The workshop will focus on designing sensors, systems, and analytics to monitor a person’s metabolic health, detect events that are important for assessing metabolic health, and to design interventions for maintaining metabolic health and preventing chronic conditions arising from metabolic complications. The workshop will inform the audience about state-of-the-art research and future directions on end-to-end systems including sensor development, system deployment, data generation, and analytics to process and interpret findings. The workshop aims to cover the following topics:
- Measurement: this refers to search on developing sensors and tools for measuring behavioral, physiological, and biological determinants of metabolic health such as diet, physical activity, stress, and microbiome.
- Phenotyping: this refers to research on discovering and calculating biomarkers of metabolic health from multimodal wearable, environmental, and health data.
- Intervention: this topic refers to effective approaches that promote initiation and enhance maintenance of interventions that improve metabolic health, as well as new ways to design interventions, and how to identify optimal personalized interventions for precision health.
2:45 – 3:00 | Introduction from Chairs
3:00 – 4:00 | Talks
- List of Speakers (Mini-Talks)
- Amir Rahmani (University of California Irvine): Exploring the Significance of Multimedia Food Logging and Personal Models in Food Computing
- Lauren Lederrer (Duke University): TBD
- Elena Idi and Francesco Prendin (Universita Degli Studi Di Padova): Detecting malfunctioning of CGM sensor and insulin pumps for safer T1D treatment
- Hassan Ghasemzadeh (Arizona State University): Counterfactual Explanations for Simulating Behavioral Treatments
- Bobak J Mortazavi (Texas A&M University): Multimodal Learning for Automated Nutrition Monitoring
4:00 – 4:15 | Break
4:15 – 5:15 | Panel on Digital Twin for Metabolic Health
- Hassan Ghasemzadeh (Arizona State University)
- Amir Rahmani (University of California Irvine): Topic: Digital twin (or personal model) augmented LLMs for personalized food recommendations
- Ali Roghanizad (Duke University): TBD
- Giacomo Cappon (Universita Degli Studi Di Padova): Digital twin methods and decision support systems in diabetes therapy
- Bobak J. Mortazavi (Texas A&M University): Automated and Personalized Nutrition Monitoring
5:15 – 5:30 | Closing Remarks