Speaker: Cecilia Mascolo - Full Professor, University of Cambridge
Abstract: Sensing and data analysis has made strides to improve how we understand human behaviour and health. I will discuss work in the area of finding more effective and efficient ways of sensing and analysis sensing data from wearable devices to understand our health.
I plan to reflect on the challenges and opportunities that mobile and wearable health systems are introducing for the 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 one specific example, because of the pandemic, I will discuss our project on using audio signals for disease diagnostics and our recent work in the context of COVID-19: a crowdsourced collected through mobile apps (covid-19-sounds.org) of respiratory sounds (coughs, breathing and voice) to pre-screen and diagnose COVID-19.
Bio: 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 co-director of the Centre for Mobile, Wearable System and Augmented Intelligence and Deputy Head of Department for Research. 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 received numerous best paper awards and in 2016 was listed in “10 Women in Networking /Communications You Should Know”. She has served as steering, organizing and programme committee member of mobile and sensor systems, data science and machine learning conferences. More details at www.cl.cam.ac.uk/users/cm542