OGOliver Gerstl
Oliver Gerstl
*This is an algorithmically generated author record based on publicly available information.
Current Organizations / Institutes
The organizations/institutes from the scholar's latest article
Research Subjects
Oliver Gerstl has a total of 6 co-authors. Here are the top co-authors by number of shared publications:
- Florian Sihler
Ulm University, Ulm, Germany
4 shared publications - Matthias Tichy
Ulm University
4 shared publications - Lars Pfrenger
Ulm University, Ulm, Germany
2 shared publications - Julian Schubert
Ulm University, Ulm, Germany
1 shared publication - Manuel Di Agostino
University of Parma, Parma, Italy
1 shared publication - Vincenzo Arceri
University of Parma, Parma, Italy
1 shared publication
Latest Publications
- Open Access
- 7 October 2026
- Published by Association for Computing Machinery (ACM)
- p. 1446-1456
Inferring the Shape of Data Frames in R Programs using Abstract Interpretation
- 7 October 2026
- Published by Association for Computing Machinery (ACM)
- p. 1446-1456
Open AccessInferring the Shape of Data Frames in R Programs using Abstract Interpretation
- Open Access
- 2 October 2026
- Published by Association for Computing Machinery (ACM)
- p. 18-25
Towards Statically Reasoning about R Vectors
- 2 October 2026
- Published by Association for Computing Machinery (ACM)
- p. 18-25
Open AccessTowards Statically Reasoning about R Vectors
- Open Access
- 5 July 2026
- Published by Association for Computing Machinery (ACM)
- p. 112-116
Supporting the Comprehension of Data Analysis Scripts
- 5 July 2026
- Published by Association for Computing Machinery (ACM)
- p. 112-116
Open AccessSupporting the Comprehension of Data Analysis Scripts
- Open Access
- 12 April 2026
- Published by Association for Computing Machinery (ACM)
- p. 36-40
Towards Automatically Inferring Constraints to Identify Implicit Assumptions in Data Analysis
- 12 April 2026
- Published by Association for Computing Machinery (ACM)
- p. 36-40
Open AccessTowards Automatically Inferring Constraints to Identify Implicit Assumptions in Data Analysis