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.

Berkeley IEOR Undergrad Wins Best Student Paper Award at KDIR 2020

IEOR Undergraduate Jonathan Bodine (Class of ’21) and Professor Dorit Hochbaum won the Best Student Paper Award at the 12th International Conference on Knowledge Discovery and Information Retrieval (KDIR 2020). The award winning work authored by Bodine and Hochbaum was titled The Max-Cut Decision Tree: Improving on the Accuracy and Running Time of Decision Trees.…

Berkeley IEOR researchers selected as Best Student Paper Award Finalists at IEEE Conference

The paper authored by Salar Fattahi, a recent IEOR PhD grad and incoming Assistant Professor at University of Michigan, Cedric Josz, a former IEOR/EECS postdoc and Assistant Professor at Columbia University, and Reza Mohammadi, an IEOR postdoc, along with Professors Javad Lavaei and Somayeh Sojoudi was selected as a Best Student Paper Award Finalist at…

Ken Goldberg investigates robot-assisted precision irrigation to help address global water demand

While the drought seems to flow away with recent California rains, the demand for water in agriculture remains a problem. However, California is not the only place with this issue: all over the world, agriculture consumes at least 70% of the world’s freshwater.  To help address this problem, the U.S. Department of Agriculture has provided University of…

IEOR research selected as finalist for 2019 American Control Conference

The paper titled “On the Exponential Number of Connected Components for the Feasible Set of Optimal Decentralized Control Problems” by IEOR PhD student Han Feng and associate professor Javad Lavaei has been selected to be a finalist at the 2019 American Control Conference (ACC). ACC accepts over 1000 papers for each conference and only five are selected…

Artificial intelligence could identify you and your health history from your step tracker

by Anil Aswani & Yoshimi Fukuoka (originally published in USA Today) Manufacturers say data stripped of identifying information is no privacy risk. But we found AI can overcome that. Time to update health privacy laws. Recent revelations about how social media giants misuse our personal data for profit have elevated the issue of privacy among Americans, but what…

New ambidextrous robot may redefine the warehouse

Research published in Science Robotics this week announced a new “ambidextrous” robot that could change the fundamentals of warehouse distribution. The robot, developed by researchers at the University of California, Berkeley’s Laboratory for Automation Science and Engineering features a suction cup gripper on one hand and a parallel-jaw gripper on the other, allowing the robot to choose the most…

Shmuel Oren awarded Berkeley Citation

IEOR professor of the graduate school Shmuel Oren has just been awarded the Berkeley Citation by Chancellor Carol Christ.  The Citation is one of the University of California, Berkeley’s highest awards, and is reserved for individuals “whose contributions go beyond the call of duty and whose achievements exceed the standards of excellence in their fields.”…

Fairness in machine learning paper wins INFORMS 2018 Data Mining & Decision Analytics award

IEOR PhD student Matt Olfat’s and assistant professor Anil Aswani’s paper “Spectral algorithms for computing fair support vector machines” won the INFORMS 2018 Data Mining & Decision Analytics Workshop best student paper award. Olfat presented the paper at the INFORMS Annual Meeting in Phoenix. The paper can be found here. Abstract: Classifiers and rating scores are prone…

Berkeley engineers win 2018 best paper in data mining

IEOR PhD student Salar Fattahi and EECS assistant professor in residence Somayeh Sojoudi have won the INFORMS 2018 Data Mining Best Paper Award. Fattahi and Sojoudi presented their paper “Graphical Lasso and Thresholding: Equivalence and Closed-form Solutions” at the INFORMS Annual Meeting in Phoenix. The paper can be found here. Abstract:  Graphical Lasso (GL) is a popular method for learning…

Max Z. Shen awarded as a 2018 INFORMS Fellow

IEOR Professor Max Shen has been named a 2018 INFORMS Fellow.  INFORMS Fellows Awards are announced once per year, and are reserved for researchers that exemplify outstanding lifetime achievement in operations research and management sciences or have demonstrated exceptional accomplishments or significant contributions to the advancement of operations research and/or management sciences over time. Prof. Shen…

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.