Wednesday 22 June 2022, 1.30PM to 2.30pm
Wearable and mobile devices are very good proxies for human behaviour. Yet, making the inference from the raw sensor data to individuals’ behaviour remains difficult.
The list of challenges is very long: from collecting the right data and using the right sensor, respecting resource constraints, identifying the right analysis techniques, labelling the data, limiting privacy invasion, to dealing with heterogeneous data sources and adapting to changes in behaviour.
In this talk, I plan to reflect on these challenges and highlight the opportunities that mobile and wearable health systems are introducing for community, the developers as well as the users.
I will use examples from my group's ongoing research on exploring machine learning and data analysis for health application in collaboration with epidemiologists and clinicians.
In particular I will concentrate a part of the talk on a project focusing on leveraging audio signals from the human body to help automated diagnostics and disease progression analysis.
Professor Cecilia Mascolo is the mother of a teenage daughter but also a Full Professor of Mobile Systems in the Department of Computer Science and Technology, University of Cambridge, UK. She is a director of the Centre for Mobile, Wearable System and Augmented Intelligence.
She is also a Fellow of Jesus College Cambridge and the recipient of an ERC Advanced Research Grant. Prior joining Cambridge in 2008, she was a faculty member in the Department of Computer Science at University College London.
She holds a PhD from the University of Bologna. Her research interests are in mobile systems and machine learning for mobile health. She has published in a number of top tier conferences and journals in the area and her investigator experience spans projects funded by Research Councils and industry.
She has served as steering, organizing and programme committee member of mobile and sensor systems, data science and machine learning conferences.
Visit Cecilia's profile to find out more.
Admission: Online only