Andrew Maas

In Fall 2009, I will begin the Computer Science PhD program at Stanford University. I will be supported by a National Science Foundation graduate research fellowship. In May 2009 I completed my Bachelors in Computer Science and Cognitive Science at CMU.

Email: amaas [a t} stanford dot edu

Research Interests

I work at the intersection of machine learning, robotics, and cognitive science. Human perception and learning are remarkable when we consider the complex data entering our senses. Developing algorithms for perception and higher-level learning will enable autonomous agents to better integrate into our homes and lives.

Demos

Demo of a Personalized Navigation Device which Predicts User Behavior Mapprentice Project. 2009.

Dynamically Adjusting Suggested Route as Hazards Change. Mapprentice Project. 2008.

Destination Prediction Route so far shown in black, log probability of destination shown in varying red intensities. Mapprentice Project. 2008.

Predicting Route During Travel Destination is known. Mapprentice Project. 2008.

Publications

A. L. Maas & C. Kemp. (To Appear). One-Shot Learning with Bayesian Networks. Proceedings of The 31st Annual Meeting of The Cognitive Science Society.
Passenger data set used in the paper

B. D. Ziebart, A. Maas, A. K. Dey, & J. A. Bagnell. (2009). Human Behavior Modeling with Maximum Entropy Inverse Optimal Control. AAAI Spring Symposium on Human Behavior Modeling.

B. D. Ziebart, A. Maas, A. K. Dey, & J. A. Bagnell. (2008). Navigate Like a Cabbie: Probabilistic Reasoning from Observed Context-Aware Behavior. Proceedings of the 10th International Conference on Ubiquitous Computing.

B. D. Ziebart, A. Maas, J. A. Bagnell, & A. K. Dey. (2008). Maximum Entropy Inverse Reinforcement Learning. Proceedings of the 23rd AAAI Conference on Artificial Intelligence.