*ABSTRACT*: Traditional healthcare is centered around face-to-face interactions between patients and clinicians. While these human relationships are important for establishing empathetic and ethical care, they limit the extent to which healthcare can be accessed and delivered. Ubiquitous technologies like smartphones and wearables can augment traditional healthcare workflows by increasing the access that people have to health-monitoring tools. Rather than viewing healthcare as a reactive endeavor, we can work towards proactive approaches like preventative screening, continuous disease management, and informative visualizations that empower all stakeholders to make informed and timely decisions. To achieve this vision, my research group applies signal processing and machine learning on sensor data to measure vital signs and infer symptoms. Since these technologies may sometimes be intended for people without medical training, my group also explores how such tools should be designed to achieve clinically relevant goals. In this talk, I will highlight three projects: (1) acoustic cardiac sensing with earbuds, (2) passive speech analysis for respiratory monitoring, and (3) accurate and informative menstrual health tracking.
*BIO*: Alex Mariakakis is an Assistant Professor in the Department of Computer Science at the University of Toronto and an Affiliate Scientist at Techna. He runs the Computational Health and Interaction (CHAI) lab, which leverages ubiquitous and emergent technologies to address problems related to people’s health and wellbeing. His research not only creates new sensing technologies for measuring physiological, behavioral, and contextual health indicators, but also examines the implications of these technologies in people’s hands. Alex and his research group deliver innovative solutions using expertise in multiple subareas of computer science, particularly machine learning, signal processing, computer vision, and human-computer interaction.
Alex received his Ph.D. from the School of Computer Science and Engineering at the University of Washington. As a student, he received the National Science Foundation Graduate Research Fellowship, the Qualcomm Innovation Fellowship, and the Gaetano Borriello Outstanding Student Award at UbiComp 2018. His work has garnered multiple Best Paper Awards at ACM venues (CHI, COMPASS) and significant attention from media outlets ranging from the BBC to National Geographic.
