Machine Learning and Data Science
UC 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 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.…
Read MoreTransforming Decision-Making with AI: A Shift from Prediction to Optimization in Machine Learning
Amid the surging popularity of AI, companies, organizations, and institutions worldwide are eagerly embracing its transformative potential. Yet, a disparity exists, rooted in the steep costs and extended time frames required for training machine learning models. This challenge is particularly acute in supervised learning—a popular machine learning approach where algorithms learn from labeled datasets. Here,…
Read MoreAI4OPT In Its Third Year
AI4OPT In Its Third year The Artificial Intelligence Institute for Advances in Optimization is delivering a paradigm shift in automated decision-making September marked the third anniversary of The AI Institute for Advances in Optimization (AI4OPT). Established in 2021 with a $20 million grant from the National Science Foundation, this visionary institute is a collaborative effort,…
Read MoreBerkeley IEOR Professor Ying Cui Receives NIH Award for Advancing Biomedical Data Learning
Berkeley IEOR professor Ying Cui was recently awarded an R01 grant from the National Institutes of Health (NIH)’s National Cancer Institute, titled “SCH: A New Computational Framework for Learning from Imbalanced Biomedical Data,” for her work aimed at developing new machine learning tools to address the challenge of learning from imbalanced biomedical data, setting the…
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