Dr. Timothy Havens

Associate Professor, Electrical and Computer Engineering, and Computer Science

Timothy Havens Dr. Havens’ research focuses on applications of and methods in machine learning and pattern recognition, computational intelligence, and signal and image processing. Most recently, he has applied his research in three main areas: stand-off detection of explosive hazards using multi-modal sensor systems, sensor fusion from heterogeneous robotic platforms, and machine learning in heterogeneous and big data. In the first area, he is focusing on fusion of forward-looking ground-penetrating radars and camera sensors to detect and locate buried explosive devices. Dr. Havens’s novel method of autonomously characterizing the target environment and adaptively fusing radar and visible-spectrum camera sensors has significantly improved the detection capabilities of a US Army system.

Together with Colin Brooks at Michigan Tech Research Institute (MTRI), Dr. Thomas Oommen in Geological & Mining Engineering, and Dr. Tess Ahlborn in Civil and Environmental Engineering, Dr. Havens has been investigating the use of UAVs for transportation inspection and monitoring. He has developed a sensor fused platform and sensor fusion algorithms that combine inertial sensors, visual spectrum video, and LIDAR to construct high-quality three-dimensional maps of transportation infrastructure from micro-UAVs.

The last area, Big Data, was recently recognized by the White House as an important challenge for US researchers. Dr. Havens has developed several algorithms for pattern discovery in large data sets. Currently, he is developing new machine learning methods for Big Data and is also investigating how his methods can be applied to social network analysis. Dr. Havens’ article on FCM algorithms for Big Data is the most downloaded paper in the IEEE Transactions on Fuzzy Systems.

For more information, please visit Dr. Havens' website.

Recent publications

01 Multi-Band Sensor-Fused Explosive Hazard Detection in Forward-Looking Ground-Penetrating Radar
T. C. Havens, J. Becker, A. Pinar, and T. J. Schulz
SPIE Proceedings, vol. 9072, p. 90720T (2014)
02 Quadratic Program-based Modularity Maximization For Fuzzy Community Detection In Social Networks
J. Su, T. C. Havens
IEEE Transactions Fuzzy Systems (2014)
03 Fuzzy Community Detection in Social Networks Using A Genetic Algorithm
J. Su and T. C. Havens
IEEE International Conference on Fuzzy Systems, Beijing, China. July 2014
04 Scalable Approximation Of Kernel Fuzzy C-means
Z. Zhang, T. C. Havens
IEEE International Conference on Big Data, Silicon Valley, CA. October 2013.
05 ClusiVAT: A Mixed Visual/numerical Clustering Algorithm For Big Data
D. Kumar, M. Palaniswami, S. Rajasegarar, C. Leckie, J. C. Bezdek, T. C Havens
IEEE International Conference on Big Data, Silicon Valley, CA. October 2013.