We are incredibly excited to welcome four new faculty members to our community. The Machine Learning Department at Carnegie Mellon University continues to expand each year, being the home for over 30 faculty members across Carnegie Mellon.
Carnegie Mellon University is currently ranked as the number one educational research institution in the areas of machine learning, artificial intelligence, data mining, NLP, computer vision along with information retrieval, according to CSRankings, our department’s continuous expansion solidifies our focus on research as the fields of machine learning and artificial intelligence continue to evolve.
Distinguished Career Professor
Ghani’s research interests lie at the intersection of machine learning, public policy, and social sciences. He is interested in solving large-scale and high impact social problems using data-driven and evidence-based methods.
Ghani was previously the Director of the Center for Data Science and Public Policy, Research Associate Professor in the Department of Computer Science, and a Senior Fellow at the Harris School of Public Policy at the University of Chicago.
Li’s work primarily focuses on machine learning. His goal is to design efficient and provable algorithms for practical machine learning problems. He is also very interested in convex/non-convex optimization.
Li was previously a postdoc in the Computer Science Department at Stanford University. He earned his Ph.D. in Computer Science from Princeton University.
Risteski’s work lies in the intersection of machine learning and theoretical computer science. The broad goal of his research is theoretically understanding statistical and algorithmic phenomena and problems arising in modern machine learning.
Risteski was previously a Norbert Wiener Fellow and Applied Mathematics Instructor at MIT. He received his Ph.D. at Princeton University with a focus on machine learning and theoretical computer science.
Smith’s interests lie at the intersection of machine learning, optimization, and computer systems. A unifying theme of her research is to develop machine learning methods and theory that effectively leverage prior knowledge and account for practical constraints (e.g., hardware capabilities, network capacity, statistical structure). Specific topics include distributed optimization, large-scale machine learning, resource-constrained learning, multi-task learning, transfer learning, and data augmentation.
Smith was previously an Assistant Professor with the Electrical and Computer Engineering Department at Carnegie Mellon University. She earned her Ph.D. in Computer Science at the University of California — Berkeley.
Assistant Professor joint with the Computer Science Department at CMU
Chen’s interests lie in the intersection of machine learning and systems. To Tianqi, the real excitement of this area comes from what it can be enabled when bringing advanced learning techniques and systems together. On that end, he is also pushing the direction on deep learning, knowledge transfer, and lifelong learning.