I am a professor, the inaugural director of the USC-Amazon Center for Secure and Trusted Machine Learning (Trusted AI), and the director of the Information Theory and Machine Learning (vITAL) research lab at the Electrical and Computer Engineering Department and Computer Science Department of University of Southern California. I am also the co-founder and CEO of FedML. I received my Ph.D. in Electrical Engineering and Computer Sciences from UC Berkeley in 2008. I do research in the areas of information theory, decentralized and federated machine learning, secure and privacy-preserving learning and computing.
As the inaugural director of the Center for Secure and Trusted Machine Learning, or Trusted AI, I am excited about our collaboration with Amazon at the USC Viterbi School of Engineering. This partnership is dedicated to revolutionizing machine learning by focusing on the development of robust, privacy-preserving solutions. By leveraging USC's academic expertise and Amazon's technological capabilities, we aim to address critical challenges in ML privacy, security, and trustworthiness, ensuring that our advancements lead to secure, practical applications in real-world scenarios.
FedML is a comprehensive open-source framework I helped create to facilitate the development, deployment, and management of federated learning (FL) applications. By enabling collaboration across multiple devices and data sources without compromising privacy, we address the challenges of distributed machine learning. Our platform supports a variety of FL paradigms, including horizontal, vertical, and federated transfer learning, making it versatile for diverse applications. As a co-founder and the CEO of FedML, I am dedicated to empowering researchers and developers to efficiently scale FL models, ensure data privacy, and optimize performance through decentralized computing.
