Dr. Timothy Havens
Assistant Professor, Electrical and Computer Engineering, and Computer Science
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.