company in La Laguna, Nicaragua
  • 04/2012 - 07/2015
 B.Sc. Management and Economics (JGU Mainz, Germany) & Licence mention Sciences de Gestion (Université Paris X, France), Franco-German Double-Degree
  • 03/2014 - 06/2014
 Intern at Audi Brussels, Belgium

Publications

2023

Reiter, J., Schweim, D., & Wittenberg, D. (2023). Pretraining Reduces Runtime in Denoising Autoencoder Genetic Programming by an Order of Magnitude. Proceedings of the Companion Conference on Genetic and Evolutionary Computation, 2382-2385. DOI
Wittenberg, D., & Rothlauf, F. (2023). Small Solutions for Real-World Symbolic Regression Using Denoising Autoencoder Genetic Programming (Vols 13986, pp. 101-116). DOI
Wittenberg, D., Rothlauf, F., & Gagne, C. (2023). Denoising autoencoder genetic programming: strategies to control exploration and exploitation in search. GENETIC PROGRAMMING AND EVOLVABLE MACHINES, 24(2). DOI

2022

Sobania, D., Briesch, M., Wittenberg, D., & Rothlauf, F. (2022). Analyzing Optimized Constants in Genetic Programming on a Real-World Regression Problem. PROCEEDINGS OF THE 2022 GENETIC AND EVOLUTIONARY COMPUTATION CONFERENCE COMPANION, GECCO 2022, 606-607. DOI
Wittenberg, D. (2022). Using Denoising Autoencoder Genetic Programming to Control Exploration and Exploitation in Search (pp. 102-117). DOI
Wittenberg, D., & Rothlauf, F. (2022). Denoising Autoencoder Genetic Programming for Real-World Symbolic Regression. PROCEEDINGS OF THE 2022 GENETIC AND EVOLUTIONARY COMPUTATION CONFERENCE COMPANION, GECCO 2022, 612-614. DOI

2021

Olmscheid, C., Wittenberg, D., Sobania, D., & Rothlauf, F. (2021). Improving Estimation of Distribution Genetic Programming with Novelty Initialization. Proceedings of the Genetic and Evolutionary Computation Conference Companion, 261-262. DOI
Schweim, D., Wittenberg, D., & Rothlauf, F. (2021). On sampling error in evolutionary algorithms. Proceedings of the Genetic and Evolutionary Computation Conference Companion, 43-44. DOI
Schweim, D., Wittenberg, D., & Rothlauf, F. (2021). On sampling error in genetic programming. Natural Computing. DOI

2020

Wittenberg, D., Rothlauf, F., & Schweim, D. (2020). DAE-GP: denoising autoencoder LSTM networks as probabilistic models in estimation of distribution genetic programming. Proceedings of the 2020 Genetic and Evolutionary Computation Conference, 1037-1045. DOI