Carl Sangan

Staff

Department of Mechanical Engineering

Professor Carl Sangan

Carl’s research focuses on improving the efficiency of gas turbines for power generation and aerospace engines, with a particular interest in modelling the fluid mechanics and heat transfer inside rotating systems. Through theoretical and experimental studies he employs the approach that investigations into the underpinning fundamental science are key to understanding the complex rotating disc systems found within these turbomachines. The insight from this research informs the design of future engines and influences the design methods employed by engine designers.

University of Bath Research Portal:

https://researchportal.bath.ac.uk/en/persons/carl-sangan/

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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