Dr Shufan Yang

Profile

I am an Associate Professor of Computational Intelligence and Optimisation at the Institute of Medical and Biological Engineering, School of Mechanical Engineering, the 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 Intelligent Systems Research Centre, Ulster University. In 2012, I was appointed as 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 have been awarded a Royal Academy of Engineering Industrial Fellow 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 one EU FP7 project. 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 IT for several years. Moreover, I actively champion on open research initiatives through the evolvement of open hardware foundation.
 

Research interests

I have led my team in pioneering several innovations by applying advanced  computational techniques including artificial neural network (ANN) and other machine learning methods to solve engineering problems. In 2017, I developed the first ANN–based face detection system for unconstrained environments, achieving the fastest reported detection rates that year. I also pioneered the use of ANN for fall prediction using radar. Building on these foundational contributions, I have extended my research to ANN-based methods that facilitate the integration of  multiphysics models, which focusing on studying interaction between mechanical properties and electrical activity. Ultimately it will lead to effective optimisation strategies that consider interactions between mechanical, electrical, and biological processes. 

My research includes but not limited to:

• Biologically inspired intelligent systems for micro-electro-mechanical systems.
• ANN-driven multiscale iterative workflow couples computational modelling for resource limited environment.
• ANN-based real-time image reconstruction for safer and effective interventions.
• ANN-based optimisation for the nonlinear dynamics of 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:

• 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

Dr Yang would be delighted to hear from potential PhD candidates interested in exploring any aspect of biomedical engineering research using AI and edge computing for smart imaging/sensors.
Other funding opportunities for post-graduate students, postdoc researchers and visiting scholars:
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
• MBCS
• Fellow HEA

Student education

• Robotics and Machine Intelligence
• Professional Project

Research groups and institutes

• Leeds Cancer Research Centre

Current postgraduate researchers

• Taskin Md Siham Sayeed