1. BeWell: Sensing Sleep, Physical Activities and Social Interactions to Promote Wellbeing
2. Identifying Behavioral Phenotypes of Loneliness and Social Isolation with Passive Sensing: Statistical Analysis, Data Mining and Machine Learning of Smartphone and Fitbit Data
3. If It’s Convenient: Leveraging Context in Peer-to-Peer Variable Service Transaction Recommendations
4. Estimation of Symptom Severity During Chemotherapy from Passively Sensed Data: Exploratory Study
Research Experience
Research projects involve identifying behavioral phenotypes of loneliness and social isolation through passive sensing, and connecting communities of people through mobile technology to enable successful and meaningful service transactions, especially in low-income communities.
Education
Ph.D. in Computer Science, UVA School of Engineering and Applied Science; Advisor: Not explicitly mentioned
Background
Research Interests: Computational modeling of human behavior; Professional Field: Machine Learning, Data Mining, Statistics, and Human-Computer Interaction; Introduction: Works on computational modeling of human behavior from data streams collected through mobile, wearable, and embedded sensors. Examples in the health domain include detection of behavior change in people with depression, predicting mania-depression episodes in bipolar disorder, estimation of symptom severity in cancer patients, and modeling of surgical activities inside the operating room. Also works on intelligent applications for social good.