Organisers

Principal Researcher at Microsoft Research Cambridge in the Healthcare Intelligence Team. She works on the intersection of machine learning and healthcare, especially on global challenges such as mental health. Her research focuses on integrating medical domain knowledge to develop statistical machine learning models to understand disease progression and heterogeneity. Her main research interests are in probabilistic graphical modelling and causal modelling frameworks to identify subtypes of disease, in order to help develop personalized treatment and intervention strategies.

Senior Researcher at Microsoft Research Cambridge in the Healthcare Intelligence Team. Her focus is on modelling physiological time series data such as that collected in intensive care units with an aim to build decision-support tools to support clinicians. She is also interested in responsible AI, in particular the intersection of privacy and machine learning.

Vanier Doctoral Scholar at McGill University and Mila – the Québec AI Institute. He is interested in the design and development of improved diagnostic and decision-making tools in healthcare through the combination of machine learning and biosignal processing. He leads Ubenwa Health, a collaboration between researchers in Canada and Nigeria, developing low-cost tools for the diagnosis of perinatal asphyxia from infant cry. His research, in conjunction with neonatologists at the Montreal Children’s Hospital, further investigates cardiorespiratory behaviour of preterm newborns for improved monitoring in the neonatal intensive care unit

Research scientist at Google AI in Accra. Previously he was the head of the Artificial Intelligence and Data Science research lab in Makerere University, Uganda, where he was focused on finding and implementing better, simpler and cost-effective solutions to address some of the prevailing problems in developing countries, using AI, vision and Machine Learning. He is one of the co-organizers of Data Science Africa.

Associate Professor at the London School of Hygiene and Tropical Medicine. His research group works on developing computational tools using machine learning to address problems in early stage drug discovery for neglected tropical diseases and antimicrobial resistance. Has organised several international and national meetings and workshops in computational biology.

DeepMind Professor of Machine Learning at the University of Cambridge, Senior AI Fellow at the Alan Turing Institute, visiting Professor at the University of Sheffield and the co-host of Talking Machines. Neil’s main research interest is machine learning through probabilistic models. He focuses on both the algorithmic side of these models and their application. His recent focus has been on the deployment of machine learning technology in practice, particularly under the banner of data science.

Contact Us

mlforglobalhealth@gmail.com