General Information

I'm a PhD student in the field of data science. In particular, my PhD-related research is about feature selection with constraints. Leveraging constraints, one may select features not only based on their predictive quality but also consider aspects like domain knowledge or interpretability. Thus, constraints can make feature selection more user-centric. 

Additionally, I had and have several collaborations with other researchers from data science and application domains like materials science, process verification, and SAT solving. Many of my teaching and research activities correspond to a GitHub project that contains the code and sometimes further materials.

My publication and the corresponding experimental data are listed in the following. The article "Understanding the Effects of Temporal Energy-Data Aggregation on Clustering Quality", which resulted from my master's thesis, has a dedicated website.


Active Learning for SAT Solver Benchmarking
Fuchs, T.; Bach, J.; Iser, M.
2023. Tools and Algorithms for the Construction and Analysis of Systems. Ed.: S. Sankaranarayanan. Pt. 1, 407–425, Springer Nature Switzerland. doi:10.1007/978-3-031-30823-9_21
Leveraging Constraints for User-Centric Selection of Predictive Features
Bach, J.
2022, October 6. AI Hub @ Karlsruhe (2022), Karlsruhe, Germany, October 5–7, 2022
An Empirical Evaluation of Constrained Feature Selection
Bach, J.; Zoller, K.; Trittenbach, H.; Schulz, K.; Böhm, K.
2022. SN Computer Science, 3 (6), Art.-Nr.: 445. doi:10.1007/s42979-022-01338-z
Presentation for the Paper "A Comprehensive Study of k-Portfolios of Recent SAT Solvers"
Bach, J.
2022, August 2. 25th International Conference on Theory and Applications of Satisfiability Testing (SAT 2022), Haifa, Israel, August 2–5, 2022
A Comprehensive Study of k-Portfolios of Recent SAT Solvers
Bach, J.; Iser, M.; Böhm, K.
2022. 25th International Conference on Theory and Applications of Satisfiability Testing (SAT 2022). Hrsg.: Kuldeep S. Meel, 2:1–2:18, Schloss Dagstuhl - Leibniz-Zentrum für Informatik (LZI). doi:10.4230/LIPIcs.SAT.2022.2
Data-driven exploration and continuum modeling of dislocation networks
Sudmanns, M.; Bach, J.; Weygand, D.; Schulz, K.
2020. Modelling and simulation in materials science and engineering, 28 (6), Art. Nr.: 065001. doi:10.1088/1361-651X/ab97ef
Understanding the effects of temporal energy-data aggregation on clustering quality
Trittenbach, H.; Bach, J.; Böhm, K.
2019. Information technology, 61 (2-3), 111–123. doi:10.1515/itit-2019-0014
On the tradeoff between energy data aggregation and clustering quality
Trittenbach, H.; Bach, J.; Böhm, K.
2018. 9th ACM International Conference on Future Energy Systems, e-Energy 2018; Karlsruhe; Germany; 12 June 2018 through 15 June 2018, 399–401, Association for Computing Machinery (ACM). doi:10.1145/3208903.3212038


I taught the exercises of "Data Science 1" (old name: "Big Data Analytics") three times and the practical course "Data Science Laboratory Course" (old name: "Analyzing Big Data Laboratory Course") five times. I fundamentally re-designed the exercises of "Data Science 1" when acquiring the "Baden-Württemberg Certificate for Teaching and Learning at University Level". Further, I supervised one project each for the courses "Software Engineering in Practice" ("Praxis der Softwareentwicklung"; project topic: "CS:Select -  A Game for Feature Selection in Machine Learning") and "Research Project" ("Praxis der Forschung"; project topic: "Automating SAT Solver Research"). Finally, I supervised three seminar ~, seven bachelor's ~, and three master's theses.

Title Type Semester
Practical course (P) SS 2023
Projektgruppe (Pg) SS 2022
Practical course (P) SS 2022
Projektgruppe (Pg) WS 21/22
Lecture WS 21/22
Practical course (P) SS 2021
Lecture WS 20/21
Practical course (P) SS 2020
Lecture WS 19/20
Seminar (S) WS 19/20
Seminar (S) SS 2019
Practical course (P) SS 2019
Vorlesung (V) WS 18/19
Practical course (P) SS 2018