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Curriculum vitae


The core of my research focuses on probabilistic graphical modelling and deep learning to understand heterogeneous phenomena using big data to understand causality mechanisms. I have extensive experience both in leading projects and teams and hands-on implementations of end-to-end solutions in statistical machine learning for healthcare. I also implement and develop a broad spectrum of supervised and unsupervised machine learning methods. I have a wide range of hands-on experience with several ML algorithms implemented with C#, Python, STATA, R, SAS, SPSS.

Over 80 Academic publications including 2 patents.

I have given over 50 invited keynotes at top ML venues including NeurIPS, ICML, ML for Healthcare, CHIL, Harvard, Berkley, University of Melbourne, UCL, LSE, University of Oxford, University of Cambridge.

Publications and Academic Contributions


PhD (Subject Area: Machine Learning for Healthcare) 2010 – 2013

The University of Manchester

Thesis: Probabilistic causal models for asthma and allergies developing in childhood

MSc Statistics - University College London

BSc Business Mathematics and Statistics - London School of Economics

Selected Academic Service

• Program Chair NeurIPS 2022 - I was one of the four program chairs responsible for Keynote invitations at NeurIPS and overseeing the review process and decision making for the NeurIPS 2022 program 

• Tutorial Chair of NeurIPS 2019, 2020

• Board of Directors and Diversity and Inclusion Chair WiML 2020-present

• Diversity and Inclusion Chair AISTATS 2020

• Diversity and Inclusion Chair NeurIPS WiML 2020

• Co-organiser NeurIPS 2020 workshop “Causal Discovery & Causality-Inspired Machine Learning”

• Founder and Co-Organiser 1st ICML Workshop on Machine Learning for Global Health 2020

• Organiser Machine Learning for Healthcare: Key to better patient-practitioner-system partnerships - DALI Workshop 2019

• Board Member Women in Machine Learning 2019-2020

• Co-opted Member of the Data Science Section of the Royal Statistical Society

• Area Chair: NeurIPS 2018, 2019; ICML 2020

• Co-Organiser of the 1st Khipu 2019 (Increasing participation of Latin America in Machine Learning)

• Advisory Board Deep Learning Indaba 2018-present

• Organiser AI in Healthcare: Key to better patient-practitioner-system partnerships - DALI Workshop 2018

• Scientific Committee Microsoft Research AI Summer School 2018

• Organiser (Senior Program and Meeting Chair) Women in Machine Learning 2017

• Program Committee Member Special Session on Machine Learning Applications in Psychiatric Research at The 17th IEEE International Conference on Machine Learning and Applications 2017

• Area Chair for Women in Machine Learning 2016

Selected invited Keynotes and Tutorials

• Invited Keynote at Time Series for Healthcare workshop NeurIPS 2022

• Invited Lecture NYU Department of Electrical and Computer Engineering special seminar series, 2021

• Keynote: NeurIPS 2020 Workshop “I Can’t Believe it’s not Better”

• Invited Lectures at Stanford, University of Michigan, Columbia University, LSE 2020

• Keynote and Panel on AI for Social Good, Khipu 2019

• Tutorial: Data Science Summer School, L’Ecole Politechnique 2019

• Keynote: Machine Learning for Healthcare Conference, Stanford 2018

• Tutorial: Machine Learning for Personalised Health at ICML, Sweden 2018 (ICML is the 2nd largest Machine Learning Conference. The tutorial attracted >3000 attendees)

• Keynote: Advances in Data Science, Manchester 2018

• Keynote: Women in Data Science Conference, Zurich 2018

• Invited Speaker: Columbia University, 2018

• Keynote: Big Challenges of Big Data – Spanish Allergy Society, Valencia 2018

• Tutorial: Machine Learning Strategies in Healthcare Research. Deep Learning Indaba, Johannesburg 2017

• A Bayesian Predictive Modelling Framework for Endotype Discovery. University of Manchester 2017

• Course Instructor at Health Informatics Conference: Machine Learning in Healthcare (Teaching with Prof Magnus Rattray) 2017

• Statistical Learning Approaches to Latent Variable Modelling to Accelerate Endotype Discovery. Systems Genomics Group, University of Melbourne, Australia 2016

• A Bayesian Approach to Compensating for Missing Data. Missing Data Methods Group, Murdoch Children's Research Institute, Australia 2016

• Centre for Epidemiology and Biostatistics, University of Melbourne, Australia 2016

• Invited Speaker at the 1st UK Prediction Modelling in Psychiatric Research Workshop, King's College London 2016

• Machine Learning to Understand Subtypes of Childhood Wheezing. International Congress on Pediatric Pulmonology, Naples 2016

• Workshop: Statistics for the Respiratory Pediatrician. International Congress on Pediatric Pulmonology, Naples 2016

• The Asthma E-Lab: Discovering Subtypes of Disease with Model-based Machine Learning. Royal Statistical Society Lancashire and East Cumbria, University of Lancaster 2016

• GlaxoSmithKline Biostatistics Annual Conference, London 2015

• Machine Learning and Perception Group, Microsoft Research Cambridge 2012

• Teaching Assistant: Generalized Linear Latent and Mixed Models. University of Oxford Spring School, Oxford 2010

• Course Assistant: Generalized Linear Latent and Mixed Models. 39th GESIS Spring Seminar: Testing and Modeling with Latent Variables, Cologne 2010

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