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Faculty of Mathematics

 
Research interests: 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–Jul 2024: 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

Inferring networks from time series: A neural approach
T Gaskin, GA Pavliotis, M Girolami
– PNAS Nexus
(2024)
3,
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 multiagent models
T Gaskin, GA Pavliotis, M Girolami
– Proc Natl Acad Sci U S A
(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