Current areas of research include:

Please check faculty pages to see their current projects on these topics. 


IEMS research in statistics for enterprise engineering addresses emerging challenges in statistical modeling, analysis and experimental design for transforming data into knowledge. Methodologies and applications are diverse: Recent research includes statistical methods for the design and analysis of computer simulation experiments, such as finite element simulation for improving vehicle crashworthiness and discrete-event simulation for optimizing production facilities; data mining large industrial databases for discovering root causes of poor quality and other buried information; statistical modeling of credit card customer data for strategic risk management; design and analysis of clinical trials in healthcare; and data mining for business intelligence. Faculty in this group include Bruce Ankenman, Dan Apley, Barry Nelson, and Ajit Tamhane.


Financial engineering (See Official Website) develops mathematical and statistical models to optimize investment portfolios, manage financial risk, and design and value financial products. Financial engineering also devises computational algorithms to implement these models and calibrate them to financial market data. Students working in financial engineering prepare by undertaking rigorous training in probability theory and stochastic processes, statistics, simulation, and numerical methods, as well as in financial engineering. Recent Ph.D. graduates have developed stochastic models of interest rates, credit risk, mortgage prepayments, and convertible bonds; have invented methods to precisely estimate critical financial risk measures; and developed efficient numerical algorithms to compute option prices in jump-diffusion models. Faculty in this group include Vadim Linetsky, Barry Nelson and Jeremy Staum.


Faculty working in healthcare engineering (See Official Page)apply methods from optimization, statistics, stochastic processes, decision analysis and simulation to problems arising in healthcare systems. Recent research includes assessing the cost-effectiveness of medical interventions such as cancer screening, hip replacement, and contact tracing for infectious disease; deriving optimal radiation treatment strategies; inference for gene regulatory networks; extensions of the quality-adjusted life year model; and Bayesian approaches for probabilistic sensitivity analysis in medical cost-effectiveness. Faculty and students collaborate with centers, departments and schools at Northwestern, including the Fineberg School of Medicine, the Institute for Healthcare Studies, the School of Public Health, and the Department of Chemical and Biological Engineering, as well as institutions and universities in the greater Chicago area including Northwestern Memorial Hospital and Evanston-Northwestern Healthcare. Faculty in this group include Benjamin Armbruster, Mark Daskin, Gordon Hazen, and Sanjay Mehrotra.


Optimization research in IEMS derives new models and methods for minimization or maximization of functions of large numbers of decision variables, subject to restrictions on the space of acceptable decisions. Optimization problems are found throughout operations research -- in production, scheduling, packing, location, and design, for instance -- and more broadly in civil, mechanical, electrical, biomedical and other fields of engineering, in computational economics, and in varied aspects of management including portfolios, hedging, and auctions. The study of optimization begins with fundamentals in analysis and linear algebra, proceeds through the investigation of many algorithms and the diverse formulations to which they apply, takes in services and languages for communicating between formulations and algorithms, and eventually involves specializations to particular application domains. Faculty in this group include Mark Daskin, Robert Fourer, Tito Homem-de-Mello, Diego Klabjan, Sanjay Mehrotra, Jorge Nocedal, and Karen Smilowitz.


Research on organizational theory and systems analysis in IEMS focuses on the social and technical dynamics within formal and informal organizations. Faculty and students use both engineering and social scientific methods to explore how people’s motivations and behaviors co-evolve in organizations, including communities of practice in business, the automobile industry, science and engineering communities, disaster response, medical care, public health and “virtual worlds.” Central themes of our research program are the design and implementation of new technologies; global product development; management of engineering work; and formation, maintenance, and dissolution of dynamically linked social and knowledge networks. Students working in this area take courses in organizational theory, technology management, network analysis, innovation, organizational change, organizational communication, and leadership along with core IEMS courses. Students are encouraged to conduct research that is empirical in nature, theoretically grounded and has practical implications. Faculty members in this group include Noshir Contractor, Mark Daskin, Seyed Iravani, Paul Leonardi, Charles Thompson, and William White.


Production and logistics research in IEMS studies analytical and computational aspects of production and logistics systems. The main focus of our research program is in applying these concepts to emerging domains such as supply chain management, product development, and service operations such as health care, business intelligence, and risk mitigation. Students with specialization in this area gain skills in various fields that include optimization, stochastic modeling and simulation. Faculty in this group include Mark Daskin, Seyed Iravani, Diego Klabjan, and Karen Smilowitz.


Simulation research in IEMS derives new methods for the design, analysis and optimization of simulation experiments and applies these methods in production, logistics and financial engineering domains. Central themes of our research program are quantifying uncertainty, measuring and controlling risk and developing robust solution algorithms. Students working in simulation prepare by undertaking rigorous training in stochastic processes, statistics and optimization, as well as obtaining a solid grounding in the application domain of interest, often by working with an industrial sponsor. Recent Ph.D. graduates have derived robust simulation optimization algorithms; have invented methods to precisely estimate critical financial risk measures; and have provided tools to represent large-scale industrial simulations as easy-to-use metamodels. Faculty in this group include Bruce Ankenman, Tito Homem-de-Mello, Barry Nelson and Jeremy Staum.