Members

Team Lead

Honghan Wu is a Professor of Health Informatics and AI, based in the School of Health and Wellbeing of the University of Glasgow, where he leads the research theme of data science and AI. Prof Wu is a co-director of Health Data Research Scotland. He also is an honorary professor at Hong Kong University, an honorary associate professor at Institute of Health Informatics, UCL, and a former Turing Fellow of The Alan Turing Institute, UK's national institute for data science and artificial intelligence. Prof Wu holds a PhD in Computing Science. His current research focuses on machine learning, natural language processing, knowledge graph and their applications in medicine.

PhD Students

Jinge Wu

Jinge is based at the Institute of Health Informatics, UCL. She works on AI in Healthcare, including disease modelling/phenotyping, clinical NLP with multimodal data.

Yunsoo Kim

Yunsoo is based at the Institute of Health Informatics, UCL. He works on multimodal large language model in health data. His research interest also includes applications of the models in diagnosis and prognosis of neurodiseases such as dementia.

Bo Peng

Bo Peng is based at the Institute of Health Informatics, UCL. He works on Applying Multimodal AI for Timely, Accurate Brain Tumor Diagnosis and Treatment Efficacy Prediction.

Fanny (Fran) Silavong

Fran is based at the Institute of Health Informatics, UCL. She works on Enhancing Health Literacy Using Large Language Models and Knowledge Graph.

Sindhu Bhaarrati Naidu

Dr Sindhu Bhaarrati Naidu is doing her PhD research, titled "OLIVE: Improving the early detection of lung cancer in never-smokers", which is funded by NIHR (Award NIHR303686).

Chao Xu

Chao Xu is based in School of Health and Wellbeing. He works on Developing a risk stratification tool to detect ADHD in children and adolescents. His research interest is around big data analysis applying statistical machine learning in child and adolescent health.

Yusuf Abdulle

Yusuf Abdulle is based at King's College London. He works on Exploiting the electronic health records and multimodal data to model patters of onset and progression in Motor Neurone disease (MND/ALS).

MSc Students

• Lingyu Kong works on a systematic review and meta-analysis on adverse outcome predictors for elderlies in the era of large language models. He is based at the University of Glasgow.

Alumni

2025

• Abul Hasan joined us in Nov 2022 as a research fellow based in Institute of Health Informatics, UCL. He worked on deep learning and clinical NLP on NIHR funded project, AIM-CISC.
• Charlotte Greene was a PhD student based in Usher Institute, University of Edinburgh. She studied antidepressant and antipsychotic drug prescribing in people with diabetes.
• Farah Francis was a PhD student based at Usher Institute, University of Edinburgh. She worked on machine learning methods for detecting and predicting fetal hypoxia.

2024

• Rajan Rai worked on a multimodal LLM project titled Multi-view medical image classification/captioning.
• Jamie chow worked on a project titled What are the key factors that need to be considered when monitoring AI applications in radiology?.
• Harry Wilde was a postdoctoral researcher working on the QMIA project on “Quantifying and Mitigating Bias affecting and induced by AI in Medicine”.
• Julia Isabelle Dela Rosa worked on a project titled Evaluating bias mitigation methods for clinical prediction models as part of our MRC funded QMIA project.
• Emily Sun was a high school student doing an internship on a knowledge graph project for early ALS prediction.

• Neel Basak worked on a dissertation project titled Knowledge graph-based approaches to drug repurposing for rare and novel diseases: A Systematic Review.

• Yuxuan Huang worked on a project titled Deciphering the Decision-Making Processes of Transformer-Based Pretrained Models in Clinical NLP Tasks.
• Yifan Li worked on using machine learning and large language models for interlinking research datasets for research on antibiotic resistant bacteria in the veterinary space.
• Irini Kanaris Miyashiro worked on a project aiming to use AI technologies for facilitating tackling the NHS workforce challenge. She is working with a start-up company, Shiftpartner, and Mid and South Essex NHS Foundation Trust. Technically, she will use large language models for digitalising competency frameworks of health professionals and skills/knowledge needed for ever-changing bankshifts at the NHS.
• Yue Gao is a PhD student from Beijing University of Posts and Telecommunications. Funded by CSC, Yue visited KnowLab for one year doing research on human in the loop AI models for automated clinical coding.

