Scholar
Nikolaus Vertovec
Google Scholar ID: GBX1MDcAAAAJ
University of Oxford
Citations & Impact
Citations
32
H-index
4
i10-index
0
Publications
12
Co-authors
0
Publications
Resume (English only)
Academic Achievements
- 2025: 'Certified Neural Approximations of Nonlinear Dynamics'
- 2025: 'SPoRt -- Safe Policy Ratio', IJCAI
- 2025: 'Certified Approximate Reachability (CARe)'
- 2025: Published in 'Ethnic and Racial Studies', 48(9), pp. 1859–1875
- 2024: DPhil thesis 'Optimal control for safety-critical systems'
- 2024: 'Finite sample learning of moving targets'
- 2024: 'Safety-Aware Hybrid Control of Airborne Wind Energy Systems', Journal of Guidance, Control, and Dynamics
- 2023: 'State Aggregation for Distributed Value Iteration in Dynamic Programming', IEEE Control Systems Letters
- 2022: 'Multi-objective low-thrust spacecraft trajectory design using reachability analysis', European Journal of Control
- 2022: 'Verification of safety critical control policies using kernel methods', European Control Conference (ECC)
- 2021: 'Multi-objective minimum time optimal control for low-thrust traj...'
Research Experience
- Postdoctoral Researcher, St Hugh’s College / Department of Computer Science, University of Oxford (Jan 2024–Present): Certified learning and neural network verification
- DPhil Candidate, Department of Engineering Science, University of Oxford (Apr 2020–Apr 2024): Optimal control for safety-critical systems
- Intern, NASA Jet Propulsion Laboratory (Oct 2019–Mar 2020): Worked on the Mars 2020 Rover mission
- GNC Engineer, Akademische Raumfahrtsinitiative Schweiz (ARIS) (Sep 2018–Jul 2019): Designed control systems for a sounding rocket
- Teaching Assistant, ETH Zürich (Feb 2019–Jul 2019): Taught Numerical Methods to second-year bachelor students
Background
- Career Development Fellow in Artificial Intelligence at St Hugh’s College, University of Oxford
- Member of the Oxford Control & Verification group, Department of Computer Science, University of Oxford
- Research focuses on certified learning and learning for verification, with an emphasis on certified AI for Science
- Research interests include physics-informed machine learning, finite-sample learning, certified learning, and learning for verification
- Applied research to safety verification of uncertain dynamical systems, including spacecraft trajectory design and airborne wind energy
Co-authors