Research interests: Parameter calibration | Machine Learning & Deep Learning | High-dimensional statistics | Computational Epidemiology | Optimal Transport | Network Modelling | Natural Language Processing
I am a PhD student with Mark Girolami at the Computational Statistics and Machine Learning Group, working on topics combining graph topology and network dynamics, stochastic modelling, and machine learning. I am interested in modelling dynamical systems using neural differential equations and understanding complex behaviour and self-organisation, while spanning a wide area of application, including the spread of contagious diseases, economic activity and optimal transport, power systems modelling, and social network dynamics.
I am a maintainer at the Utopia project, a complex systems modelling framework. My machine learning codebase can be found on GitHub.
Biography:
- Mar–Aug 2024: Visiting research fellow at Zuse Institute/FU Berlin, Germany.
- Sept 2023–Feb 2024: Research assistant at Department of Mathematics, Imperial College London and Department of Mathematics, Warwick University
- Since Jan 2023: Visiting student at Department of Mathematics, Imperial College London.
- Since Sep 2021: PhD Student DAMTP
- Mar–Aug 2021: Guest researcher, Chair of Network Dynamics, Institute of Theoretical Physics, TU Dresden, Germany. Supervisor: Marc Timme.
- 2018–2020: M.Sc. Physics, Heidelberg University. Focus on Complex Systems. Supervisor: Kurt Roth.
- 2018–2019: Visiting student at Peking University, China.
- 2017–2020: B.Sc. Mathematics, Heidelberg University, Germany. Focus on Topology. Supervisor: Markus Banagl.
- 2014–2018: B.Sc. Physics, Heidelberg University, Germany. Focus on Mathematical Physics. Supervisor: Johannes Walcher.
Publications
Neural parameter calibration and uncertainty quantification for epidemic forecasting.
– PLoS ONE
(2024)
19,
e0306704
(doi: 10.1371/journal.pone.0306704)
Inferring networks from time series: A neural approach.
– PNAS nexus
(2024)
3,
pgae063
(doi: 10.1093/pnasnexus/pgae063)
Neural parameter calibration and uncertainty quantification for epidemic
forecasting
(2023)
Neural parameter calibration for large-scale multiagent models
– Proceedings of the National Academy of Sciences of the United States of America
(2023)
120,
e2216415120
(doi: 10.1073/pnas.2216415120)
Neural parameter calibration for large-scale multi-agent models
(2022)
(doi: 10.48550/arxiv.2209.13565)