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.
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.
- 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).