Category: Optimization
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Engaging Detroit’s next generation of engineers
With help from an Engage Detroit grant, U-M IOE and Robotics empower future engineers through hands-on learning and community building.
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U-M IOE faculty and students awarded at IISE Annual Conference and Expo
IOE faculty and students awarded for industrial and systems engineering research and scholarship.
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Michigan IOE welcomes two new faculty
Dr. Sushil Varma joins from INRIA Paris, where he designed algorithms for online marketplaces and EV-based transportation. Dr. Manhua Wang joins from Virginia Tech Transportation Institute, focusing on trustworthy, adaptive human-AI collaborations in intelligent transportation and automated vehicles.
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IOE graduation speakers reflect on their Michigan Engineering journeys
Each spring, the University of Michigan (U-M) College of Engineering selects two student speakers for the undergraduate and graduate commencements. This year both speakers are from the Department of Industrial and Operations Engineering (IOE). Undergraduate student Bella Wash and Ph.D. student Dr. Leena Ghrayeb will share their unique paths and their commitment to human-centered engineering.
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Berahas named member of the Air Force Office of Scientific Research 2025 Young Investigator Program
Albert S. Berahas will use this opportunity to develop, analyze and implement algorithms for solving noisy constrained derivative-free optimization problems, prevalent in fields such as engineering design, medicine and machine learning.
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Albert S. Berahas wins Charles Broyden Prize
U-M IOE assistant professor recognized for advancements in machine learning optimization
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U-M IOE community excels at 2024 INFORMS Annual Meeting
The University of Michigan Industrial and Operations Engineering Department walked away with numerous awards, including an INFORMS Fellow Award for Amy Cohn and Magna Cum Laude status for the U-M INFORMS student chapter.
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Berahas Receives ONR Grant to Develop Advanced Optimization Algorithms
The grant will fund the development of cutting-edge algorithms for solving complex nonlinear constrained stochastic optimization problems. Albert Berahas, and Micheal O’Neill will lead the research to advance optimization methods and address real-world challenges.
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Viswanath Nagarajan Awarded NSF Grant
The awarded project focuses on developing advanced algorithms for stochastic optimization, tackling a fundamental challenge in algorithm design: uncertainty in input parameters.
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University of Michigan Hosts Optimaize Day to Spark Interest in Engineering Among Detroit High School Students
In partnership with the Detroit Educational Takeover Team, Optimaize Day bridges theoretical understanding of engineering with everyday applications, leaving a lasting impression on the minds of potential future engineers.
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New statistical tool to distinguish shared and unique features in data from different sources
Personalized PCA method overcomes the challenges of heterogeneous data analysis
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Salar Fattahi receives NSF CAREER Award for work in nonconvex problems in machine learning
Salar Fattahi discusses his award-winning CAREER proposal which aims to close the current gap between optimization and statistical learning.
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Standardizing bilevel optimization for future researchers and practitioners
Bilevel optimization is used not only in research, but also in business to identify the best solutions for a variety of hierarchical problems. A U-M researcher aims to develop a comprehensive software package that will enable many industries to use this in everyday decision-making.
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U-M IOE takes home awards from the INFORMS Annual Meeting
IOE faculty and students bring in awards from the 2023 Institute for Operations Research and the Management Sciences Annual Meeting.
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U-M Industrial and Operations Engineering wins several awards at the 2022 INFORMS Annual Meeting
The Institute for Operations Research and the Management Sciences (INFORMS) annual meeting has officially wrapped up with the University of Michigan Industrial and Operations Engineering (U-M IOE) Department walking away with several awards.
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Helping people get back to work using deep learning in the occupational health system
University of Michigan researchers have produced a new prediction model using longitudinal information and deep learning to better predict the return to work time for people with occupational injuries.