Computational tools are poised to play an increasingly large role in our society across different domains, including public health and conservation. As a consequence, computational tools need to be designed to ensure equitable benefits for everyone.
To that end, we need to bring in a diverse set of perspectives that spans from algorithmic fairness, human-centered computing, and sustained deployment. This seminar series will explore how artificial intelligence can equitably solve social problems. For example, what role can AI play in promoting health, access to opportunity, and sustainable development? How can human-centered computing methods be deployed to ensure AI systems are ethical, inclusive, and accountable?
- Arpita Biswas (firstname.lastname@example.org, sites.google.com/view/arpitabiswas)
- Herman Saksono (email@example.com, hermansaksono.com)
Danielle Belgrave, Ph.D. (Microsoft Research Cambridge, UK)
Principal Research Manager, Microsoft Research Cambridge (UK)
Abstract: Machine learning advances are opening new routes to more precise healthcare, from the discovery of disease subtypes for stratified interventions to the development of personalised interactions supporting self-care between clinic visits. This offers an exciting opportunity for machine learning techniques to impact healthcare in a meaningful way. Within the healthcare domain, machine learning for mental healthcare is an under-investigated area and yet a potentially highly impactful area of research. In this talk, I will present recent work on machine learning to enable a more personalised approach to mental healthcare, whereby information can be aggregated from multiple sources within a unified modelling framework. I will present applications from both mental health and respiratory diseases.
Danielle Belgrave bio
Danielle Belgrave is a machine learning researcher in the Healthcare Intelligence group at Microsoft Research, in Cambridge (UK) where she works on Project Talia. Her research focuses on integrating medical domain knowledge, probabilistic graphical modelling and causal modelling frameworks to help develop personalized treatment and intervention strategies for mental health. Mental health presents one of the most challenging and under-investigated domains of machine learning research. In Project Talia, she and her team explore how a human-centric approach to machine learning can meaningfully assist in the detection, diagnosis, monitoring, and treatment of mental health problems. She obtained a BSc in Mathematics and Statistics from London School of Economics, an MSc in Statistics from University College London and a PhD in the area of machine learning in health applications from the University of Manchester. Prior to joining Microsoft, she was a tenured Research Fellow at Imperial College London.