Dr Shufan Yang

Profile

I am an Associate Professor in Computational Intelligence at the Institute of Medical & Biological Engineering, School of Mechanical Engineering, University of Leeds. I was also appointed as an Honorary Associate Professor in Medical Technologies at the Research Department of Orthopaedics and Musculoskeletal Science, University College London. I obtained my PhD degree in Computer Science from the University of Manchester, UK, in 2010 under the supervision of Professor Steve Furber. I was a Royal Academy of Engineering Industrial Fellow from 2022 to 2023 with Codeplay Software Ltd, the PI for Royal Society of Edinburgh-funded project, and PI for two Innovate UK funded Knowledge Transfer Partnerships Projects and a Co-I for EU FP7 Project.  In the past, I have worked on ANN-based image reconstruction, Edge acceleration for ANN,  deep reinforcement learning for multi-agent systems, system-on-chip for multiple processors, and computational neuroscience modelling, publishing over 60 scientific articles in top-tier journals and conferences, including Nature, IEEE Transactions on Medical Imaging, Parallel Computing, etc. I have served on many research award panels for Innovate UK, EPSRC peer review, Royal Academy of Engineering Research Fellow peer review, Canada Foundation for Innovation, and the Dutch Research Council. I have also been involved in multiple STEM public engagement activities with Nuffield Health and currently serve as a council member of the BCS, the Chartered IT Council.

Research interests

My research focuses on using deep neural network algorithms for optimisation in engineering problems, particularly in translational therapeutics for neurodegenerative disease, the analysis of medical and biomedical images for biomarker extraction, and data-driven healthcare utilising edge machine learning acceleration technologies. My current research projects include developing optimisation for mesh-based Monte Carlo photon simulation software on heteogeneous platforms to benefit from parallel processing capabilities, optimised memory management, thread allocation and customised interactive algorithmic optimisations. I am also working on building pathology and physiology modeling for neurodegenerative disease and using optimisation techniques to improve the performance of physics-informed neural networks and convolutional neural networks for spatial data, refining mesh size for computational models and improving algorithms for numerical solutions. I specialise in hardware-software acceleration for artificial neural network imaging reconstruction in ultrasound imaging, photoacoustic computed tomography, optical coherence tomography, and radar-based human activity recognition using micro-Doppler techniques for resource-limited platforms. 

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 acceleration 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
• FHEA

Student education

• Robotics and Machine Intelligence
• Professional Project

Current postgraduate researchers

• Taskin Md Siham Sayeed