2023

• Eva Keller worked on Analysing data embedded bias for treating cardiovascular diseases. This was a collaboration with Profs Sarah Wild and Cathie Sudlow.
• Daqian Shi (2022-2023) was a visiting Knowledge Graph Embedding (KGE) aims to model information in KGs into higher dimensional space. Daqian was a Ph.D. student in the Department of Information Engineering and Computer Science of the University of Trento, supervised by Prof. Fausto Giunchiglia.
• Tianyue Qi (2023) and Ying Gao (2023), they both work on the project titled Reproducible machine learning based risk prediction models for cardiovascular disease. Ying focuses on the literature review and Tianyue focused on reproducing ML models from the literature.
• Emily Groves (2023) was an MSc student at UCL doing an internship on evaluating embedding models for chemistry ontology enrichment tasks.
• Zexi Li (2023) was an MSc student at UCL doing an internship on health data science projects.
• Yingjia Wang (2023) was an MSc student at UCL doing an internship on health data science projects.
• Guitao Fang (2023) worked on Benchmarking distributed representations for Chemistry knowledge graph. This is a collaboration with iris.ai.
• Kai Chen (2023) works on Addressing NHS Nurse Shortage with Machine Learning and Behavioural Science. This is a collaboration with Mid and South Essex NHS Foundation Trust.-
• Zewei Shen (2023) worked on Pretraining a language model on NHS A-Z corpus. This is part of the ExplainHealth project, funded by Moorfields Charity.
• Xinyi Yue (2023) worked on Deriving a knowledge graph from NHS A-Z website. This is part of the ExplainHealth project, funded by Moorfields Charity.
• Siyuan Zhao (2023) worked on Lab linkage: can we unlock research into the rise of antibiotic resistant bacteria?. This is a collaboration with Royal Veterinary College.
• Qihe Wang (2023) worked on Understanding reasons of patient attendance to General Practice using natural language processing and machine learning. This is a collaboration with NHS Frimley Health Foundation Trust.

2022

• Aneeta Sylolypavan (2021-2022) was a part-time research associate based at University of Edinburgh. Aneeta worked on evaluating disparities between clinicians in labelling data for training machine learning models, particularly in the context of decision making supports within Intensive Care Units.
• Rowena Smith (2022) is a part-time clinical research associate based at University of Edinburgh. Rowena works on a project studying the associations between childhood adverse events and later life mental disorders. This project used linked health datasets in Scotland including free-text clinical notes from both primary care and secondary care.
• Hengrui Zhang was an MSc student at UCL (2022) and worked on deep learning models for automated coding from discharge summaries. He is now pursuing PhD opportunities.
• Yun-Hsuan Chang was an MSc student at UCL (2022) and worked on Parkinson’s Disease Modelling using mutimodal data. She is now a Clinical Informatics Lead at uMed.
• Xuezhe Wang was an MSc student at UCL (2022) and worked on graphic neural network algorithms for predicting adverse drug reactions of COVID-19 treatments. He is now a PhD student at UCL.
• Zhaolong Wu was an MSc student at UCL (2022) and worked on clinical NLP models utilising multi-documents for automating medical coding from free-text EHR data. He is now a PhD student at Hong Kong University.
• Minhong Wang was a Research Fellow at Institute of Health Informatics, UCL (2020-2022). Her research interests include applying machine learning, deep learning and natural language processing in healthcare. She worked on domain specific word embeddings and automated triage using GP referrals. She works now with HSBC.

~ to 2021

• Alexander Dobres was a Msc student (2021) worked on knowledge graph based approaches for predicting adverse drug events of COVID-19 drugs.
• Tina Yao was a MRes student (2021) based at the Institute of Health Informatics, UCL. She worked on deep learning methods for facilitating the management of atrial fibrillation in patients with critical illnesses.
• Dr Hang Dong was working on applied machine learning, natural language processing (NLP), and data mining for phenotyping from electronic health records (2018-2021). He is now a research fellow at Oxford University.
• Menka Shah was an MSc student at Institute of Health Informatics, UCL (2021). She now works at Frimley Health NHS Foundation Trust.
• Fahad Siddiqui was an Msc Institute of Health Informatics, UC (2021). He works at Kettering General Hospital.
• Dr Víctor Suárez Paniagua was a Research Fellow on machine learning (2021), with an emphasis on deep learning; now works in the industry.
• Emma Whitfield (COVID-19 Project Placement, 2020), HDR UK-Turing Wellcome PhD Programme
• Claire Coffey (COVID-19 Project Placement, 2020), HDR UK-Turing Wellcome PhD Programme
• Cheng Wan (visiting researcher 2019-2020), Nanjing Medical University
• Joesph Powell, MSc student (2020), Institute of Health Informatics, UCL
• Steven Cassady, MSc student (2019), School of Informatics, University of Edinburgh
• Qianyi Zeng, MSc (2019), School of Informatics, University of Edinburgh
• Wenjun Chen, MSc (2019), School of Informatics, University of Edinburgh
• Quan Sun, MSc (2017), King’s College London
• Hui Wang, MSc (2017), King’s College London