Posts Tagged ‘Paul Grigas’
Transforming 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 MoreProf. Paul Grigas Wins INFORMS Junior Faculty Interest Group Paper Competition
Berkeley IEOR Professor Paul Grigas has been selected as winner of the 2020 INFORMS Junior Faculty Interest Group (JFIG) Paper Competition for his work titled Smart “Predict, then Optimize”, co-authored along with colleague Adam Elmachtoub from Columbia University. Grigas and Elmachtoub were selected for proposing a new prediction and optimization framework, called Smart “Predict, then…
Read MoreGrigas to Investigate New Framework For Operations-Driven Machine Learning
IEOR professor Paul Grigas has just been awarded $290,060 by the National Science Foundation to improve operational decision-making by leveraging data and machine learning. Grigas will collaborate with Adam Elamchtoub from Columbia University to advance a new statistical learning framework called Smart “Predict, then Optimize” (SPO) which aims to improve optimization and prediction for better decisions in sectors such as transportation,…
Read MoreIEOR Welcomes Paul Grigas as new Assistant Professor
The Department of Industrial Engineering and Operations Research at UC Berkeley is proud to welcome Paul Grigas as a new Assistant Professor this fall. Paul’s research interests include large-scale convex optimization, statistical machine learning, and data-driven decision making. He is also interested in applications in online advertising and data analytics, among other areas. Paul was recently awarded…
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