Dr Shufan Yang

Profile

I am an Associate Professor of Optimisation and Computational Intelligence at the Institute of Medical and Biological Engineering, School of Mechanical Engineering, University of Leeds, and an Honorary Associate Professor in Medical Technologies at University College London. I completed my PhD under the supervision of Professor Steve Furber at the University of Manchester, followed by post-doctoral research at the Intelligent Systems Research Centre, Ulster University. In 2012, I was appointed as a Lecturer in Computational Modelling. My research focuses on machine learning based optimisation for the nonlinear dynamics of electromechanical and biomechanical systems, with a particular emphasis on enhancing real-time sensor data processing to better understand neurology and cardiovascular disease.
I was awarded a Royal Academy of Engineering Industrial Fellowship in 2022. In addition, I serve as Principal Investigator (PI) for two Innovate UK funded Knowledge Transfer Partnerships Projects with Scot Young Research Ltd and Codeplay Software Ltd. I was the Co-I for a Dstl project and EU FP7 projects. I am currently an active member of the EU COST action: Stochastic Differential Equations: Computation, Inference, Applications (STOCHASTICA) and Edge Deep Learning for Particle Physics (EPIGRAPHY). I am also actively involved in STEM public engagement activities with Nuffield Health and currently serve as a Council Member of the BCS, the Chartered Institute for IT. Moreover, I am an active champion of open research initiatives through the involvement with Open Hardware Foundation and MLcommons.

Research interests

I lead a research program focused on optimisation of electromechanical and biomechanical systems through advanced computational intelligence. My team applies artificial neural networks (ANNs) and machine learning methods to develop novel optimisation strategies for coupled multiphysics problems. Building upon foundational work in real-time pattern recognition—including the fastest ANN-based face detection system for unconstrained environments (2017) and pioneering radar-based fall prediction—I have directed my research toward the core challenge of electromechanical and biomechanical optimisation. My current work develops ANN-based methodologies for integrating multiphysics models that capture the complex interplay between mechanical properties and electrical activity, providing new approaches for designing and controlling advanced electromechanical and biomechanical systems.

My research includes but not limited to:

• Biologically inspired optimisation for micro-electro-mechanical systems.
• ANN-driven multiscale iterative workflow couples computational modelling for resource limited environment.
• Real-time optimisation of biomechanical interventions via ANN-based image reconstruction.
• ANN-based optimisation for the nonlinear dynamics in coupled electromechanical and biomechanical systems.

I advocate for open science, particularly in developing digital tissue phantoms with IPASC standardised image reconstruction project.

I am also passionate about open hardware and have participated in competitions for the last decade. My past winning projects include:

• 2025 AMD open hardward runer-up for Edge Implementation of Artificial Neural Networks for fNIRS application
  2024 Prize Winner for Pervasive AI developer contest with AMD
• 2023 AI PHD Prize Winner fNIR motion artification detection
• 2019 PYNQ Finalists White blood cell microscopic image classification
• 2016 Xilinx XPU openhardware Real-time open access image processing platform

I would love to hear from you if you're interested in pursuing PhD research in biomedical engineering with a focus on AI and edge computing for smart materials and sensors! Whether you have specific ideas already or want to discuss possibilities, please get in touch. I'm always happy to explore how we might work together. I also welcome applications for funded positions from:

Commonwealth Scholarships
EU Marie Sklodowska-Curie Fellowships: Home - Marie Skłodowska-Curie Actions
Newton Fellowships

Qualifications

• PhD in Computer Science
• MEng in EECS
• BEng in EECS

Professional memberships

• Charted Engineer (CEng)
• SMIEEE
• Fellow BCS
• Fellow HEA

Student education

• Design Optimisation
• Robotics and Machine Intelligence
• Professional Project

Research groups and institutes

• Leeds Cancer Research Centre

Current postgraduate researchers

• Taskin Md Siham Sayeed
• James Baxter Chapman