SUMMARY
Andrew Foong, Ph.D., is a deep learning researcher focused on developing advanced artificial intelligence (AI) methods to improve cancer care. His research leverages machine learning techniques, including diffusion generative models, large language models and Bayesian deep learning to address complex challenges in oncology. He co-directs the artificial intelligence and data analytics team in the Department of Radiation Oncology, aiming to translate foundational advances in AI into meaningful clinical impact for people with cancer.
Focus areas
- Deep learning for personalized radiation oncology. Dr. Foong's research explores advanced deep learning techniques to personalize each stage of cancer care, from diagnosis through radiation treatment to patient follow-up. His work aims to develop predictive models that anticipate individual treatment responses and optimize radiation dosing to enhance therapeutic effectiveness while minimizing side effects. This work also provides deeper insight into tumor behavior using clinical data.
- Translational AI for cancer care. A core goal of Dr. Foong's research is translating recent advances in deep learning into meaningful clinical tools for oncology. His approach emphasizes collaboration between Mayo Clinic clinicians and globally recognized AI researchers from academia and industry. This will enable the responsible integration of state-of-the-art AI technologies into cancer care.
- Inductive biases in scientific domains. The performance of AI models is chiefly determined by the quality of training data combined with the inherent inductive biases of the models, such as geometric equivariance. Dr. Foong's research aims to embed crucial clinical and scientific inductive biases into concrete deep learning architecture and training innovations. This will lead to improved accuracy with less data.
- Probabilistic and Bayesian deep learning. Bayesian methods offer a mathematically rigorous framework for combining clinical observational data with prior medical knowledge. This enables more accurate estimation of important clinical outcomes such as the risk of cancer recurrence. Dr. Foong's research aims to develop probabilistic models that combine information and quantify uncertainty robustly, helping clinicians make informed decisions.
Significance to patient care
Dr. Foong creates new AI tools to improve cancer care. Cancer treatments, including radiation therapy, can be difficult. By developing advanced AI methods, Dr. Foong and his team help clinicians deliver more personalized treatments, making therapies safer and more effective. One of Dr. Foong's goals is to provide healthcare professionals with tools to improve their ability to predict how people might respond to treatment. This could make cancer treatment easier, with fewer side effects, helping people feel better.
Professional highlights
- George and Lilian Schiff Foundation Studentship Ph.D. funding award, University of Cambridge, 2018-2021.
- Trinity Hall Research Studentship Ph.D. funding award, University of Cambridge, 2018-2021.
- Institution of Engineering and Technology Prize, University of Cambridge, 2018.
- Institution of Civil Engineers Baker Prize, University of Cambridge, 2017.
- British Petroleum First Year Prize, University of Cambridge, 2015.