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Faculty of Mathematics

 

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 self-organisation, spanning a wide area of application, including social network dynamics, the spread of contagious diseases, and economic activity. 

I am a maintainer at the Utopia project, a complex systems modelling framework.

Biography

Jan–Jul 2023: Visiting student at Department of Mathematics, Imperial College London, supervised by Prof. G. Pavliotis

Since Sep 2021: PhD Student DAMTP

Mar–Aug 2021: Guest researcher, Chair of Network Dynamics, Institute of Theoretical Physics, TU Dresden, Germany

2018–2020: M.Sc. Physics, Heidelberg University. Focus on Complex Systems.

2018–2019: Visiting student at Peking University, China

2017–2020: B.Sc. Mathematics, Heidelberg University, Germany. Focus on Topology.

2014–2018: B.Sc. Physics, Heidelberg University, Germany. Focus on Mathematical Physics.

Publications

Inferring networks from time series: A neural approach
T Gaskin, GA Pavliotis, M Girolami
– PNAS Nexus
(2024)
pgae063
Neural parameter calibration and uncertainty quantification for epidemic forecasting
T Gaskin, T Conrad, GA Pavliotis, C Schütte
(2023)
Inferring networks from time series: a neural approach
T Gaskin, GA Pavliotis, M Girolami
(2023)
Neural parameter calibration for large-scale multi-agent models
T Gaskin, GA Pavliotis, M Girolami
– Proceedings of the National Academy of Sciences
(2023)
120,
e2216415120
Neural parameter calibration for large-scale multi-agent models
T Gaskin, GA Pavliotis, M Girolami
(2022)

Research Groups

Mathematics of Information (Applied)
Statistical Laboratory

Room

D0.15

Telephone

01223 760370