Maria Balcan

Maria Balcan is the Cadence Design Systems Professor in the Computer Science Department at Carnegie Mellon University. Her research focuses on Artificial Intelligence and Theory, with particular emphasis on Algorithms and Complexity, Machine Learning, and Algorithmic Game Theory. Her work explores foundational questions in machine learning, including interactive learning, distributed learning, and multi-task learning, as well as the application of machine learning to algorithmic game theory and the analysis of algorithms beyond worst-case scenarios. She is also involved in the development of new frameworks and algorithms for modern learning paradigms, with a focus on statistical efficiency, computational efficiency, and noise tolerance.

Maria-Florina Balcan is a Professor in the School of Computer Science at Carnegie Mellon University, specializing in Machine Learning, Algorithmic Game Theory, and Theoretical Computer Science. Her research focuses on understanding the theoretical foundations of learning algorithms, particularly in active learning, meta-learning, and distributed learning. She explores how to improve the efficiency and robustness of machine learning models under noisy or adversarial conditions. Her work also addresses the interplay between learning and game-theoretic models, such as in online learning and security games. Balcan's contributions span both algorithmic and theoretical aspects of machine learning, with an emphasis on scalability, privacy, and communication complexity.