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Yui Hei Augustine Luk

Dr

30/08/1997

Y6541

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+447724516731

47 Grandpont Place
Oxford, OX1 4NH
United Kingdom

Croucher_Foundation

As a clinician-scientist from Hong Kong pursuing my PhD in Clinical Medicine at Oxford, my research explores how artificial intelligence can transform healthcare delivery. My work involves developing machine learning tools that accurately predict antibiotic resistance in patients presenting with sepsis, thereby improving clinical decision-making, patient outcomes, and antibiotic stewardship. Additionally, I am developing an interactive dashboard to identify and mitigate hospital-acquired infections in real-time, enhancing hospital resilience against outbreaks.

Having completed my undergraduate medical training during the COVID-19 pandemic, I deeply appreciate the need for innovative research into the prevention and management of infectious diseases. My unconventional background blending clinical medicine with a passion in technology has shown me the immense value of interdisciplinary collaboration – much groundbreaking research arise from collaborations across diverse fields and backgrounds.

Participation in the Hong Kong Laureate Forum offers an extraordinary opportunity to engage with brilliant young scientists from Hong Kong and worldwide, fostering interdisciplinary and cross-boundary collaborations that drive meaningful contributions to global health research. I am particularly enthralled by the prospect of learning from distinguished Shaw Prize laureates and global scientific leaders, while also playing my part in inspiring future generations of clinician-scientists dedicated to bridging clinical medicine and technological innovation.

Postgraduate (PhD)

Medicine, Data Science

University of Oxford

Oxford, UK

DPhil (PhD) fully funded by the University of Oxford-Croucher Scholarship (2023). Published research in machine learning applications for predicting antibiotic resistance and hospital patient outcomes. Presented at international conferences, including the European Society of Clinical Microbiology and Infectious Diseases (2025) and International Society for Magnetic Resonance in Medicine Workshop (2018). Named Chui’s Students of Excellence (2019) and recipient of the HKU Entrance Scholarship (2015) at the University of Hong Kong.

My current research focuses on optimising antimicrobial therapy and infection control in healthcare settings through data science and machine learning techniques.

Specifically, I am developing Bayesian neural networks (BNNs) that predict antibiotic resistance in patients presenting with sepsis, guiding timely and informed decisions regarding treatment escalation or de-escalation. By integrating electronic health data, these models generate personalised recommendations, shifting from the traditional population-based, guideline-driven approach. The incorporation of Bayesian inference provides quantifiable uncertainty estimates, enabling the typically opaque “black-box” AI to acknowledge when it “doesn’t know”. This significantly enhances model interpretability and supports clinicians in making safer and more informed decisions, thereby improving patient outcomes, reducing unnecessary antibiotic use, and combating antimicrobial resistance.

Additionally, my work includes developing an interactive infection prevention and control dashboard that integrates electronic healthcare data—such as patient admissions, ward movements, and pathogen genomic information—to rapidly identify and contain healthcare-associated infections. Given the increasing frequency and severity of disease outbreaks, this research aims to strengthen healthcare systems' resilience against inevitable pandemics.

Further research avenues include using MALDI-TOF mass spectrometry data for rapid antimicrobial resistance prediction and applying graph neural networks to better understand and predict infection transmission dynamics within hospitals.