I am a Research Scientist at DeepMind.

Welcome!

I recently joined DeepMind as a Research Scientist! Formerly I was a PhD student in Statistics at the University of Warwick where I was supervised by Dr. Theodoros Damoulas. More information about my background can be found on my CV.

Research Interests

My research interests are at the interface of Bayesian statistics and computer science. I am interested in developing scalable causal decision-making frameworks based on probabilistic models. I have previously worked on machine learning algorithms for multi-task learning with Gaussian processes with a particular focus on point processes models. In terms of applications, I am interested in tackling problems in the social sciences and in healthcare.

Projects

  • I completed a summer internship (July 2020 - January 2021) within the Azua Team in the Microsoft Research Lab in Cambridge.
  • I completed a summer internship at Amazon Supply Chain Optimization Technologies (SCOT) group in Cambridge under the supervision of Javier Gonzalez.
  • I am collaborating with the University of Sydney’s Centre for Translational Data Science on Bayesian Optimisation for criminology. This is part of a broader collaboration between the Alan Turing Institute and the Centre for Translational Data Science.
  • I visited CSIRO’s Data61 (11/18-02/19) to work on Gaussian Process modulated Poisson Processes.
  • I am a visiting researcher at the Alan Turing Institute and I am part of the Clean Air project. The project aims at developing statistical methodology and machine learning algorithms to support London’s Major’s office in taking data-driven/evidence based policy decisions in order to improve air quality over the city of London.
  • I collaborated with CUSP London on a project aiming at assessing the impact of traffic policies on the number of accidents happening in London.

Talks & Posters

  • September 2021 (Talk): I spoke about causal decision-making with Gaussian Processes at the Aalto University Seminar Series on Advances in Probabilistic Machine Learning.
  • July 2021 (Talk): I gave a talk on Causal Bayesian Optimization at the Eastern European Machine Learning School (EEML) in Lithuania, Vilnius.
  • September 2020 (Talk): I gave a talk on Multi-task Gaussian process modelling for point process data at the Gaussian Processes Summer School (slides, video)
  • September 2020 (Talk): I gave the talk “Causal decision-making meets Gaussian Processes” at the Gaussian Processes Summer school (slides, video)
  • June 2020 (Talk): I spoke about Causal Decision Making Under Uncertainty at CogX (London, UK) (video)
  • December 2019 (Poster): I presented a poster on Structured Variational Inference in Continuous Cox Process Models at Neural Information Processing Systems (NeurIPS) (Vancouver, Canada)
  • April 2019 (Poster): I presented the paper “Efficient Inference in Multi-task Cox Process Models” at the International Conference on Artificial Intelligence and Statistics (AISTATS) (Naha, Okinawa, Japan).
  • November 2018 (Talk): I spoke about Variational Inference in Non-homogeneous Poisson Processes at the Centre for Translational Data Science of The University of Sydney (Sydney, Australia).