IEOR researchers investigate the latest mathematical tools, approaches, and methodologies to make new theoretical discoveries and innovations that touch nearly every industry, making them more efficient and profitable in areas such as supply chain, logistics, manufacturing, data science, energy system, energy systems, robotics, and management.
Supply Chain & Logistics
Research in supply chain management explores the effective and efficient flow of goods and services in supply chains. Supply chain management is one of the fastest growing and most influential areas within industrial engineering and operations research, and UC Berkeley IEOR faculty members are some of the world's leading supply chain management experts. Researchers in the department are actively exploring a variety of approaches to integrating and optimizing various operational, tactical and strategic decisions in large-scale supply chains, and are developing techniques to help managers deal with the uncertainty inherent in the real world.
Financial Systems
Financial engineering concerns the application of analytical, statistical, and computational methods to solve problems in financial economics. It is a multidisciplinary field that draws on tools from applied mathematics, computer science, statistics, and economic theory. Faculty at UC Berkeley IEOR conduct various research projects in credit risk, real options, high-frequency trading, and portfolio management. These research activities have been broadly supported by the National Science Foundation, National Security Agency, and various industry partners including Bloomberg and the NASDAQ OMX educational group. The research team attracts the best quality and highly motivated students, who go through rigorous and deep analytical training in mathematics and statistics, and develop proficiency in hand-on skills such as programming. Over the last decades, they have been aggressively recruited by the top investment banks around the globe as well as high-tech firms including Google and Facebook.
Energy Systems
Energy Research in the IEOR Department focuses on modeling, analysis and optimization of energy systems and in particular power systems. The program which is directed by Professor Shmuel Oren is focused on graduate education at the Ph.D. level and it currently involves several faculty members (Professors, Oren, Shen, Atamturk and Lavaei), six to eight Ph.D students, and one to two post-doctoral researchers. On average it is turning out about one to two Ph.D. graduates each year. The program is affiliated with PSERC (power systems engineering research center) of which Dr. Oren is a co-founder and site director, with CERTS (center for electric reliability technology solutions). Research activities have focused on a variety of topics including: power systems economics, electricity market design, energy and environmental regulation, demand response, renewables integration, energy risk management and the development of computational tools for planning, operation and analysis of electric power systems. The research has been funded by PSERC, DOE, ARPA-E, NSF, FERC, CERTS, EPRI, LLNL and the SIEMENS Co. The program boasts a long list of distinguished alums that currently hold academic faculty positions in power systems, economics, and operations research and executive positions in energy related industries including electric utilities, energy trading and consulting companies.
Healthcare Systems
Faculty at UC Berkeley IEOR are involved with research impacting different levels of the healthcare system. Current projects include research at the treatment scale (radiation treatment planning and medical robotics), at the individual scale (personalized chronic disease management and addressing food insecurity), at the infrastructure scale (operation room scheduling), and at the national policy scale.
Optimization and Algorithms
Optimization is in the center of every engineering discipline and every sector of the economy. Airlines and logistics companies run optimization algorithms to schedule their daily operations; power utilities rely on optimization to efficiently operate generators and renewable resources and distribute electricity; biotechnology firms search through massive genetic data using optimization to find new discoveries. UC Berkeley IEOR Department is at the forefront of optimization research. Our faculty and their students create new fields of optimization and push the boundaries in convex and nonconvex optimization, integer and combinatorial optimization to solve problems with massive data sets.
Stochastic Modeling and Simulation
Risk and uncertainty is inherent in all real-world systems, and understanding its impact is essential in performance analysis and optimization. Researchers in the IEOR Department at UC Berkeley are developing stochastic models and simulations for applications ranging from call centers to cloud computing, as well as expanding fundamental theory in areas such as stochastic control, semi-Martingale and filtration expansions, the economics of queueing systems, and design of simulation experiments.
Robotics and Automation
Robotics and automation are advancing rapidly due to innovations in sensors, devices, UAVs, networks, optimization, and machine learning, accelerated by corporate and private investment. These systems have enormous potential to reduce drudgery and improve human experience in healthcare, manufacturing, transportation, safety, and a broad range of other applications, building on emerging advances in cloud computing, ensemble learning, big data, open-source software, and industry initiatives in the "Internet of Things", "Smarter Planet", "Industrial Internet" and "Industry 4.0." Recent developments in sequential non-convex optimization, model predictive control, partially observable Markov decision processes, reinforcement learning, and approximate probabilistic inference hold promise for addressing these problems at scale. Cloud Computing can provide access to large datasets and clusters of remote processors to filter, model, optimize, and share data across systems to improve performance over time.