I am a Senior Lecturer in Artificial Intelligence in the Department of Computer Science and a member of the Nature Inspired Computing and Engineering (NICE) Group. Prior to joining Surrey as a Lecturer in AI in 2018, I was a Postdoctoral Research Assistant in Machine Learning working with Steve Roberts in the Machine Learning Research Group, University of Oxford (2017-2018). I was also a Junior Research Fellow at Wolfson College, University of Oxford (2018). I completed my Ph.D. in electrical engineering with Mark Coates at McGill University in Montréal, Canada in 2017.
My research interests are in the design and development of machine learning solutions that improve human life. My ongoing and past research efforts particularly focus on high-dimensional, data-adaptive uncertainty quantification methodologies, as well as applications in the areas of AI for health such as dental disease detection with radiography, breast cancer detection with RF sensors, and device-free target tracking in sensor networks. My work on malaria-vectoring mosquito detection using low-cost mobile phones has received media coverage from BBC, the Guardian, New Scientist, Digital Trends, and MIT Technology Review, among other venues.
Areas of specialism
[2022.6] We are hiring! A 4-year fully-funded PhD studentship for UK and Pre-settled EU students on Statistical machine learning for medical imaging is available for October 2022 or January, April, July 2023 start. The application is rolling-based, so do apply early if you are interested.
[2022.6] Congratulations to George Papagiannis whose paper Imitation Learning with Sinkhorn Distances is accepted by ECML 2022! The work was derived from George's final-year undergraduate project at Surrey before he moved to Cambridge for an MSc study and Imperial for PhD. It was fantastic experience working with George - looks like George felt the same.
[2022.5] Xiongjie and I presented our recent work on normalising flow-based differentiable particle filters (for learning dynamic models and proposal distributions and for learning measurement models) in the 5th Workshop on Sequential Monte Carlo Methods in Madrid, Spain.
[2022.4] Wickham Wenhan Li joined the group as a PhD student. Welcome Wickham!
[2022.4] We are hiring! New fully-funded PhD studentship for 3.5 years available on Machine learning meets sequential Monte Carlo methods for July, October 2022 or Jan, April, July 2023 start. This is a collaboration with National Physical Laboratory (NPL) and will involve a secondment for at least three months in the Data Science Department at NPL.
[2022.2] We are organising a Special Session on ''Machine Learning for Sequential Monte Carlo Methods" at EUSIPCO 2022. Submission deadline extended to 7th March 2022.
[2022.1] One paper Augmented Sliced Wasserstein Distances was accepted by ICLR2022. Congratulations to Xiongjie and Yongxin!
[2022.1] Became a Surrey AI Fellow at the Surrey Institute for People-Centred AI.
[2021.12] Talk at 2021 Bellairs Workshop on Machine Learning and Statistical Signal Processing for Data on Graphs in the Bellairs Research Institute in Barbados.
[2021.12] Talk at CIDFIC Innovation Meetup.
[2021.12] We are hiring! Two PhD studentships on "Differentiable particle filters for data-driven sequential inference" are available for April, July, October 2022 and January, April, July 2023 start. Please contact me if you are interested in working with me on the interface between machine learning and Bayesian Monte Carlo methods. We also support the application of Vice Chancellors Studentship Award, Breaking Barriers Studentship Award, Shine Scholars Studentship Award, and China Scholarship Council (CSC) - Surrey Award on the broad topics of statistical machine learning and AI for health for October 2022 starters.
[2021.12] Official Launch of the dental disease detection Zooniverse site!
[2021.12] The HumBugDB acoustic mosquito dataset was presented at NeurIPS'21 Track on Datasets and Benchmarks. Congratulations to Dr Ivan Kiskin and the Oxford team!
[2021.12] Ryan Banks joined the team as a Research Assistant in December 2021 and will start as a PhD student in January 2022, having worked in the team as a MSc student throughout 2021. Welcome back Ryan!
[2021.11] Xiongjie Chen and I attended FUSION'21 in Sun City, South Africa to present the paper Differentiable Particle Filters through Conditional Normalizing Flow.
[2021.9] We are hiring! One fully funded PhD studentships on "Uncertainty Quantification for Robust AI through Optimal Transport" is available for January 2022 start.
[2021.8] Dr Govind Sharma joined the team as a Postdoctoral Research Fellow. Welcome!
[2021.7] Talk at 2021 ABCP Annual Conference.
[2021.6] One paper End-To-End Semi-supervised Learning for Differentiable Particle Filters was presented at ICRA'21.
[2021.4] Talk in the Department of Computer Science at University of Cambridge.
[2021.4] Became an Associate Editor for Neurocomputing.
In the media
I am interested in foundational machine learning topics motivated by applications aiming to improve human life and environment. I am currently working on improving the ability of deep neural networks to quantify uncertainty in their predictions, which can benefit vast data science domains, from disease diagnostics to environment sensing. My work on trustworthy AI has found applications in cancer detection, biomedical imaging, and infectious disease monitoring.
Building an AI-powered system with the capability in recognising normal anatomical structures and differentiate from subtle abnormalities in digitised dental radiographic images.
Machine learning for sequential Monte Carlo methods.
Postgraduate research supervision
Postdoctoral Research Fellow:
Govind Sharma (2021)
Conghui Hu (2020 - 2021; now a research fellow at National University of Singapore)
PhD students (Principal Supervisor):
Xiongjie Chen (2019 - )
Hao Wen (2019 - )
Camellia Ruoqing Yin (2020 - )
Ryan Banks (2022 - )
Wickam Wenhan Li (2022 - )
PhD students (Co-supervisor):
Narges Pourshahrokhi (2019 - )
Xilu Wang (2019 - )
Galen Wilkerson (2019 - )
Guoyang Xie (2019 - )
Shiqing Liu (2020 - )
Xinhao Mei (2021 - )
MSc student researcher:
Ryan Banks (2020 - 2021)
Undergraduate student researcher:
Zamzam Mohamed (2021)
Georgios Papagiannis (2019 - 2020; now a PhD at Imperial College London)
Completed postgraduate research projects I have supervised
- COMM054: Data Science Principles and Practices (2019/20, 2020/21)
- COM1033: Foundations of Computing II (2018/19, 2019/20, 2020/21, 2021/22)
- Year 1 (undergraduate) Coordinator and Personal Tutor (2018 - now)
- Industry Placement Visiting Tutor (2018 - 2021)
- Departmental Seminar Organiser (2018 - 2019)