I am a senior research scientist at Spotify research working with a great team in London, UK.
Before that, I was a senior researcher at Microsoft Research Cambridge part of ML for Healthcare. I was a postdoc at the University of Oxford working with Yee_Whye Teh. I did my PhD with Zoubin Ghahramani in Cambridge university where I was studying the fairy tales of Machine Learning.
A pure Bayesian in education, I spent years building probabilistic models that uncover the latent structure in data. My work focused on methodology and was applied on a plurality of data, from genomics to relational. Most recently, I moved away from methodology driven research and got interested in solving challenges in the real world and most recently in the domain of Health. I constructed models for the prediction of adverse patient outcomes in a hospital. I mostly worked on time-series using probabilistic but also deep learning approaches. I am a firm believer on co-developing the ML models with the users they intend to assist.
Opinions are my own
Excited to join the Organizing Committee as the Senior Program Chair for the WiML Workshop at NeurIPS 2022.
[ICML 2020 workshop on Healthcare Systems, Population Health, and the Role of Health-Tech]
I am excited to announce call for short papers to our ICML workshop on HSYS.
Go and check our website for details!
Paper submission deadline: May 29, 2020 (AOE)
Workshop: July 17 or 18, 2020 | Virtual Worldwide
I am part of the AISTATS 2020 Organizing Committee.
I gave an invited talk in the Construction Engineering Masters (CEM) programme at the Department of Engineering, University of Cambridge. An overview of the Opportunities and Challenges for ML in Healthcare.
I am honoured to be part of the comittee of the Women in Data Science and Statistics (WiDSS) launched by the Royal Statistical Society.
Gave a tutorial on Machine Learning and Healthcare in Deep Learning Indaba, Stellenbosch, 2018
Danielle Belgrave, Lamiae Azizi and myself gave an ICML tutorial on ML for Personalised Health.
Gave a tutorial on Probabilistic Reasoning in Deep Learning Indaba, Johannesburgh, September 10-15, 2017