D3A 2.0

Preliminary program

22 October 2024

09:00

Registration and networking

10:00

Opening session

10:30

Keynote: AI for Wearable Health

Cecilia Mascolo, Professor of Mobile Systems Department of Computer Science and Technology, University of Cambridge, UK

11.30

Lunch

13:00

Parallel sessions

TBA

TBA

TBA

TBA

TBA

TBA

14.30

Break and check-in

15:30

Poster session

17.30

Social activities

18.30

Dinner and announcement of the winners of DM in AI

20.45

Social activities

23 October 2024

07:00

Morning exercise and breakfast

09:00

Panel debate

  • Anna Rogers, ITU
  • Serge Belongie, Pioneer Centre for AI
  • Anja Bechmann, Scool of Communication and Culture – Media Studies, AU
  • Carsten Schürmann, Dept. of Computer Science, ITU


Moderator: David Budtz Pedersen, Dept. of Communication and Psychology, AAU

10.00

Break

10:30

Parallel sessions

TBA

TBA

TBA

TBA

TBA

TBA

12.00

Lunch

13:15

Parallel sessions

TBA

TBA

TBA

TBA

TBA

TBA

14.45

Break

15:00

Keynote: Beyond models – Applying AI and data science effectively 

Alfred Spector, Visiting scolar, MIT and a Senior Advisor at Blackstone

16.00

Closing session

AI for wearable health

By Cecilia Mascolo, Professor
Dept. of Computer Science and Technology, University of Cambridge, UK

Wearable devices are becoming pervasive in our lives, from smart watches measuring our physiology to wearables for the ear accompanying us in every run or virtual meeting. The monitoring of our health and fitness through sensors and wearables is the focus of much research in the community, however, despite advances in AI on wearable data, many challenges still remain before truly scalable, trustworthy and affordable wellness monitoring becomes a reality.  
 
In this talk I will discuss where commercial systems have gotten to today and highlight the open challenges that these technologies still face before they can be trusted health measurement proxies. Namely, the ability to work in the wild and to cope with the variability of uses; the trade offs that we need to consider with respect to the sensitivity of the data and the use of constrained on device resources; the uncertainty of the prediction over the data and the crucial need for robust, open AI frameworks to benchmark and assess performance in the context of critical applications such as health and fitness. I will mostly use examples from my team’s ongoing research on AI for health and fitness, on-device machine learning and “hearable” sensing to explore the current and future path of AI for wearable data. 

Cecilia Mascolo

Cecilia Mascolo is a Professor of Mobile Systems in the Department of Computer Science and Technology, University of Cambridge, UK. She is director of the Centre for Mobile, Wearable System and Augmented. She is also a Fellow of Jesus College Cambridge and the recipient of an ERC Advanced Research Grant.

She has been Deputy Head of Department between 2018 and 2021. 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 and wearable 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.

Beyond models – Applying AI and data science effectively

By Alfred Spector
Visiting Scholar, MIT and Senior Advisor at Blackstone

Applying artificial intelligence and data science effectively requires a considerably broader focus than just data and machine learning. Based on the speaker and his co-authors’ recent book, Data Science in Context (and an associated MIT Course), this presentation distills these additional challenges into a rubric and illustrates its application with a number of examples.

Beyond the rubric, the presentation also presents useful frameworks for making the complex trade-offs that are present and growing. While the talk should have practical value to those applying and regulating AI and DS, it also illustrates contemporary research challenges.

Alfred Spector

Dr. Alfred Spector is a Visiting Scholar at MIT, and a Senior Advisor at Blackstone. His career has led him from innovation in large scale, networked computing systems to broad engineering and research leadership. He obtained a Ph.D. in computer science from Stanford and a B.A. in applied math from Harvard.

Recently, Dr. Spector co-authored a Cambridge University Press textbook, Data Science in Context: Foundations, Challenges, Opportunities.

Previously, Dr. Spector was CTO, and Head of Engineering at Two Sigma Investments. Before that, he spent eight years as VP of Research and Special Initiatives at Google, and he held various senior-level positions at IBM, including as global VP of Services and Software Research and global CTO of IBM’s Software Business.

Spector was a Hertz Fellow at Stanford and is also a Fellow of both the ACM, and the IEEE. He is a member of the National Academy of Engineering and the American Academy of Arts and Sciences.

AI for wearable health

By Cecilia Mascolo, Professor
Dept. of Computer Science and Technology, University of Cambridge, UK

Wearable devices are becoming pervasive in our lives, from smart watches measuring our physiology to wearables for the ear accompanying us in every run or virtual meeting. The monitoring of our health and fitness through sensors and wearables is the focus of much research in the community, however, despite advances in AI on wearable data, many challenges still remain before truly scalable, trustworthy and affordable wellness monitoring becomes a reality.  
 
In this talk I will discuss where commercial systems have gotten to today and highlight the open challenges that these technologies still face before they can be trusted health measurement proxies. Namely, the ability to work in the wild and to cope with the variability of uses; the trade offs that we need to consider with respect to the sensitivity of the data and the use of constrained on device resources; the uncertainty of the prediction over the data and the crucial need for robust, open AI frameworks to benchmark and assess performance in the context of critical applications such as health and fitness. I will mostly use examples from my team’s ongoing research on AI for health and fitness, on-device machine learning and “hearable” sensing to explore the current and future path of AI for wearable data. 

Cecilia Mascolo

Cecilia Mascolo is a Professor of Mobile Systems in the Department of Computer Science and Technology, University of Cambridge, UK. She is director of the Centre for Mobile, Wearable System and Augmented. She is also a Fellow of Jesus College Cambridge and the recipient of an ERC Advanced Research Grant.

She has been Deputy Head of Department between 2018 and 2021. 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 and wearable 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.