Ali Kargarandehkordi is a PhD candidate in Computer Science at the University of Hawai‘i at Mānoa, with a research focus at the intersection of mobile sensing, artificial intelligence, and digital health. His work centers on developing personalized machine learning models using biosignal data from wearable devices (e.g., Fitbit) to detect and predict mental health conditions such as stress-induced blood pressure spikes, stress and anxiety, and substance use behaviors. Ali leads and contributes to projects that combine time-series analysis, hybrid deep learning models, and real-world data collection via wearables and ecological momentary assessments (EMA). He is also the developer of several mobile health applications, such as BanAware, CardioMate, STAND, and LabelLab, which support behavioral health monitoring and intervention. Ali collaborates with researchers at UCSF and is passionate about translating AI-driven insights into meaningful clinical and public health outcomes.
LinkedIn: https://www.linkedin.com/in/ali-kargarandehkordi-b11b0185/
Personal Website: https://aliknd.github.io/