Murat Kocaoglu is an assistant professor in the Johns Hopkins University Department of Computer Science, where he leads the CausalML Lab. He is also a member of the Data Science and AI Institute.
Kocaoglu is interested in developing new theoretical results that provide insights about fundamental causal discovery and inference problems, and developing novel algorithms based on these insights with a wide range of applications, from computer security and machine learning to generative AI. His current research interests include causal inference and discovery, generative models, causal methods for computer security, online algorithms, and information theory.
He received an Adobe Data Science Research Award in 2022, an NSF CAREER Award in 2023, and an Amazon Research Award in 2024. Additionally, he serves as an area chair for the Conference on Neural Information Processing Systems, the International Conference on Machine Learning, the International Conference on Learning Representations, the AAAI Conference on Artificial Intelligence, the International Conference on Artificial Intelligence and Statistics, and the Conference on Uncertainty in Artificial Intelligence, and as a reviewer for several journals.
Kocaoglu received a BS in electrical and electronics engineering with a minor in physics from the Middle East Technical University in Turkey in 2010, an MS from Koç University in Istanbul in 2012, and a PhD from the University of Texas at Austin in 2018. Prior to joining Johns Hopkins, he was an assistant professor in the Elmore Family School of Electrical and Computer Engineering at Purdue University and was a research staff member at the MIT-IBM Watson AI Lab in Cambridge, Massachusetts from 2018 to 2020.