My research interests as I thought of them before I started the PhD program are captured here. The following are my high-level areas of interest:

• Reinforcement learning and Bayesian methods
• Multi-agent learning, with emphasis on AI in games and multi-robot systems
• Mechanism design and game theory
• Applications, such as Smart Grid control systems and human-robot interaction

Our research group tackles the scientific and engineering challenges of creating teams of intelligent agents in complex, dynamic, and uncertain environments, in particular adversarial environments. The use of AI for societal benefit is also a key topic of interest.

I currently focus on research in computational sustainability, especially as it relates to the Smart Grid. Planning and operating a large and complex digital ecosystem like the Smart Grid requires advances in control systems, support for dynamic pricing, computational techniques for game-theoretic models and mechanism design, distributed multi-agent based models, and decision-support and optimization tools [1].

This paper describes strategy learning for new types of agents in Smart Grid markets and this paper analyzes the economic behaviors of such learned strategies. Our most recent paper, to be published at AAAI in July, presents novel models and algorithms for Smart Grid customers. I am also a co-creator of Power TAC, an open-source agent-based simulation environment for Smart Grid research and competition; this paper presents an overview and also serves as a game specification for the competition setting.

Factored Models for Multiscale Decision Making in Smart Grid Customers. P. Reddy and M. Veloso. To appear in the Proceedings of the Twenty-Sixth AAAI Conference on Artificial Intelligence (AAAI-12), Toronto, July 2012. [bib]
RSSI-based Physical Layout Classification and Target Tethering in Mobile Ad-hoc Networks. P. Reddy and M. Veloso. Proceedings of the IEEE International Conference on Intelligent Robots and Systems (IROS-11), San Francisco, September 2011. [bib]
Learned Behaviors of Multiple Autonomous Agents in Smart Grid Markets. P. Reddy and M. Veloso. Proceedings of the Twenty-Fifth AAAI Conference on Artificial Intelligence (AAAI-11), San Francisco, August 2011. [bib]
Strategy Learning for Autonomous Agents in Smart Grid Markets. P. Reddy and M. Veloso. Proceedings of the Twenty-Second International Joint Conference on Artificial Intelligence (IJCAI-11), Barcelona, July 2011. [bib]
The Power Trading Agent Competition. W. Ketter, J. Collins, P. Reddy, C. Flath and M. De Weerdt. Erasmus Research Institute of Management Report Series ERS-2011-011-LIS, SSRN 1975237, May-December 2011. [bib]

Robotic Ad-hoc Sensor Networks

I worked on DARPA's LANdroids program, whose goal was to create autonomous communication and surveillance networks in urban indoor environments using mobile robots. This paper describes our algorithms which allow the robots to reduce uncertainty about their relative locations and tether themselves to humans and other robots. (The project was covered by Gizmodo and Engadget.)

Astrophysics Data Mining

The goal here was to develop new statistical measures and data mining methods to describe the positions of galaxies in the universe. We used simulated positions of galaxies, generated according to prevalent cosmological theories, and developed multiple large scale sampling-based techniques for comparing subspaces and for estimating critical epsilon, the percolation threshold.

Multimodal Activity Recognition

This project used multimodal first person sensor data (video camera and inertial motion) from the CMU-MMAC database to identify the actions being performed by the person wearing the sensors. This was accomplished using an SVM-KNN combination with additional HMM-based smoothing. This paper provides further details.

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