Robotics Institute; Machine Learning Department
Carnegie Mellon University
Carlos Guestrin is Assistant Faculty in the Robotics Institute and Adjunct Faculty in the Machine Learning Department at Carnegie Mellon. His main long-term research interest is in developing efficient algorithms and methods for designing, analyzing and controlling complex real-world systems. A common thread in his research has been the focus on large-scale stochastic dynamical systems, where the state of the system evolves over time and uncertainty is prevalent. Such systems exist in many diverse application areas: from economics, through computer science and engineering, to computational biology. His long-term research goals are to develop efficient distributed algorithms for effective inference, learning and control in large-scale real-world distributed systems, such as sensor networks. These algorithms must perform the global inference and optimization tasks required by sensor network applications, while being robust to network losses and failures, and limiting communication and power requirements. In addition to developing theoretically-founded algorithms, we seek to evaluate these methods on data from real sensor network deployments, and to implement some of these approaches on real deployed systems.