Hi, I hope you’re having a wonderful day! I’m a research-focused mathematician and informatician enthusiastic about modelling intelligent systems. In particular, my research interests include reinforcement learning, model alignment, causal inference, randomised algorithms, and AI4Science. Apart from that, I make a point of pointing out that calling informatics “computer science” would be like calling astrophysics “telescope science”… 😉
One of my favourite formulae is the bias-variance decomposition in least-squares regression with risk \(r(a) = \mathbb{E}[(a(X) - Y)^2]\), computed (randomised) algorithm \(A\), and optimal algorithm \(a^*\):
\[\underbrace{\mathbb{E}[r(A)] - r(a^*)}_{\text{expected excess risk}} = \underbrace{\mathbb{E}\left[(\mathbb{E}[A(X) \mid X] - a^*(X))^2\right]}_{\text{expected square bias}} + \underbrace{\mathbb{E}[\mathrm{Var}[A(X) \mid X]]}_{\text{expected variance}}\]Education
- MSc Informatics, TUM, 2026 (expected)
- exchange stay at UNSW Sydney, 2024
- MSc Mathematical Sciences, Oxford University, 2023
- BSc Computer Science, University of Passau, 2022
- BSc Mathematics, University of Passau, 2022
Work Experience
- currently: Research Assistant, TUM, Munich, Germany
- Summer 2023: ML Engineer Intern, Google, California & New York
- Summer 2022: Software Engineering Intern, Google, San Francisco
- 2018-2022: Self-employed Web Developer, Remote, Germany
Publications
The Merit of River Network Topology for Neural Flood Forecasting
Kirschstein, N., Sun, Y. (2024). The Merit of River Network Topology for Neural Flood Forecasting. In: Proceedings of the 41st International Conference on Machine Learning (ICML'24), Vol. 235. JMLR.org, Article 990, 24713–24725.
Deep Active Learning for Detection of Mercury’s Bow Shock and Magnetopause Crossings
Julka, S., Kirschstein, N., Granitzer, M., Lavrukhin, A., Amerstorfer, U. (2023). Deep Active Learning for Detection of Mercury’s Bow Shock and Magnetopause Crossings. In: Amini, MR., Canu, S., Fischer, A., Guns, T., Kralj Novak, P., Tsoumakas, G. (eds) Machine Learning and Knowledge Discovery in Databases. ECML PKDD 2022. Lecture Notes in Computer Science(), vol 13716. Springer, Cham.
Research Projects
Teaching
Mathematics Tutor
Leading 2x2h of weekly tutorial sessions as part of the lecture "Discrete Probability Theory"
Teaching Assistant
Undergraduate courses "Algorithms and Data Structures" and "Theoretical Computer Science"
Scholarships
- 2020-present: Studienstiftung scholarship of Germany
- 2018-present: Max Weber Programme of Bavaria
- 2022-2023: Joan Protheroe Choral Scholarship from Lincoln College, University of Oxford
- 2022-2023: DAAD fellowship by the German Academic Exchange Service
Service and Leadership
- 2025-present: Regional Representative of the Studienstiftung scholarship holders in Munich, Germany
- 2023-2024: Board Member of UniversitätsChor Munich, Germany
- 2021: Conference Co-organiser, University of Passau, Germany
- 2020: Student Representative in professorship appointments, University of Passau, Germany
- 2020: On-call Social Worker, Bavarian Red Cross, Germany