Meet our 2021 cohort.
My research interests lie at the intersection of computer vision and medicine. The broader goal of my research is to make AI algorithms more robust, generalizable, and deployable to a real-world setting. More specifically, I am interested in exploring self-supervised learning, domain adaptation and vision transformers for different facets of medical research - radiology, cardiology, assistive-surgery and many more.
The accumulation of big datasets has the potential to help us improve our understanding of complex processes that are inaccessible to our intuition. Explainable AI techniques can help transform model predictions into insights. My research interest lies particularly in the application of these to a task in immunology – peptide presentation by MHC proteins.
Having worked in the pharmaceutical industry, my interests lie at the intersection of AI techniques, bioinformatics methods, and modern drug design. Specifically, I am eager to look into ongoing challenges in cancer drug development to ensure more effective treatments and discover more biologically relevant targets.
I am interested in using machine learning to optimize pharmaceutical-producing gene circuits and control architectures. I am also interested in using network science to understand bacterial genetics.
My research interest is the incorporation of machine learning tools into molecular dynamics simulations to accelerate drug discovery. I work on improving underlying methods and applying them to applications of specific interest, such as drug development to combat antimicrobial resistance.
My research interests currently lie in the development of new machine learning tools to better take advantage of the spatio-temporal dynamics and multi-modality of biomedical data, and how such models can be used to assist patient treatment decisions.
My research interests are focused on the application of Machine Learning (ML) techniques to improve patient healthcare outcomes. With increased data availability and disease specific research I believe there is the opportunity to tailor and optimise patient treatment plan using ML.
My research interests lie in the use of machine learning methods to evaluate surgical outcomes, particularly for patients that have had cleft lip and palate repair or plastic reconstructive surgery.
My research interests mainly lie in developing models to better understand disease mechanisms from the perspective of systems biology. I would like to combine biophysical theory with machine learning to discover new insights in the field.
I am interested in the origin and development of how biomedicine involves AI and an ethnographic examination of a related laboratory.
My current interest focuses on exporing multi-modal biological data with machine learning. I'm also interested in biomedical imaging analysis with self/unsupervised learning methods.