Research

Research interests

I am part of the Distributed Intelligence Lab (DILab). My research under Dr. Lynne Parker focuses on signal processing in mobile and sensor Networks. My research interests include machine learning, robotics, wireless sensor networks, Artificial General Intelligence (AGI), distributed multi-agent systems, distributed processing and decision making.

Movies

Research projects

Fall 2006 - present: Distributed modeling and anomaly detection in mobile and sensor networks
In this project, we are developing algorithms for distributed sensor data modeling and anomaly detection in systems of sensor networks and autonomous mobile robots using machine learning techniques. Data modeling in distributed systems is critically important for determining the "normal" operation mode of the system. Being able to model the expected sensor signatures for typical operations greatly simplifies the human designer's job in the practical application of new sensor and mobile robot systems; by enabling the system to autonomously characterize the expected sensor data streams, the system can learn the important features of its environment to monitor. This, in turn, allows the system to perform autonomous anomaly detection to recognize when unexpected sensor signals are detected. This type of distributed sensor modeling and anomaly detection can be used in a wide variety of sensor network and mobile robot applications, such as detecting the presence of intruders, detecting sensor failures, detecting anomalous human motion patterns, detecting unexpected chemical signatures, and so forth. The advantage of this approach is that the human designer does not have to characterize the anomalous signatures in advance. Instead, the system of sensor nodes and mobile robots can learn this characterization autonomously, for quick application to new domains. Our work focuses on the research aspect of these issues. The techniques being developed employ distributed statistical machine learning approaches for sensor data modeling and anomaly detection. The algorithms are being designed, developed, evaluated, and validated experimentally in laboratory application scenarios, such as intruder detection, or environmental coverage and exploration.

Summer 2004 RA: Indoor positioning using WLAN