Yoav Wald

Yoav Wald is an incoming Faculty Fellow at New York University's Center for Data Science (CDS), joining in Fall 2023. Previously, he served as a Postdoctoral Fellow at Johns Hopkins' Whiting School of Engineering, where he focused on developing safe and robust machine learning systems with healthcare applications. He completed his PhD at the Hebrew University of Jerusalem under the supervision of Amir Globerson and was also a researcher at Google Research, working on structured prediction and neural network explainability in computer vision problems.

Wald holds a BSc in Physics and Computer Science along with an MSc in Computer Science. His research interests center on improving the reliability of machine learning systems, particularly in healthcare contexts. He specializes in out-of-distribution detection, model calibration, and addressing spurious correlations in AI systems. Wald is deeply committed to making machine learning more transparent and trustworthy for real-world applications.

His recent publications in top-tier conferences like UAI, ICCV, and NeurIPS demonstrate his contributions to understanding model robustness and generalization. Wald is the lead organizer of the Spurious Correlations, Invariance and Stability (SCIS) Workshop at the International Conference on Machine Learning (ICML), which he previously co-organized in 2022.

Among his honors, Wald received excellence awards for teaching during his time at the Hebrew University of Jerusalem. He has established himself as a promising researcher in the machine learning community with a focus on foundational issues that impact real-world deployment of AI systems.

Wald has collaborated with industry researchers at Google and is building connections across academia through his workshop organization and conference participation. His move to NYU CDS represents a significant step in his academic career, where he plans to advance research on safe machine learning in healthcare within CDS's diverse and dynamic environment.