Scholar
Yunpeng Li
Google Scholar ID: JzyKdRUAAAAJ
Reader in AI & Digital Oral Health, King's College London
Citations & Impact
Citations
707
H-index
15
i10-index
20
Publications
20
Co-authors
12
list available
Publications
Resume (English only)
Academic Achievements
- - First place in each of the three tasks on Cone Beam Computed Tomography (CBCT) scan segmentation in the MICCAI 2025 ODIN challenges
- - Paper 'Differentiable Interacting Multiple Model Particle Filtering' accepted for publication in Signal Processing
- - Projects include AI-assisted dental disease detection with radiography (supported by NIHR i4i Product Development Award) and large-scale acoustic monitoring for invasive insect species (supported by NERC Innovation in Environmental Monitoring programme)
- - Founded dental AI spinout, BeCertain, named the 'most investor-ready' company in the MedTech group in BIA PULSE 2025
- - Invited talks at various international conferences, such as the 2025 Bellairs Workshop on Machine Learning and Statistical Signal Processing for Data on Graphs
Research Experience
- - Postdoctoral Researcher, Machine Learning Research Group, University of Oxford, 2017-2018
- - Faculty Member, Computer Science Research Centre, University of Surrey, 2018-2024
- - Currently, Reader in AI & Digital Oral Health, King’s College London, leading the Translational AI Research (TAIR) Lab
Education
- - Ph.D. in Electrical Engineering, Department of Electrical and Computer Engineering, McGill University, Montréal, Canada, 2017 (Advisor: Mark Coates)
- - Junior Research Fellow, Wolfson College, and Postdoctoral Researcher with Steve Roberts in the Machine Learning Research Group, University of Oxford, 2017-2018
Background
- Dr. Yunpeng Li is a Reader in AI & Digital Oral Health at King’s College London. He leads the Translational AI Research (TAIR) Lab and is a member of the Centre for Oral, Clinical & Translational Sciences in the Faculty of Dentistry, Oral & Craniofacial Sciences. His research applies statistical machine learning and Bayesian statistics to a wide range of domains, including clinical and environmental AI applications. He is particularly interested in developing novel machine learning methodologies and products to solve unmet needs in dental and oral health, biodiversity, and computational statistics.
