Afsaneh Doryab, Ph.D.
My research is at the intersection of health, mobile and ubiquitous computing, AI, and Human Computer Interaction.
I work on computational modeling of human behavior from data streams collected through mobile, wearable, and embedded sensors. Examples of my work 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.
I also work on intelligent applications for social good. My recent work in Context-aware Peer-to-Peer economic exchange is focused on connecting communities of people through mobile technology to enable successful and meaningful service transactions, especially in low-income communities. In my research, I draw on methods from Machine Learning, Data Mining, Statistics, and Human-Computer Interaction.
Recent Mentions in the Community
Doryab Presents the First Paper on Modeling Biobehavioral Rhythms for Predicting Rehospitalization Risk
ESE Weekly News | September 27th, 2019
The paper titled "Modeling Biobehavioral Rhythms with Passive Sensing in the Wild: A Case Study to Predict Readmission Risk after Pancreatic Surgery" was presented at Ubicomp, the Association for Computing Machinery (ACM) leading conference on ubiquitous and mobile computing. Biobehavioral rhythms estimated from wearable device data collected from 49 patients before pancreatic surgery, in hospital, and after discharge were shown to be predictive of readmission risk with accuracies above traditional clinical approaches to readmission risk stratification. The results demonstrate the feasibility of using passively sensed consumer sensor data to characterize biobehavioral rhythms as well as the potential value of rhythm modeling in predicting health-related outcomes such as readmission risk. The research in computational modeling of human rhythms is funded by the National Science Foundation.
Doryab's Paper on the Estimation of Symptom Severity During Chemotherapy from Passively Sensed Data Selected as the Best Cancer Informatics Paper by International Medical Informatics Association
ESE Weekly News | September 20th, 2019
The International Medical Informatics Association (IMIA) publishes the annual IMIA Yearbook of Medical Informatics. The Editorial Board of the 2019 IMIA Yearbook of Medical Informatics selected article entitled: "Estimation of symptom severity during chemotherapy from passively sensed data: Exploratory study" which was led by Dr. Afsaneh Doryab for listing in the 2019 edition of the Yearbook as one of the best articles published in 2018 in the ‘Cancer Informatics’ subfield of medical informatics. The 2019 IMIA Yearbook of Medical Informatics which special theme was "Artificial Intelligence in Health: New Opportunities, Challenges, and Practical Implications", is available here. Open access, all items are free to download.