Nuno Reis

Staff

Department of Chemical Engineering

Dr Nuno Reis
Nuno is interested in modelling of fundamental physical, chemical and biological processes that underpin the robust development and performance of novel disruptive biological and biomedical technologies. For example, using CFD modelling to understand mixing, mass and heat transfer, particles suspension, or bubbly flow in two-phase or multiphase unsteady oscillatory flow reactors and in microfluidic (miniaturised) devices. He also explores antibodyantigen binding, mass transfer and matrix effects in novel microfluidic biosensors through numerical modelling and uses mathematical algorithms for stratifying patients’ outcome based on a biomarkers fingerprinting from human samples – a statistical approach that could speed up adoption of novel microfluidic tests for rapid diagnosis of e.g. sepsis, viral and bacterial infections, acute renal and cardiac injuries.

University of Bath Research Portal

https://researchportal.bath.ac.uk/en/persons/nuno-reis

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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