Mladen Kolar’s 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 Applied Statistics, and 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 previous research included work with INRIA Rocquencourt in Paris, France, and the Joint Research Centre in Ispra, Italy.
Kolar earned his PhD in machine learning from Carnegie Mellon University, as well as a diploma in computer engineering from the University of Zagreb.
Kolar joined the University of Chicago faculty in 2013.