BMath, University of Waterloo (2019)

MSc, University of Waterloo (2022)

Antibodies work by binding to their targets (antigens), and either inhibiting their function or activating other components of the immune system. Predicting the mode by which an antibody binds to its cognate antigen is called antibody-antigen complex modelling or docking. While general protein complex prediction has seen great improvements in recent years, driven by methods such as AlphaFold Multimer, antibody-antigen complexes are still difficult to model because we rarely have useful homologs to provide co-evolutionary information. My DPhil project is focused on developing new machine learning docking models to more accurately predict antibody-antigen complexes.

I'm a PhD student within the Oxford Protein Informatics Group (OPIG), studying the developability of antibodies. I started my PhD in 2022 through the SABS CDT program. Before this, I completed my Bachelors in Medical Natural Science and Masters in Bioinformatics at the Vrije Universiteit Amsterdam. 

My research focuses on leveraging computational methods to predict antibody developability properties. The goal is to improve efficiency of the antibody engineering and developability process.

Hello! I'm Yael, a DPhil student at the Oxford Protein Informatics Group (OPIG). I'm currently researching structure-based drug design, with a focus on deep-learning generative models. I earned my Bachelor's and Master's degrees in Electrical and Computer Engineering from the Ben-Gurion University in Israel. Outside of academia, I enjoy hiking and exploring the UK with my husband, Gal, and our one-year-old daughter, Abigail.

Deep learning for drug design, graph neural network, generative models

I am a second-year DPhil student on the StatML CDT. Previously, I obtained my BSc in Mathematics at Imperial College London and my MSc in Statistical Science at the University of Oxford.

My current research interests include developing meta-learning methods for efficiently adapting LLMs and applications of geometry within machine learning (e.g. deep generative models for manifold-valued data and geometric deep learning). I am also increasingly interested in the intersection of cognitive science and neuroscience with machine learning, for inspiring more effective and efficient learning algorithms.