Pradeep Ravikumar

Pradeep Ravikumar is a Professor in the Machine Learning Department at Carnegie Mellon University's School of Computer Science. He leads the Statistical & Symbolic Learning (Neuro-Symbolic AI) Group and serves as co-Editor-in-Chief of the Journal of Machine Learning Research (JMLR). Previously, he was Associate Editor-in-Chief for IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI). His research focuses on machine learning theory, probabilistic graphical models, neuro-symbolic AI, and causal representation learning.

He has taught advanced courses such as Advanced Machine Learning (PhD level), Probabilistic Graphical Models, and Convex Optimization. His awards include the Sloan Research Fellowship, NSF CAREER Award, and Siebel Scholarship. His work emphasizes theoretical foundations and practical applications, with recent contributions in neuro-symbolic systems, causal inference, and robust machine learning.

Key research themes include differentiable structure learning, interpretable AI, and invariant representation learning. He has published extensively in top venues like NeurIPS, ICML, and JMLR, addressing challenges in optimization, causal discovery, and model interpretability.