Publications
The AI Supply Chain Whisperer
When Anthony Noguera arrived at UC Berkeley in the 1990s, he could not have foreseen that his graduate studies in industrial engineering and operations research would one day take him all over the world and allow him to redefine how global supply chains operate. After more than two decades at NVIDIA, Anthony is harnessing the…
Read MoreUC Berkeley IEOR Hosts Inaugural Tesla Case Competition on Global Sourcing Strategy
UC Berkeley IEOR Hosts Inaugural Tesla Case Competition on Global Sourcing Strategy The Department of Industrial Engineering and Operations Research (IEOR) at the University of California, Berkeley, hosted its inaugural Tesla Case Competition last month, bringing together more than 40 student teams to tackle one of the most pressing challenges in global operations today: managing…
Read MoreUC Berkeley IEOR-led Team Takes Second Place for Predictive Model Aiming to Improve Rail Safety
UC Berkeley IEOR PhD student Alberto Gennaro and his team are applying data-driven modeling to a problem that directly affects the safety and reliability of railroads: predicting when train wheels are likely to fail. Their project earned second place in the 2025 INFORMS Railway Applications Section Problem Solving Competition, which invites researchers and practioners to…
Read MoreUC Berkeley IEOR professor and alum earn international prize for breakthroughs in optimization
UC Berkeley Professor Alper Atamturk and UC Berkeley alum Andrés Gómez have been awarded the 2025 INFORMS Computing Society Prize, an honor recognizing outstanding contributions at the intersection of computing with operations research. They share the award with their collaborator Shaoning Han. The team is recognized for a sequence of five papers introducing new mathematical…
Read MoreUC Berkeley Researchers Win Top Honors at World’s Leading Robotics Conference
A team of researchers from UC Berkeley has received the Best Paper Award on Robot Learning at the 2025 IEEE International Conference on Robotics and Automation (ICRA), considered the world’s premier conference in robotics and automation. The event was held in Atlanta 19-23 May. The award went to UC Berkeley IEOR Professor Ken Goldberg, his…
Read MoreUC Berkeley Researchers Offer a Smarter Way to Price With Stability and Impact in Mind
With inflation, tariffs, and shifting consumer habits, setting the right price has never been more complicated or more crucial. But new research from UC Berkeley’s Department of Industrial Engineering and Operations Research suggests that simplicity might be the key to navigating today’s complex markets. In a recent study, Associate Professor Javad Lavaei, Professor Emeritus Max…
Read MoreZeyu Zheng Receives Peter D. Welch Early Career Award
Zeyu Zheng has been honored with the inaugural Peter D. Welch Early Career Award by the INFORMS Simulation Society (I-SIM). This award recognizes early-career researchers for their exceptional contributions to the field of simulation. Zheng received the award at the December 2024 Winter Simulation Conference, where he and his co-authors also won the Best Theoretical…
Read MoreAncient Wisdom: Exploring the Intersection of AI, Art, and Nature
The cover of the winter 2025 edition of Berkeley IEOR Magazine showcases artwork from Ancient Wisdom: Trees, Time, and Technology, an exhibition by Ken Goldberg and Tiffany Shlain currently on view at the Skirball Museum in Los Angeles through March 2, 2025. Part of the Getty Museum’s city-wide Pacific Standard Time quadrennial, the exhibit examines…
Read MoreBerkeley IEOR PhD students Win Best Theoretical Paper Award
Berkeley IEOR PhD students Haoting Zhang, Jinghai He, and recent alum Jingxu Xu (PhD ’24) were honored with the Best Theoretical Paper Award at the 2024 Winter Simulation Conference held in Orlando, Florida. Their award-winning paper, Enhancing Language Models with Both Human and Artificial Intelligence Feedback Data, explores innovative methods to improve AI performance through…
Read MoreTraining Smarter AI: New Research Integrates Human and Machine Feedback
AI models rely heavily on feedback to refine their outputs and meet user needs. While human feedback is highly effective, it is often expensive, time-intensive, and limited by data privacy constraints. For example, training an AI system to detect early signs of cancer in medical scans relies on radiologists to verify predictions and provide annotations.…
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