Computer Science Research Seminar Series: Multimodal Machine Learning and Human-Centered Computing for Health and Wellbeing

  • Starts: 10:00 am on Friday, April 21, 2023
Multimodal Machine Learning and Human-Centered Computing for Health and Wellbeing

Guest Speaker: Dr. Akane Sano, Assistant Professor of Electrical and Computer Engineering, Rice University

Moderated by: Dr. Reza Rawassizadeh, Assistant Professor of Computer Science, Boston University Metropolitan College

Description: What if we can design data driven + human centered personalized feedback loop systems for patients, clinical stakeholders, and healthy people for processing and modeling multimodal clinical and moment-to-moment data and managing and improving health?

First, I will introduce potential and challenges in designing such a system by combining multimodal measurements such as electric health records, fMRI, and clinical assessment with field measurements via mobile and remote sensing.

Second, I will introduce a series of studies, algorithms, and systems we have developed for addressing these issues and measuring, predicting, and supporting personalized health and wellbeing.

More specifically, I will talk about (1) leveraging unlabeled data to design robust but interpretable models, (2) approaches that positively transfer knowledge from multiple modalities to fewer modalities in model deployment in the real world, and (3) balancing bias and performance in health prediction machine learning models and collecting diverse data samples in mobile health systems.

I will also discuss learned lessons and potential future directions in health and wellbeing research.

Speaker bio: Akane Sano is an Assistant Professor at Rice University, Department of Electrical Computer Engineering, Computer Science, and Bioengineering. She directs the Computational Wellbeing Group and is a member of Rice Digital Health Initiative. Her research includes data science, machine learning, and human-centered intelligent systems for health and wellbeing and spans in the field of affective computing, ubiquitous and wearable computing, and biobehavioral sensing and analysis/modeling. She has been developing tools, algorithms, and systems to measure, forecast, understand and improve health and wellbeing using multimodal data from mobile and wearable devices in daily life settings, and clinical assessment. She received her Ph.D. at the Massachusetts Institute of Technology and her MEng and BEng at Keio University, Japan. Her recent awards include the NSF Career Award, the Best of IEEE Transactions on Affective Computing 2021, and the Best Paper Award at IEEE BHI 2019 conference.