- Associate Professor, UNH, ECE Department
July 2006 to present
- Faculty Fellow, Volpe Center, July 2014 to present
- Assistant Professor, UNH, ECE Department
August 2000 to July 2006
- Visiting Professor, UNH ECE Department
June 1999 to August 2000
- Development Engineer, Falmouth Scientific, Inc.
September 1997 to June 1999
- University of New Hampshire, Ph.D. Engineering (Electrical Engineering option), 1997
- University of New Hampshire, MS Electrical Engineering, 1994
- University of New Hampshire, BS Electrical Engineering, 1992
Andrew Kun received his Ph.D. in Electrical Engineering from the University of New Hampshire in 1997. For his dissertation Andrew implemented a new control algorithm for a two-legged walking robot. After receiving his doctorate Andrew spent two years in industry working as development engineer at Falmouth Scientific, Inc., an oceanographic instrumentation manufacturer from Cataumet, MA.
Andrew was the principal investigator of the Project54 effort which involved integrating embedded mobile computing equipment and wireless networking into police cruisers. The Project54 system integrates car-based electronic systems and provides a speech user interface to them. There were over 1,000 police cruisers equipped with the Project54 technology nationwide.
Currently, a significant part of Andrew’s research is focused on driving simulator-based exploration of in-car user interfaces, and estimation methods of the drivers’ cognitive load to determine the effect of the user interface on the driving performance. In this area he is interested in speech interaction, as well as the use of visual behavior and pupil diameter measures to assess and improve the design of user interfaces. He served as the General Chair of the 2012 AutomotiveUI conference, and continues to serve as a Steering Committee member for the conference series. In addition to transportation-related work, Andrew is also involved in the experimental modeling of collaborative user activities around large-scale multi-touch displays, with a focus on the use of visual behaviors of multiple participants in parallel in building these models.