Carlos Guestrin

www |
GHC 8002
(412) 268-3075
(412) 268-2205
Mailing Address: Machine Learning Department
Computer Science Department
Carnegie Mellon University
5000 Forbes Avenue
Pittsburgh, PA 15213-3891
Assistant: Monica Hopes |
(412) 268-5527

Assistant Professor, Machine Learning Dept. and SCS
Distributed Sensor Networks

Research Interests:

My 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 my 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.

Wireless sensor networks are a central application domain for my research efforts. These networks are usually composed of small, low-cost devices that communicate wirelessly to achieve global sensing and decision-making tasks. Current real-world deployments range from scientific data collection and analysis applications (e.g., monitoring of bird habitats in the Great Duck Island), through environmental monitoring and intervention (e.g., precision agriculture in vineyards), to large-scale fault diagnosis and prevention (e.g., diagnosis from vibration data at Intel factories).

To tackle such real-world systems, one must link theoretically-founded algorithms and techniques from computer science, artificial intelligence, statistics, optimization theory and operations research, to knowledge and structure specific to the problem at hand. This link between theoretically well-posed algorithms and problem-specific structure allows us to scale up methods to tackle complex, large-scale systems. Such scaling up of algorithms to real-world problems is the central long-term goal of my research efforts.

Sensor networks offer an additional set of challenges: Due to cost and space constraints, typical nodes in sensor networks have very limited processing power. Additionally, wireless networks are usually very lossy, suffering from undetectable packet losses, packet collisions and other forms of interference. Finally, battery power is a very strong constraint in these networks. At full duty cycle, the batteries of typical nodes last no more than a couple of days. Successful long-term deployments thus require effective power management.

My 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.





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