Multivariate Menstrual Tracking

I am interested in creating accurate, inclusive, accessible, and educational menstrual trackers. This work ranges from exploring the nuances for menstrual trackers whom have received minimal sexual education to improving prediction algorithms based on a plethora of physiological signals.

Investigating Menstrual Prediction Mechanisms and Designs for Menstrual Tracking (Advised by Dr. Khai Truong and Dr. Alex Mariakakis)

We collect and analyze a plethora of physiological data collected over multiple menstrual cycles and self-reported qualitative data for better prediction markers. We collect data such as blood glucose, body temperature, respiratory rate, heart rate, sleep cycle, hormones etc. using smart watches, continuous glucose monitors, hormone tests, etc.

Investigating Culturally Responsive Design for Menstrual Tracking and Sharing Practices (Advised by Dr. Neha Kumar and Dr. Elizabeth Mynatt)

Women who often lack familial and societal sex education, as well as access to specialty health care, suffer from a variety of issues from feelings of lack of control to debilitating and unrelievable pain, yet knowing when something is wrong with their menstrual health is rarely afforded by current menstrual trackers.