Faculty

Computer Science Department

Associate Professor

I'm interested in multi-agent planning, reinforcement learning, decision-theoretic planning, statistical models of difficult data (e.g. maps, video, text), computational learning theory, and game theory.

Carlos Guestrin

Computer Science Department

Assistant Professor

My research interests are in statistical machine learning, sensor networks, and designing algorithms that learn and adapt to changing environments.

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Graduate Students

Andreas Krause

Computer Science Department

Advisor: Carlos Guestrin

My research interests include machine learning algorithms and probabilistic reasoning both in theory and in complex real world systems. I am currently investigating optimization and approximation of value of information problems in graphical models, which frequently arise in large distributed systems such as sensor networks.

Stanislav Funiak

Robotics Institute

Advisor: Carlos Guestrin

I am interested in formalisms and algorithms for effectively addressing large-scale problems in Bayesian inference and machine learning. My research interests include graphical models, convex optimization, distributed algorithms, and their application to computer vision.

Jonathan Huang

Robotics Institute

Advisor: Carlos Guestrin

I work on probabilistic models that can be used for inference in multi-camera networks. More broadly, my interests lie somewhere between the fields of machine learning and computer vision.

Gaurav Veda

Computer Science Department

Advisor: Carlos Guestrin

My research interests include machine learning and applied algorithms.

Alexandra Meliou

Computer Science Department

Advisor: Carlos Guestrin

My research interests are in the area of database systems and my advisor is Joe Hellerstein. I am also co-advised by professor Carlos Guestrin at CMU I am currently working on query optimization in sensor networks, and I am specifically interested in power-efficient routing.

Sajid Siddiqi

Robotics Institute

Advisor: Geoff Gordon

I work on efficient models and algorithms for machine learning on temporal data, and on applying these methods to problems in activity monitoring, mobile robotics and other domains.

Chris Murray