Ipek Ensari, PhD
About Me
Ipek Ensari, PhD, is an Assistant Professor at the Windreich Department of Artificial Intelligence and Human Health at the Icahn School of Medicine and the Hasso Plattner Institute of Digital Health at Mount Sinai. She investigates mobile health (mHealth) and machine learning methods for complex patient-generated data toward improving chronic disease characterization and patient self-management. Her work is grounded in women’s reproductive health conditions (e.g., endometriosis, chronic pelvic pain disorders) and populations at increased risk for health disparities (e.g., sexual and gender minorities). To this end, her lab conducts studies investigating the potential of utilizing longitudinal multi-modal data, NLP methods, and functional data approaches to improve clinical decision-making in women's reproductive health using mHealth data. She is currently leading a NIH-funded project that aims to design digital patient reported outcome measures for improved real-time measurement and monitoring of chronic pain symptoms outside of the clinic for women with chronic pelvic pain disorders. Ipek completed her doctorate at the University of Illinois at Urbana-Champaign and post-doctoral training at Columbia University.
Language
Position
Research Topics
Behavioral Health, Bioinformatics, Biomedical Informatics, Biostatistics, Diversity, OBGYN, Pain, Patient Care, Patient Centered Outcomes Research, Pediatrics, Personalized Medicine, Rehabilitation, Reproductive Biology, Sleep Medicine, Technology & Innovation
About Me
Ipek Ensari, PhD, is an Assistant Professor at the Windreich Department of Artificial Intelligence and Human Health at the Icahn School of Medicine and the Hasso Plattner Institute of Digital Health at Mount Sinai. She investigates mobile health (mHealth) and machine learning methods for complex patient-generated data toward improving chronic disease characterization and patient self-management. Her work is grounded in women’s reproductive health conditions (e.g., endometriosis, chronic pelvic pain disorders) and populations at increased risk for health disparities (e.g., sexual and gender minorities). To this end, her lab conducts studies investigating the potential of utilizing longitudinal multi-modal data, NLP methods, and functional data approaches to improve clinical decision-making in women's reproductive health using mHealth data. She is currently leading a NIH-funded project that aims to design digital patient reported outcome measures for improved real-time measurement and monitoring of chronic pain symptoms outside of the clinic for women with chronic pelvic pain disorders. Ipek completed her doctorate at the University of Illinois at Urbana-Champaign and post-doctoral training at Columbia University.
Language
Position
Research Topics
Behavioral Health, Bioinformatics, Biomedical Informatics, Biostatistics, Diversity, OBGYN, Pain, Patient Care, Patient Centered Outcomes Research, Pediatrics, Personalized Medicine, Rehabilitation, Reproductive Biology, Sleep Medicine, Technology & Innovation