Kumar Kshitij Patel
Kumar Kshitij Patel joined Yale as a Postdoctoral Associate in the Summer of 2025. Kshitij received his PhD in Computer Science from the Toyota Technological Institute at Chicago, where he was advised by Prof. Nathan Srebro and Lingxiao Wang. Before that, he obtained his B.Tech. in Computer Science and Engineering from the Indian Institute of Technology (IIT) Kanpur. His research focuses on federated learning, optimization, and privacy, with a broader interest in how machine learning systems can efficiently and reliably operate across multiple tasks and data distributions under constrained access. His work combines theoretical frameworks, such as min-max complexity, with algorithm design to model and address key challenges arising from communication costs, data heterogeneity, fairness issues, and distribution shifts. He is particularly interested in domains such as healthcare, where both the need to integrate multiple datasets and the imperative to preserve privacy arise as central challenges. His research has been recognized with the Distinguished Paper Award at IJCAI 2024 and a Best Paper Honorable Mention at the Federated Learning Workshop at ICML 2023. As an FDS Postdoc, he is interested in the economics of data, specifically in how to assign value, design incentive mechanisms and markets, and protect privacy, fairness, and ownership as the demand for high-quality datasets increases. His goal is to help develop collaborative learning systems that are socially beneficial and practical, ensuring fair compensation for data owners, enabling trustworthy cooperation, and upholding data rights while informing legislation that governs data usage and enforces privacy protections.