Computational, Data-Centric and Technological Systems
Develops and assesses computational and data-centric approaches that enable knowledge creation, scientific discovery, and improved engineering decision-making. We also develop improvements to the techniques used for developing the complex technological systems upon which society depends, and to improve the way that such systems interact with humans.
Healthcare Policy and Operations
Investigates novel applications of industrial and systems engineering concepts and methods in health policy research, healthcare operations improvement, health services delivery, and medical decision making. We develop sophisticated simulation models, information technological systems, data-driven predictive analytics, complex-system performance improvement techniques, and healthcare operations and decision analysis research to generate knowledge and techniques to inform health policy, improve health systems and support personalized medical decision-making.
Manufacturing and Design
Creates new knowledge, novel processes, and enabling tools for current and future design and manufacturing practices, such as additive manufacturing and artificial intelligence and machine learning enabled systems. Our fundamental and applied research improves competitiveness in the development and realization of new products through new processes and data-analytics for smart manufacturing. The Center for Advanced Manufacturing (CAM) of the Viterbi School of Engineering supports the group’s work.
Operations, Supply Chains and Transportation
Uses advanced mathematical models and algorithms to solve emerging problems in supply chain management, logistics, and transportation. Faculty hold leadership positions in the METRANS Transportation Center, a joint center with the Price School of Public Policy, whose mission is to solve transportation problems of large metropolitan regions through interdisciplinary research, education, and outreach.
Optimization and Systems Modeling
Develops methods for smart planning and effective decision-making. Our research encompasses all aspects of optimization starting from systems modeling and covering mathematical analysis, design of solution methods, computational and sensitivity studies, software development, and contemporary problem-solving. We advance science, enable engineering, benefit society, and enrich lives through analytics, financial engineering, logistics, machine learning, renewable energy, risk management, signal processing, transportation science, and other applications.
Risk, Economic and Decision Analysis
Studies complex societal problems with uncertain outcomes, affecting lives, the environment and the economy. We use risk analysis, economic consequence analysis and decision analysis to evaluate and find solutions to these problems, which include terrorism, natural disasters, pandemics, and climate change. The Center for Risk and Economic Analysis of Threats and Emergencies (CREATE), founded in the department, supports the group’s work.
Statistics and Machine Learning
Focuses on advancing machine learning techniques to harness the massive amounts of data generated by contemporary systems, in order to solve important technological and societal challenges effectively and responsibly. Faculty and students engage in interdisciplinary research, combining ideas from probability, statistics and optimization, and develop cutting-edge methods. Topics include reinforcement learning, knowledge graphs, interpretable and fair Artificial Intelligence, reproducibility, algorithms for large-scale problems, and control-theoretic machine learning, as well as the applications of Machine Learning and statistics to manufacturing, healthcare and operational problems.
Stochastic Systems and Simulation
Formulates and analyzes stochastic models of real-life phenomena. When exact results cannot be analytically determined, we consider bounds, approximations, and asymptotic results. In these latter cases, a simulation is often needed for evaluation purposes. Because simulation can be quite time consuming, the group is also interested in determining effective simulation techniques that reduce the computation time either by speeding up a simulation or by using estimators with small variances.
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