Temiloluwa (Temi) Olubanjo is originally from Lagos, Nigeria. Â She received her B.S. in Electrical Engineering from the University of Texas at Austin in December 2010. After which, she worked for The Dow Chemical Company in Houston, TX for one year before continuing on to graduate school. She received her M.S. and Ph.D. from Georgia Institute of Technology in 2014 and 2016, respectively. With her PhD, post-doc and future faculty position, she seeks to continually explore the question: how can engineering innovation facilitate improved health? Her PhD focused on developing robust signal-processing algorithms for objective monitoring of dietary behavior towards obesity management. Her career goal is to flourish in research and development of preventive, diagnostic, and assistive health technology. In her leisure, she loves to spend time with her family and friends. She also loves to dance and enjoys traveling.
Research Topic: SOLVD â€“ Smartphone and Online Usage-based Evaluation for Perinatal Depression
As a post-doctoral fellow, Temi will be working under the mentorship of Electrical Engineering professors, Dr. Ashutosh Sabharwal and Dr. Ashok Veeraraghavan in the Scalable Health Initiative Laboratory. The objective of her post-doctoral research (SOLVD) is to evaluate when and to what extent quantitative digital data correlates with actively queried mental status for depressed patients. Unlike physiological disorders that can be assessed using direct measurements from the human body (e.g. a blood test), diagnosis in mental health does not rely on objective data. In todayâ€™s society, mobile interaction accounts for a substantial part of an individualâ€™s daily activities. On average, users can interact with their smartphones up to 3.3 hours per day for diverse purposes and applications. Such voluntary interactions can provide an inlet to tracking the userâ€™s emotional state particularly for depression and other mental illnesses. A data-driven approach that repurposes already existing smartphone and online usage can play a notable role in mental health tracking. Unobtrusive and continuous monitoring can contribute to on-time emotion and behavioral interventions.