Maryam Pishgar
Lecturer of Industrial and Systems Engineering
Education
- Doctoral Degree, University of Illinois at Chicago
- Master's Degree, University of Illinois at Chicago
- Bachelor's Degree, University of Illinois at Chicago
Biography
Dr. Maryam Pishgar is a faculty member in the Department of Industrial and Systems Engineering at the University of Southern California (USC), where she has served since 2023. She teaches graduate-level courses including Predictive Analytics, Data Analytics Consulting, Text Analytics, Data Mining, Database Management, and Optimization Methods for Analytics.Dr. Pishgar earned her B.S., M.S., and Ph.D. degrees in Industrial Engineering and Operations Research from the University of Illinois at Chicago (UIC), with a specialization in Data Science and research interests in artificial intelligence, deep learning, and large-scale data analytics. Following completion of her Ph.D. in 2023, she completed a one-semester postdoctoral research fellowship in the Department of Biomedical Engineering at the University of California, Irvine.
In parallel with her teaching, Dr. Pishgar maintains an active research program focused on large-scale healthcare data analytics. She has supervised more than 20 master’s students and collaborated with several Ph.D. students. Her work has received over 600 citations (h-index 13) and includes numerous publications in leading Q1 journals such as the IEEE Journal of Biomedical and Health Informatics. One of her papers in BMC Medical Informatics and Decision Making received more than 50 citations within its first year. Dr. Pishgar is also the Founder of Solix Data Driven Business Solutions, serves as a reviewer for multiple leading journals and conferences, and is a senior member of the IEEE and a member of the ASSP.
Research Summary
Dr. Maryam Pishgar’s research focuses on developing data-driven and artificial intelligence–based methods to improve healthcare delivery, clinical decision-making, and patient outcomes. Her work lies at the intersection of machine learning, deep learning, process mining, and large-scale healthcare analytics, with the goal of transforming complex clinical data into actionable and interpretable insights.Her research integrates predictive modeling, optimization, and advanced statistical learning techniques to address challenges in modern healthcare systems. She has extensive experience working with large-scale electronic health record (EHR) and critical care datasets to develop models that identify high-risk patients, predict clinical outcomes, and support personalized treatment strategies. These efforts contribute to the design of intelligent clinical decision support tools that enable earlier interventions and more efficient care delivery.
A major component of Dr. Pishgar’s work involves applying process mining and temporal analytics to understand patient care pathways and clinical workflows. By analyzing longitudinal healthcare data, her research uncovers variations in treatment processes, identifies inefficiencies, and supports data-driven improvements in healthcare operations. She also investigates interpretable machine learning approaches that enhance transparency and trust in AI-enabled decision systems.
In addition, her research explores emerging areas such as synthetic healthcare data generation, privacy-preserving analytics, and the integration of structured and unstructured clinical data using natural language processing and multimodal learning techniques. Dr. Pishgar has published extensively in leading Q1 journals, including the IEEE Journal of Biomedical and Health Informatics, and her work has received more than 600 citations. She has mentored over 20 master’s students and collaborates closely with interdisciplinary teams across engineering, medicine, and data science to advance data-driven healthcare innovation.
Appointments
- Daniel J. Epstein Department of Industrial and Systems Engineering
- OHE 310U
- Olin Hall of Engineering
- 3650 McClintock Ave., Los Angeles, CA 90089
- USC Mail Code: 193
- pishgar@usc.edu