UFFC has established the “IEEE UFFC Star Ambassador Lectureship Award”, similar to our Distinguished Lecturer award, but intended to be for more local travel and for early career speakers. Specifically, we imagine young professionals (within 15 years of their degree)delivering technical talks in their geographic region, highlighting their research while promoting IEEE and the UFFC Society.  We hope these awards will become an opportunity to expand our Young Professional members’ contact with colleagues and students at academic institutions, national laboratories and local industry, promoting new collaborations.  Talks at IEEE Section and UFFC-S Chapter meetings would also be supported.

Travel support is provided for up to $2500 (for multiple speaking opportunities). Awards will be made on nominations received by March 15th, June 15th, September 15th, and December 15th.

Interested candidates shall self-nominate by submitting a copy of their updated resume’, a short bio, a photo, their IEEE member number, as well as speaking plans (including targeted institutions for the visits), a lecture abstract, and a one (1) page maximum description of prior engagement with UFFC, if any. The application material should be sent to uffc-admin@conferencecatalysts.com.

The safety and well-being of our members are our priority. If meeting in person is not possible, virtual lectures are encouraged.

2020 Awards

Fei Li, Xi’an Jiaotong University, Xi’an, 710049, China

Topic: Modified Relaxor Ferroelectrics with Enhanced Polar State Heterogeneity

Himanshu Shekhar, Indian Institute of Technology, Gandhinagar, Gujarat, India

Topic: Harnessing Ultrasound and Microbubbles for Next Generation Imaging and Therapy

Joel Harley, University of Florida

Topic: Ultrasonic Physics-Informed Machine Learning

Yun Jing, North Carolina State University

Topic: Numerical Modeling of Medical Ultrasound

Nick Bottenus, University of Colorado Boulder, Boulder, CO

Topic: Maintaining Coherence and Expanding the Horizons of Ultrasound Imaging

Muyinatu Bell, Johns Hopkins

Topic: Deep Learning in Ultrasound