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-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-789
3 Credits
Topics and subject areas that are not regularly offered are provided under this course. Such courses are offered in a normal format; that is, regularly scheduled class sessions with an instructor.
ISEE-610
3 Credits
Computer-based simulation of dynamic and stochastic systems. Simulation modeling and analysis methods are the focus of this course. A high-level simulation language such as Simio, ARENA, etc., will be used to model systems and examine system performance. Model validation, design of simulation experiments, and random number generation will be introduced.
ISEE-799
1 - 3 Credits
This course is used by students who plan to study a topic on an independent study basis. The student must obtain the permission of the appropriate faculty member before registering for the course. Students registering for more than four credit hours must obtain the approval of both the department head and the adviser.
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-510
3 Credits
Computer-based simulation of dynamic and stochastic systems. Simulation modeling and analysis methods are the focus of this course. A high-level simulation language such as Simio, ARENA, etc., will be used to model systems and examine system performance. Model validation, design of simulation experiments, and random number generation will be introduced.
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 considered.
ISEE-325
3 Credits
This course covers statistics for use in engineering as well as the primary concepts of experimental design. The first portion of the course will cover: Point estimation; hypothesis testing and confidence intervals; one- and two-sample inference. The remainder of the class will be spent on concepts of design and analysis of experiments. Lectures and assignments will incorporate real-world science and engineering examples, including studies found in the literature.