Nasibeh Azadeh Fard Headshot

Nasibeh Azadeh Fard

Assistant Professor

Department of Industrial and Systems Engineering
Kate Gleason College of Engineering
Data Analytics
Healthcare Systems

585-475-2151
Office Location

Nasibeh Azadeh Fard

Assistant Professor

Department of Industrial and Systems Engineering
Kate Gleason College of Engineering
Data Analytics
Healthcare Systems

Education

BS, Iran University of Science and Technology; MS, Ph.D., Virginia Tech

Bio

Dr. Nasibeh Azadeh-Fard received her B.S. in Information Technology Engineering from Iran University of Science and Technology, and her M.S. and Ph.D. in Industrial and Systems Engineering from Virginia Tech. From August 2016 to August 2019, she was a visiting professor in Industrial and Systems Engineering Department at RIT. Prior to joining RIT, she was a postdoctoral fellow at Clemson University, Risk Engineering and System Analytics Center, where she conducted research on risk analysis and risk mitigation action plans for American International Group (AIG) insurance company. 

Dr. Azadeh-Fard’s main research areas include data analytics, healthcare systems engineering, risk analysis, early warning systems, and performance measurement and analysis. Her work has been published in peer reviewed journals including Journal of Patient Safety, Safety Science,  and PLoS ONE.

Selected Publications

  • Azadeh-Fard, N., Ghaffarzadegan, N., Camelio, J., Can a patient’s in-hospital length of stay and mortality be explained by early-risk assessments?, PLoS ONE, 11(9), 2016.
  • Azadeh-Fard, N., Schuh, A., Rashedi, E., Camelio, J., Risk Assessment of Occupational Injuries Using Accident Severity Grade, Safety Science, Volume 76, 2015.
  • Bish, E., Azadeh-Fard, N., Steighner, L., Hall, K., Slonim, A., Proactive Risk Assessment of Surgical Site Infections in Ambulatory Surgery Centers, Journal of Patient Safety, 2014.
585-475-2151

Personal Links

Select Scholarship

Journal Paper
Shariatpanahi, Seyed Peyman, et al. "Assessing the Effectiveness of Disease Awareness Programs: Evidence From Google Trends Data For the World Awareness Dates." Telematics and Informatics 34. 7 (2017): 904-913. Web.
Azadeh-Fard, Nasibeh, Navid Ghaffarzadegan, and Jaime Camelio. "Can Early Risk Assessments Predict A Patient’s Hospital Length of Stay and Mortality?" PLoS ONE 11. 9 (2016): 1-9. Web.

Currently Teaching

ISEE-561
3 Credits
In any system where parameters of interest change, it may be of interest to examine the effects that some variables exert (or appear to exert) on others. "Regression analysis" actually describes a variety of data analysis techniques that can be used to describe the interrelationships among such variables. In this course we will examine in detail the use of one popular analytic technique: least squares linear regression. Cases illustrating the use of regression techniques in engineering applications will be developed and analyzed throughout the course.
ISEE-661
3 Credits
In any system where parameters of interest change, it may be of interest to examine the effects that some variables exert (or appear to exert) on others. "Regression analysis" actually describes a variety of data analysis techniques that can be used to describe the interrelationships among such variables. In this course we will examine in detail the use of one popular analytic technique: least squares linear regression. Cases illustrating the use of regression techniques in engineering applications will be developed and analyzed throughout the course.
ISEE-752
3 Credits
This course presents the primary concepts of decision analysis. Topics important to the practical assessment of probability and preference information needed to implement decision analysis are considered. Decision models represented by a sequence of interrelated decisions, stochastic processes, and multiple criteria are also addressed. We cover EMV and Non-EMV decision-making concepts. Finally, the organizational use of decision analysis and its application in real-world case studies is presented.