Mladen Kolar is Associate Professor of Econometrics and Statistics at the University of Chicago Booth School of Business. His research is focused on high-dimensional statistical methods, graphical models, varying-coefficient models and data mining, driven by the need to uncover interesting and scientifically meaningful structures from observational data. Particular applications arise in studies of dynamic regulatory networks and social media analysis. His research has appeared in several publications including the Journal of Machine Learning Research, Annals of Statistics, Annals of Applied Statistics, and the Electronic Journal of Statistics. He also regularly presents his research at the top machine learning conferences, including Advances in Neural Information Processing Systems and the International Conference of Machine Learning.
Kolar was awarded a prestigious Facebook Fellowship in 2010 for his work on machine learning and network models. He spent a summer with Facebook’s ads optimization team working on a large scale system for click-through rate prediction. His other past research included work with INRIA Rocquencourt in Paris, France and Joint Research Center in Ispra, Italy.
Kolar earned his PhD in Machine Learning in 2013 from Carnegie Mellon University, as well as a diploma in Computer Engineering from the University of Zagreb. For his Ph.D. thesis work on “Uncovering Structure in High-Dimensions: Networks and Multi-task Learning Problems,” Kolar received from 2014 SIGKDD Dissertation Award honorable mention.
Outside of academia, Kolar enjoys chess, badminton, running, cycling and hiking.
2021 - 2022 Course Schedule
|41204||Machine Learning||2022 (Winter)|