Rajesh Ranganath

Rajesh Ranganath is an Associate Professor in the Department of Computer Science at New York University. His research focuses on machine learning, particularly in areas such as causal inference, probabilistic modeling, and healthcare applications. He is affiliated with the Courant Institute and the Center for Data Science. Ranganath has contributed to various research projects and publications, including work on deep generative models and out-of-distribution generalization. His academic career includes a Ph.D. in Computer Science from Princeton University, where he worked with David Blei. He has also collaborated with institutions such as MIT’s Institute for Medical Engineering and Science.

Rajesh Ranganath is an Assistant Professor at NYU, specializing in Machine Learning, Statistics, and Medical Informatics. His research focuses on developing advanced machine learning techniques for health applications, including clinical note analysis and hospital readmission prediction. He explores probabilistic modeling, variational inference, and deep learning for medical data. His work addresses challenges in reproducibility, interpretability, and robustness in health-related machine learning. Ranganath's publications cover topics such as hierarchical variational models, deep survival analysis, and adversarial vulnerabilities in electrocardiogram models. His research bridges statistical theory with practical applications in clinical research.