Chris Bowen

Staff

Department of Mechanical Engineering

Professor Chris Bowen

Chris’s research areas include functional materials and composites, which include ferroelectrics and shape memory alloys. Ferroelectric materials have a polarisation that changes with stress or temperature and can therefore convert mechanical motion or temperature fluctuations into an electrical signal. This research is aimed at developing novel sensors or energy harvesting systems capable of converting ambient vibrations or heat into useful electrical energy. Ferroelectrics and shape memory alloys can also be used to provide a shape change, so are being examined for actuation or morphing structures.

University of Bath Research Portal:

https://researchportal.bath.ac.uk/en/persons/chris-bowen/

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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