Ahmed Aziz Ezzat

Assistant Professor

Industrial and Systems Engineering

Phone:848-445-3625
Fax:732-445-5467
Email:aziz.ezzat@rutgers.edu
Office:CORE Building, Room 228
Office Hours: By Appointment
Website: Renewables & Industrial Analytics (RIA) Research Group
Ahmed Aziz Ezzat is an Assistant Professor of Industrial and Systems Engineering at Rutgers University. He received his Ph.D. in Industrial and Systems Engineering from Texas A&M University in 2019, and his B.Sc. degree in Industrial and Management Engineering from the Arab Academy for Science and Technology in Alexandria, Egypt, in 2013. His broad research interests are in the areas of spatio-temporal data and decision sciences, probabilistic forecasting, quality and reliability engineering, with focus on renewable energy analytics and materials science informatics. Dr. Ezzat’s work has been published in journals such as The Annals of Applied Statistics, Technometrics, IEEE Transactions on Sustainable Energy, Applied Energy, among others. His awards include the 2020 IIF-SAS® research award (methodology track), the 2020 Rutgers OAT Teaching Award, 2019 ISEN Outstanding Graduate Student at Texas A&M, INFORMS Outstanding Member of the Year at Texas A&M in 2018, First Place at the QSR Student Poster and Interaction Competition at the 2017 INFORMS Annual Meeting, Best Oral Presentation at the 2016 Texas A&M Conference on Energy, and the IISE Sierleja Memorial Fellowship in 2014. His research has been supported by the National Science Foundation (NSF), Institute of International Forecasters and SAS corporation, and the Rutgers Energy Institute. He is a member of IISE, IEEE-PES, and INFORMS.

Education

• Ph.D., Industrial & Systems Engineering, Texas A&M University, USA, 2019

• M.Sc. and B.Sc., Industrial & Management Engineering, Arab Academy for Science & Technology, Alexandria, Egypt, 2013 and 2016, respectively. 

Honors

• IIF-SAS Research Award (Methodology Track), 2020. 

• Finalist, 3 Minute Thesis Competition at Texas A&M University, for the presentation titled: "Wind Energy: A New Solution To a 5000-year Old Problem" (2019)

• Outstanding Member of The Year, Texas A&M INFORMS Chapter (2018)

• Outstanding Graduate Student of The Year, Department of Industrial & Systems Engineering, Texas A&M University (2019)

• First Place, Quality, Statistics and Reliability (QSR) Student Poster and Interaction Competition, INFORMS Annual Meeting, Houston, TX, USA (2017)

• Bronze Prize, South Eastern Texas Chapter of the American Statistical Association (ASA) Poster Competition, College Station, TX, USA (2016)

• Best Oral Presentation, Texas A&M Conference on Energy, College Station, TX, USA (2016)

• Recipient of Sierleja Memorial Fellowship, Institute of Industrial and Systems Engineers (IISE) (2014)

• Third Place, Best Student Paper Competition, 4th International Conference on Industrial Engineering and Operations Management (IEOM), Bali, Indonesia (2013)

Professional Affiliations

IISE, INFORMS, IEEE-PES

Research Interests

Spatio-temporal Data Science, Renewable Energy Analytics and Forecasting, Materials Informatics, Physics-informed Statistical Learning, Quality and Reliability Engineering

Selected Publications

• A. Ezzat, “Turbine-specific Short-term Wind Speed Forecasting Considering Within-farm Wind Field Dependencies and Fluctuations,” Applied Energy, 269, 115034, 2020.

• A. Ezzat, M. Jun, Y. Ding, “Spatio-temporal short-term wind forecast: A calibrated regime-switching method,” The Annals of Applied Statistics, 13(3), 1484-1510, 2019. 

• A. Ezzat, M. Jun and Y. Ding, "Spatio-temporal Asymmetry of Local Wind Fields and Its Impact on Short-term Wind Forecasting," IEEE Transactions on Sustainable Energy, Vol. 9(3), pp. 1437-1447, 2018.

• M. Mahmoudi, A. Ezzat, A. Elwany, "Layer-wise Anomaly Detection in Laser Powder Bed Fusion Metal Additive Manufacturing," Vol. 141(3), pp. 031002-1-12, 2019, ASME Transactions, Journal of Manufacturing Science and Engineering, 2019.

• A. Ezzat, A. Pourhabib, Y. Ding, "Sequential Design for Functional Calibration of Computer Models," Vol. 60(3). pp. 286-296, Technometrics, 2018.