David Tsang

Staff

Department of Physics

Dr David Tsang

Dave works on a broad range of problems in theoretical astrophysics and mathematical physics, such as the physics of neutron stars, black holes, gravitational waves, accretion discs and exoplanetary systems. He is also interested in problems related to (non-Hamiltonian) action principle methods for discrete and continuum systems, and applying geometric methods to develop new analytic and numerical techniques for exploring problems where nonconservative interactions can be important. This includes variational integrators and non-conservative action principles that can be potentially applicable across a wide range of physics, engineering, and applied mathematics.

University of Bath Research Portal:

https://researchportal.bath.ac.uk/en/persons/david-tsang

Second Supervisors

Francesco Aiello

Department for Health

Research interests:

Injury epidemiology, mechanisms, and prevention
Training load, fatigue monitoring, and recovery strategies
Sport technology, data science and their application in elite sport

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Sean Williams

Department for Health

Research Interests

sports injury epidemiology
training load monitoring
youth athlete development

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Edward Milsom

Deep Learning
Optimisation
Bayesian methods

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Thomas Powell

Department of Computer Science

Research interests:

Applications of logic in pure and applied probability.
Stochastic optimization.
Functional analysis.

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Wenbin Li

Department of Computer Science

Research interests:

Reinforcement learning and its inverse problems;
Graph and network optimisation;
Continuous state approximation.

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Georgios Exarchakis

Department of Computer Science

Research interests:

Learning Invariant Representations of High Dimensional Data
Efficient Inference and Learning in Probabilistic Models
Deep Learning

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Rohit Babbar

Department of Computer Science

Research interests:

supervised learning problems with large label spaces,
learning with long-tailed data, and under label/input noise,
sparse neural networks for energy & memory efficient training/fine-tuning with LLMs, and
generalisation/robustness in deep learning.

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