Mladen Kolar, who earned his Ph.D. in Carnegie Mellon University's Machine Learning Department in 2013, received the 2024 Leo Breiman Junior Award from the Statistical Learning and Data Science Section of the American Statistical Association (ASA).

Presented every two years, the Breiman Award recognizes outstanding theoretical or methodological contributions to machine learning and/or computational statistics — contributions that have made a substantial, sustained impact on the subject and its practical applications. It honors Leo Breiman, a professor at the University of California, Berkeley, whose work helped bridge the gap between statistics and computer science, particularly in the field of machine learning.

Now a professor in the Department of Data Sciences and Operations at the University of Southern California's Marshall School of Business, Kolar earned his Ph.D. at CMU under adviser Eric Xing. His research focuses on high-dimensional statistical methods, probabilistic graphical models and scalable optimization methods, driven by the need to uncover interesting and scientifically meaningful structures from observational data. He serves as an associate editor for the Journal of Machine Learning Research, the Annals of Statistics, the Journal of Computational and Graphical Statistics, and the New England Journal of Statistics in Data Science.

For more on the Breiman Award, visit the ASA's Statistical Learning and Data Science Section website.