About Me

I am a Research Fellow in Health Text Analytics and Data Science in the Department of Biostatistics & Health Informatics (BHI) at King's College London. My expertise lies in applying advanced AI and data science methods to healthcare challenges, with a particular focus on mental health.

My research combines natural language processing, machine learning, multimodal learning, and knowledge graphs to analyze longitudinal multimodal data, with the goal of advancing understanding of complex mental health conditions, improving service delivery, supporting clinical decision-making, and addressing health inequalities. I work closely with clinicians, NHS Trusts, and international research partners to translate cutting-edge AI technologies into real-world healthcare solutions.

My academic background spans multiple disciplines and institutions. I earned a PhD in Economics from the University of Southampton, funded by the Economic and Social Research Council (ESRC) in collaboration with The Alan Turing Institute, where I explored online social networks and eating disorders, working alongside economists and physicist. I also hold an MSc in Software Engineering from the South China University of Technology, and completed an exchange program at The Chinese University of Hong Kong, focusing on data mining and sentiment analysis.

Current Roles

• Research Fellow in Health Text Analytics and Data Science (Lecturer grade), Department of Biostatistics & Health Informatics, King's College London (Nov 2021 – Present)
• Advisor, Meta/Facebook Global Eating Disorder and Body Image Advisory Group (Feb 2024 – Present)
• Member, King's Institute for Artificial Intelligence (Sept 2023 – Present)
• Honorary Researcher Staff, South London and Maudsley NHS Foundation Trust (Apr 2019 – Present)
• Co-chair, NLP Reading Group at KCL (Sept 2024 – Present)

Previous Positions

• Research Associate, King's College London (Jan 2019 – Nov 2021)
• Honorary Researcher Staff, Mersey Care NHS Foundation Trust (Aug 2019 – Apr 2020)
• Enrichment PhD Fellow, The Alan Turing Institute (Sept 2017 – Sept 2018)
• Research Assistant, The Chinese University of Hong Kong (Jun 2014 – Sept 2014)
• Research Assistant, City University of Hong Kong (Apr 2014 – Jun 2014)

Education

• Ph.D. in Economics, University of Southampton and The Alan Turing Institute (Sept 2015 – Jan 2019)
  • Thesis: "Eating Disorders Studied over Online Social Networks"
  • Advisors: Dr. Antonella Ianni, Dr. Markus Brede, and Prof. Emmanouil Mentzakis
• M.Sc. in Software Engineering (Distinction), South China University of Technology (Sept 2012 – June 2015)
• Dissertation: "Product aspect extraction supervised with online domain knowledge"
• Advisors: Prof. Yi Cai, Prof. Huaqing Min, and Prof. Ho-fung Leung
• B.Sc. in Computer Science (First-Class Honours), Jiangxi Agricultural University (Sept 2008 – June 2012)

Professional Highlights

Over the years, I have contributed to numerous impactful projects and initiatives:

• Built medical timeline using large language models to support clinical decision-making
• Designed AI-driven tools to identify treatment-resistant schizophrenia for earlier intervention
• Developed systematic methodologies to examine and address health inequalities at scale
• Explored multimorbidity trajectories in individuals with severe mental illness to inform integrated care
• Developed VIEWER, the first population health management platform in mental healthcare
• Implemented CogStack and AI-based solutions across NHS Trusts to enhance clinical workflows
• Advised Meta on addressing eating disorders and body image concerns within online platforms
• Investigated social networks in online health communities to inform network-based interventions

Technical Skills

I bring expertise in:

• Programming: Python, PyTorch, TensorFlow, Hugging Face, Docker, SQL, Elasticsearch, MongoDB, Neo4j, Scikit-Learn
• Data Science & AI: NLP, LLMs, multimodal deep learning, time-series analysis, graph neural networks
• Healthcare Data: EHRs, FHIR, HL7, clinical NLP, secure data environments
• Research Methods: Statistical analysis, causal inference, network analysis, instrumental variables