KEYNOTEs
Keynote by Cecilia Mascolo Wednesday, September 16th, 2020 at 08:30PM – 09:30PM (Beijing), 08:30AM – 09:30AM (Washington), 02:30PM – 03:30PM (Rome)
Professor of Mobile System at University of Cambridge
Co-director for the Centre for Mobile, Wearable Systems and Augmented Intelligence
Professor of Mobile System at University of Cambridge
Co-director for the Centre for Mobile, Wearable Systems and Augmented Intelligence
Biography: 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 data for health, human mobility modelling, sensor systems and networking and mobile data analysis. 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, sensor systems, networking, data science conferences and workshops. She has delivered a number of keynote talks at conferences and workshops in the area of mobility, data science, pervasive computing and systems. More details at www.cl.cam.ac.uk/users/cm542.
Abstract: Mixed Signals: Audio and wearable data for health diagnostics
Considerable research has been conducted into mobile and wearable systems for human health monitoring. This research concentrates on either devising sensing and systems techniques to effectively and efficiently collect data about users, and patients or in studying mechanisms to analyse the data coming from these systems accurately. In both cases, these efforts raise important technical as well as ethical issues.
In this talk, I plan to reflect on the challenges and 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 discuss our project on using audio signals for disease diagnostics and our recent work in the context of COVID-19.
Abstract: Mixed Signals: Audio and wearable data for health diagnostics
Considerable research has been conducted into mobile and wearable systems for human health monitoring. This research concentrates on either devising sensing and systems techniques to effectively and efficiently collect data about users, and patients or in studying mechanisms to analyse the data coming from these systems accurately. In both cases, these efforts raise important technical as well as ethical issues.
In this talk, I plan to reflect on the challenges and 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 discuss our project on using audio signals for disease diagnostics and our recent work in the context of COVID-19.
Keynote by Archan Misra Tuesday, September 15th, 2020 at 08:30PM – 09:30PM (Beijing), 08:30AM – 09:30AM (Washington), 02:30PM – 03:30PM (Rome)
Professor at School of Information Systems, Singapore Management University
Professor at School of Information Systems, Singapore Management University
Biography: Archan Misra is Professor of Computer Science, and Vice Provost (Research), at Singapore Management University (SMU). He is the Director of SMU’s Center for Applied Smart-Nation Analytics (CASA), which is developing pervasive technologies for smart city infrastructure and applications. Archan has led a number of multi-million dollar, large-scale research initiatives at SMU, including the LiveLabs research center, and is a current recipient of the prestigious Investigator grant (from Singapore’s National Research Foundation) for sustainable man-machine interaction intelligence. Over a 20+ year research career spanning both academics and industry (at IBM Research and Bellcore), Archan has published on, and practically deployed, technologies spanning wireless networking, mobile & wearable sensing and urban mobility analytics. His current research interests lie in ultra-low energy execution of machine intelligence algorithms using wearable and IoT devices. Archan holds a Ph.D. from the University of Maryland at College Park, and chaired the IEEE Computer Society's Technical Committee on Computer Communications (TCCC) from 2005-2007.
Title: Collaborative IoT Machine Intelligence: Tackling the Crisis of Ubiquitous Power
Abstract:
Smart computing applications often assume a pervasive sensing substrate, incorporating both personal and wearable devices and infrastructurally mounted devices (such as radar & vision sensors). Assuring ubiquitous availability of power to such pervasive platforms remains the most serious resource bottleneck and requires advances on both the supply side (new forms of energy harvesting) and the demand side (new forms of ultra-low power sensing & analytics). Through this talk, I shall describe the vision of collaborative machine intelligence (CMI), where the sensing and inferencing pipelines on individual wearable and IoT devices collaborate, in real-time, and illustrate how CMI helps tackle this power challenge. First, I will describe work on advances in battery-less, or low-power sensing, enabled by such collaboration. Second, using an exemplar video surveillance application, I will describe how IoT-based CMI can provide dramatic reductions in energy, as well as improvements in accuracy. I shall conclude by arguing why these techniques require the emergence of a “Cognitive & Collaborative Edge”, that evolves from its focus on mere computation offloading to a platform for enabling such collaborative and trusted sense-making.
Title: Collaborative IoT Machine Intelligence: Tackling the Crisis of Ubiquitous Power
Abstract:
Smart computing applications often assume a pervasive sensing substrate, incorporating both personal and wearable devices and infrastructurally mounted devices (such as radar & vision sensors). Assuring ubiquitous availability of power to such pervasive platforms remains the most serious resource bottleneck and requires advances on both the supply side (new forms of energy harvesting) and the demand side (new forms of ultra-low power sensing & analytics). Through this talk, I shall describe the vision of collaborative machine intelligence (CMI), where the sensing and inferencing pipelines on individual wearable and IoT devices collaborate, in real-time, and illustrate how CMI helps tackle this power challenge. First, I will describe work on advances in battery-less, or low-power sensing, enabled by such collaboration. Second, using an exemplar video surveillance application, I will describe how IoT-based CMI can provide dramatic reductions in energy, as well as improvements in accuracy. I shall conclude by arguing why these techniques require the emergence of a “Cognitive & Collaborative Edge”, that evolves from its focus on mere computation offloading to a platform for enabling such collaborative and trusted sense-making.
