I am a postdoctoral research assistant in computational statistics and machine learning at the University of Oxford supervised by Arnaud Doucet and funded by the CoSInES project. I recently have joined the algorithms and inference working group in Next Generation Event Horizon Telescope (ngEHT) collaboration to help improve the algorithms used to model and image supermassive black holes. Before this, I completed a PhD in Statistics with Alexandre Bouchard-Côté at the University of British Columbia.

Monte Carlo methods, scalable Bayesian inference, parallel tempering, sequential Monte Carlo, information geometry, statistical physics.

I am a computational biologist developing methods for structural biology, primarily cryo-EM for drug discovery.

I hold a BSc degree in Biotechnology, another one in Computer Science and an MSc in Biophysics. I did my PhD at the Spanish National Center of Biotechnology (CSIC) and the Autonomous University of Madrid (UAM), under the supervision of Professor Jose Maria Carazo and Dr Joan Segura. During that time, I developed machine learning algorithms for structural biology, including BIPSPI and DeepEMhancer.

I joined the Department of Statistics in 2020 where, in collaboration with XChem, I worked on AI algorithms for fragment-based drug discovery for almost two years. Currently, I am trying to improve cryo-EM algorithms to facilitate drug discovery.

• Machine Learning / Deep Learning 
• Structural biology  
• Cryo-EM 
• Drug discovery 

I'm currently working in the Department as a Florence Nightingale Bicentennial Fellow. I previously completed a postdoc with Chris Holmes and Arnaud Doucet, working on causal inference and conformal prediction. Before that I completed my DPhil as part of the AIMS CDT under the supervision of Arnaud Doucet and George Deligiannidis. My thesis covered several topics in (primarily Bayesian) computational statistics and machine learning, including Monte Carlo methods and deep generative modelling. Before they left Oxford, I also worked with Frank Wood and Hongseok Yang on probabilistic programming.

I am broadly interested in ensuring the robustness of complex, safety-critical systems. I have worked on techniques for reliable uncertainty quantification, causal inference, as well as a variety of topics across machine learning. An underlying focus of my research is on methodology that is valid under minimal assumptions, which allows their application in large-scale real-world settings where more specific assumptions may be difficult to justify.