Forschung
Data-efficient algorithms (active learning) for applications in chemistry
Sommersemester 2026
Molekulares Design
Lebenslauf
- seit 09/2026: Doktorandin in der AG Jacob, TU Braunschweig (Fortführung der Promotion)
- 10/2025 - 11/2025: Forschungsaufenthalt in der AG Gryn'ova, University of Birmingham
- 10/2023 - 09/2026: Doktorandin in der AG Proppe, TU Braunschweig
- 10/2020 - 08/2023: Master of Science, Informatik, TU Braunschweig
Masterarbeit bei Prof. Martin Johns - 10/2017 - 09/2020: Bachelor of Science, Informatik, Universtität Leipzig
Konferenzen und Workshops
- 06/2026: Southern Lower Saxony Theoretical Chemistry Meeting, Hannover
Vortrag: Generative Design of Amines for Sustainable CO2 Capture - 03/2026: Braunschweiger JungeChemieTagung, Braunschweig
Vortrag: Generative Design of Amines for Sustainable CO2 Capture - 03/2026: Chemical Compound Space Conference, München
Poster: Generative Design of Amines for Sustainable CO2 Capture - 10/2025: SusML Workshop - Towards sustainable exploration of chemical spaces with machine learning, Dresden
Poster: Data-efficient Discovery of Amines for Sustainable Post-combustion Carbon Capture - 06/2025: International Conference on Chemical Structures, Noordwijkerhout
Poster: Active Learning for Chemical Space Exploration - 09/2024: STC – Symposium on Theoretical Chemistry, Braunschweig
Poster: Active Learning for Chemical Space Exploration - 05/2024: Chemical Compound Space Conference, Heidelberg
Poster: Active Learning for Chemical Space Exploration
2: Uncertainty Quantification for In Silico Chemistry
Tom Frömbgen, Elizaveta Surzhikova, Jürgen Dölz, Jonny Proppe, Barbara Kirchner, Christoph R. Jacob
Chem. Rev. 2026, 126 (7), 4189
Online VersionPreprint Version
1: regAL: Python Package for Active Learning of Regression Problems
Elizaveta Surzhikova, Jonny Proppe
Mach. Learn.: Sci. Technol. 2025, 6, 025064
Online VersionPreprint Version
