Dr Yingjing Feng PhD

• PhD in Applied Mathematics and Scientific Computing, University of Bordeaux, 2021
• MSc in Computing (Machine Learning), Distinction, Imperial College London, 2016
• BSc in Computer Science, First-Class Honours, University of Birmingham, 2015
• BEng in Software Engineering, Sun Yat-Sen University,  2015

During her MSc study at Imperial College London in 2015 to 2016, Yingjing developed a strong interest in applying machine learning to biomedical applications, while working on developing novel techniques to automate catheter ablation therapies using machine learning with Dr Su-Lin Lee, which was awarded a Distinguished MSc Project Prize.

In 2018, she began her PhD study in Applied Mathematics and Scientific Computing at the University of Bordeaux with Dr Edward Vigmond, under European Union’s Marie Skłodowska-Curie Horizon 2020 Innovative Training Network - Personalised In-silico Cardiology (PIC). During her PhD, she was awarded a Rosanna Degani Young Investigator Award at the Computing in Cardiology Conference (2019, Singapore). In 2021, she defended her PhD thesis entitled "Spatiotemporal machine learning on scanner-free body surface potential imaging aided by multiscale modeling for personalized atrial fibrillation treatment".

In March 2022, she joined the University of Birmingham to start working as a Research Fellow with Prof. John Terry at the Centre for Systems Modelling and Quantitative Biomedicine. She is investigating novel mathematical models for modelling seizure susceptibility in patients. She is also actively involved with the SMQB seedcorn projects, including deep learning methods for early detection of psychosis using neuroinflammatory biomarkers and brain imaging.

Module lecturer for LH Neural Computation / LH Neural Computation (extended), 2024/2025. 

Yingjing is actively involved in the supervision of Master’s student projects, providing guidance and mentorship to students as they navigate their research. She is particularly passionate about exploring the intersection of deep learning and applied mathematics.

Yingjing is interested in combining interpretable statistical modelling with mathematical modelling to improve treatment of epilepsy, cardiac arrhythmia and other pathologies. Her current research includes:

• Hybrid modelling techniques that combine statical modelling or deep learning with mathematical models.
• Spatiotemporal machine learning (including deep learning) on ECG,  and EGG EEG and fMRI recordings.
• Pattern discovery and survival analysis of clinical treatment.
• Data assimilation of mathematical models with patient data.
• Validation of novel mapping and treatment methods

Research groups and centres

• Centre for Systems Modelling and Quantitative Biomedicine

Yingjing is actively involved in contributing to research communities through organizing and participating in key academic events, in addition to her conference presentations:

• In May 2023, she co-organized a 12-session mini-symposium titled "Phase Transitions in Electrophysiological Systems" in collaboration with Dr. Aravind Kumar Kamaraj at the SIAM-Dynamical Systems Conference 2023 in Portland, United States.
• In June 2023, she organized the "Early Career Researcher Symposium" during the Perturbations in Epilepsy Workshop in Birmingham, United Kingdom.
• In Jan 2023, she attended the first edition of “Retreat for Women in Applied Mathematics” in Edinburgh, United Kingdom.