Computational Medicine for Sensing Mental and Physical Health

Join us for the third annual Christine Mona Khademi lecture with Dr. Rose T. Faghih.

As new physiological sensing technologies become available for continuous monitoring of physiological signals, the dynamic response to external influences such as environmental inputs can be quantified. This research focuses on developing mathematical algorithms for dynamically tracking mental and physical health states in the presence of different interventions. (1) Mental Health Focus: We design algorithms for a closed-loop neural wearable architecture called MINDWATCH for mental and cognitive well-being. We first infer arousal-related autonomic nervous system (ANS) activations. Then, we model and decode cognitive arousal and performance brain states where the inferred ANS activations and behavioral data are used as cognitive arousal and performance observations, respectively. We use neurofeedback to close the loop and modulate cognitive arousal and performance. (2) Physical Health Focus: We investigate clinical data from patients to study inflammation, fatigue, and metabolism and decode hidden health states (e.g., energy and pro-satiety states) dynamically. The ultimate goal is to design toolsets that can provide clinically relevant information using biosensors to prevent, diagnose, and manage health conditions.

Dr. Rose T. Faghih is a tenured associate professor of Biomedical Engineering at the New York University (NYU) where she directs the Computational Medicine Laboratory. She received a bachelor’s degree (summa cum laude) in Electrical Engineering (Honors Program Citation) from the University of Maryland, and S.M. and Ph.D. degrees in Electrical Engineering and Computer Science with a minor in Mathematics from Massachusetts Institute of Technology (MIT). She completed her postdoctoral training at the Department of Brain and Cognitive Sciences and the Picower Institute for Learning and Memory at MIT as well as the Department of Anesthesia, Critical Care and Pain Medicine at the Massachusetts General Hospital. Dr. Faghih is the recipient of various awards including a 2025 NYU Tandon School of Engineering Jacobs Excellence in Education Innovation Award, a 2024 IEEE Engineering Medicine and Biology Society (EMBS) Early Career Achievement Award, a 2023 National Institutes of Health (NIH) Maximizing Investigators’ Research Award for Early Stage Investigators, a 2020 National Science Foundation CAREER Award, a 2020 MIT Technology Review Innovator Under 35 award, and a 2016 IEEE-USA New Face of Engineering award. In 2020, she was featured by the IEEE Women in Engineering Magazine as a “Woman to Watch”. She is on the editorial board of PNAS Nexus by the National Academy of Sciences, IEEE Transactions on Biomedical Engineering and IEEE Transactions on Neural Systems and Rehabilitation Engineering. Moreover, she is a senior member of IEEE and currently an IEEE Engineering in Medicine and Biology Society Administrative Committee Technical Representative. Dr. Faghih is the senior author of a Biomedical Engineering book titled Bayesian Filter Design for Computational Medicine published by Springer. Her research interests include wearable technologies, and medical cyber-physical systems, as well as neural and biomedical signal processing.

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