Hazhir Rahmandad
Born
NationalityIranian American
Alma materSharif University of Technology
Massachusetts Institute of Technology[1]
AwardsGold Medal International Chemistry Olympiad, Moscow (1996)[2]
Scientific career
FieldsSystems Engineering, Management, Organizational Behavior
InstitutionsVirginia Tech
MIT Sloan School of Management
Doctoral advisorJohn Sterman[3]

Hazhir Rahmandad (Persian: هژیر رحمانداد) is an Iranian American Scientist and Engineer. Dr. Rahmandad is a dynamic modeling expert. His research applies dynamic modeling to a broad range of problems in strategy, organizational learning, and public health.[4]

Education

Rahmandad obtained his B.Sc. in Industrial Engineering from Sharif University of Technology.[1][5][6] He holds a Ph.D. from Massachusetts Institute of Technology (2005),[1] where he worked under the supervision of John Sterman.

Career

Rahmandad is an Associate Professor in the System Dynamics group at MIT Sloan School of Management[5] where he teaches simulation modeling and system dynamics.[7]

Before joining MIT, Rahmandad was an Associate Professor of Industrial and Systems Engineering in the College of Engineering at Virginia Tech.[6] Hazhir also contributes to expanding the system dynamics modeling toolbox through advancing parameter estimation and validation methods for dynamic models. He has published in diverse journals including Management Science, Organization Science, Strategic Management Journal, PlosOne, Epidemiology and Infection, International Journal of Obesity, and System Dynamics Review among others. He has been a reviewer for over 20 NIH and NSF panels and over two dozen different journals, and his research has been funded by the National Science Foundation, National Institutes of Health, Department of Housing and Urban Development, and multiple private sector firms, among others.[1][5][6]

Awards and honors

Gold Medal, International Chemistry Olympiad, Moscow (1996)[2]

References

This article is issued from Wikipedia. The text is licensed under Creative Commons - Attribution - Sharealike. Additional terms may apply for the media files.