Team Develops Pathway for a Memory System that Operates Near Absolute Zero Temperature The emergence of social media, widespread usage […]

Dongarra Professor of Computer Science Michela Taufer and EECS Computer Science graduate student Nigel Tan have been awarded a Best […]

Thousands of miles away from home, I found a country similar and strange to everything I am used to. Due […]

In the world of technology, there have been very few things that have driven conversation more in the last few […]

Learning thermodynamics and circuits and seeing and touring different historical engineering sites, combined with building friendships and connections with my professors and classmates, has made this study abroad an experience that I will never forget.

For his research in incorporating phase transition materials into electronics applications, Electrical Engineering Assistant Professor Ahmedullah Aziz has received the Outstanding Ph.D. Dissertation Award in Electronic Design Automation from the Association of Computing Machinery.

Bai’s research employs a new approach to electric vehicle applications, using what are called hybrid switching modules. His team’s designs utilize both the more expensive gallium nitride devices and the less expensive silicon devices in tandem, resulting in greater operating efficiency without hiking up the cost.

Included in that group are two University of Tennessee alumni from what is now the Min H. Kao Department of Electrical Engineering and Computer Science, Lynne Parker (’83) and Mark Dean (’79). In addition to their shared ties to the department, both later served as interim deans of the Tickle College of Engineering, giving UT a strong connection to the new task force.

Mench, a Chancellor’s Professor, the Condra Chair of Excellence, and a professor of mechanical engineering in the Department of Mechanical, Aerospace, and Biomedical Engineering, has served as interim dean since February. Mench joined UT in 2010 after serving as an associate professor of mechanical engineering at Pennsylvania State University.

A team of UT electrical engineering and computer science students used machine learning to predict feedback in a popular Reddit forum to identify jerks.