Dr. Onur Asan
Director
Dr. Asan is an Associate Professor in the School of Systems and Enterprises at the Stevens Institute of Technology. Dr. Asan received his Ph.D. in Industrial and Systems Engineering from the University of Wisconsin-Madison in 2013. He worked as Assistant Professor in Center for Patient Outcomes Research at Medical College of Wisconsin between 2013-2018. He started his position at Stevens in September 2018.
Dr. Asan has conducted research to improve patient-centered care and outcomes using health information technologies. He used human factors and human-computer interaction tools to explore how new technologies transform patient-centered care, care coordination, and patient safety. Dr. Asan’s work has been funded by federal and private institutions including National Health Institute (NIH), Agency for Health Care Research and Quality (AHRQ), National Science Foundation (NSF), Patient-Centered Outcomes Research Institute (PCORI), and Clinical and Translational Science Institute (CTSI).
Lab Members
Olga Strachna (MSc)
Doctoral Candidate [2020 - Present]
Olga completed her Master at Health informatics Departmentn at Cornell University. She started her Phd at HSI lab in Spring 2020, and currently working in the area of Medical AI and Decision Making in the context of Cancer Care. She also has appointment at MSK Cancer Center in New York City
Mostaan Lotfalian Saremi, MS
Doctoral Candidate [2023-Present]
Mostaan completed his first master’s in Material science from Amir Kabir University- Tehran , Iran and his second master in Business Analytics from Stevens Institute of Technology. He currently seeks his PhD degree in Systems Engineering. His research focuses on factors that impact Human reliance in AI systems.
Olya Rezaeian, MS
Doctoral Candidate [2022-Present]
Olya finished her master's in Industrial Engineering with a specialization in Engineering Management at Amirkabir University in Tehran, Iran. Since the fall of 2022, She has been working towards Ph.D. in Engineering Management, concentrating on the impact of explainability in AI systems.