Office for the Promotion of Women in Science, Engineering, and Mathematics
Faculty Profile
Ierapetritou, Marianthi
Marianthi's Profile
Marianthi's Story
Ierapetritou, Marianthi

Phone: 732-445-2971

Ph.D., Imperial College, 1995

Professional Summary/CV [.PDF]

Department of Chemical and Biochemical Engineering, School of Engineering, New Brunswick; Rutgers
Areas of Interest
Process Operations, Design and Synthesis of Flexible Manufacturing Systems, Modeling of Reactive Flow Processes, and Metabolic Engineering.
Teaching Areas
Process Simulation and Control, Process Systems Engineering, Advanced Transport Phenomena, Design of Separation Processes, Analytical Methods for Chemical and Biochemical Engineering.
Memberships and Professional Service
Elected as a Trustee of CACHE; Memberships include American Institute of Chemical Engineers (AIChE), Institute of Operations Research and Management Sciences (INFORMS), Society of Industrial and Applied Mathematics (SIAM), and AIChE Computing and Systems Technology (CAST); Nominated and elected 10a (Systems and Process Design) Division Director for 2006.
Grants, Honors, and Awards
Board of Trustees Research Fellowship for Scholarly Excellence, Rutgers University, 2004; Teaching Excellence Award Rutgers University, 2002; National Science Foundation (NSF) CAREER AWARD, 2000-2004; PRF-ACS Type G Award, 2000-2002; New Jersey Space Grant Consortium - NASA, 2000-2001; Grants include: NSF Award 2006-2009 "Systematic Mathematical Strategies for Stochastic Modeling and Uncertainty in Production Planning and Scheduline"; Office of Naval Research 2006-2009 "Efficient Characterization of of Combustion Fuels"; National Center of Excellence for Environmental Bioinformatics and Computational Toxicology - EPA, 2005-2010.
Academic Interests and Plans
My research interests include Computer-Aided Process and Product Design, Process Planning, Scheduling and Supply Chain Management, Reactive Scheduling under Uncertainty, Uncertainty Considerations in Process Design and Operations, Decomposition based Techniques for Multi-scale Systems, Reaction Model Reduction, Optimization of Complex Systems, Modeling and Optimization of Metabolic Networks, and Improved Hepatocyte Functionality for Bioartificial Liver applications.