CV

Education

PhD in Machine Learning at the University of Cambridge, 2021-2026
MSc in Mathematical Sciences at the University of Oxford, 2020-2021
BSc in Mathematics at the University College London, 2017-2020

Publications

Interpretable DNA Sequence Classification via Dynamic Feature Generation in Decision Trees

Huynh N., Kacprzyk, K., Sheridan, R. M., Bentley, D. L., & van der Schaar, M. (2026). Interpretable DNA Sequence Classification via Dynamic Feature Generation in Decision Trees. International Conference on Artificial Intelligence and Statistics. PMLR.

Skip the Equations: Learning Behavior of Personalized Dynamical Systems Directly From Data

Kacprzyk, K., Piskorz J. & van der Schaar, M. (2025). Skip the Equations: Learning Behavior of Personalized Dynamical Systems Directly From Data. International Conference on Machine Learning.

Beyond Size-Based Metrics: Measuring Task-Specific Complexity in Symbolic Regression

Kacprzyk, K., & van der Schaar, M. (2025). Beyond Size-Based Metrics: Measuring Task-Specific Complexity in Symbolic Regression. International Conference on Artificial Intelligence and Statistics. PMLR.

No Equations Needed: Learning System Dynamics Without Relying on Closed-Form ODEs

Kacprzyk, K., & van der Schaar, M. (2025). No Equations Needed: Learning System Dynamics Without Relying on Closed-Form ODEs. International Conference on Learning Representations.

Self-Healing Machine Learning: A Framework for Autonomous Adaptation in Real-World Environments

Rauba, P., Seedat, N., Kacprzyk, K., & van der Schaar, M. Self-Healing Machine Learning: A Framework for Autonomous Adaptation in Real-World Environments. The Thirty-eighth Annual Conference on Neural Information Processing Systems.

Causal Deep Learning: Encouraging Impact on Real-world Problems Through Causality

Berrevoets, J., Kacprzyk, K., Qian, Z., & van der Schaar, M. (2024). Causal Deep Learning: Encouraging Impact on Real-world Problems Through Causality. Foundations and Trends® in Signal Processing, 18(3), 200-309.

Towards Transparent Time Series Forecasting

Kacprzyk, K., Liu, T., & van der Schaar, M. (2024) Towards Transparent Time Series Forecasting. International Conference on Learning Representations.

Shape Arithmetic Expressions: Advancing Scientific Discovery Beyond Closed-Form Equations

Kacprzyk, K., & van der Schaar, M. (2024). Shape Arithmetic Expressions: Advancing Scientific Discovery Beyond Closed-Form Equations. International Conference on Artificial Intelligence and Statistics. PMLR.

ODE Discovery for Longitudinal Heterogeneous Treatment Effects Inference

Kacprzyk*, K., Holt*, S., Berrevoets*, J., Qian, Z., & van der Schaar, M. (2024) ODE Discovery for Longitudinal Heterogeneous Treatment Effects Inference. International Conference on Learning Representations.

D-CIPHER: Discovery of Closed-Form Partial Differential Equations

Kacprzyk, K., Qian, Z., & van der Schaar, M. (2023). D-CIPHER: Discovery of Closed-Form Partial Differential Equations. The Thirty-seventh Conference on Neural Information Processing Systems.

D-CODE: Discovering Closed-form ODEs from Observed Trajectories

Qian, Z., Kacprzyk, K., & van der Schaar, M. (2022) D-CODE: Discovering Closed-form ODEs from Observed Trajectories. International Conference on Learning Representations.