News

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Todd Humphreys Elected Institute of Navigation 2020 Fellow

Feb. 28, 2020
WNCG professor Todd Humphreys has received the Institute of Navigation’s (ION) “highest honor.” Humphreys was elected to the membership rank of Fellow at ION’s International Technical Meeting in January. He is one of only three recipients of the honor for 2020. Humphreys’ election cited his “significant and fundamental contributions to PNT security and precise GNSS positioning for the mass market, and for dedication to GNSS education and outreach.”
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WNCG Alum Receives ION Congressional Fellowship

March 10, 2017
WNCG alumnus Kyle Wesson was selected as the 2016-2017 Institute of Navigation’s (ION) Congressional Science and Technology Policy Fellow. Sponsored through the American Association for the Advancement of Science (AAAS), ION’s congressional fellow is selected from among eligible ION members to serve a one year appointment in Washington, DC, as a member of the personal staff of a US Senator or House Representative or to the professional staff of a Congressional Committee.
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Institute of Navigation presents Prof. Todd Humphreys with the Thomas L. Thurlow Award

Feb. 3, 2015
Manassas Virginia, January 28, 2015 - The Institute of Navigation (ION) presented its Thomas L. Thurlow Award to Dr. Todd Humphreys at the ION International Technical Meeting (ITM)in Dana Point, California, January 26-28, 2015. Dr. Humphreys was recognized for contributions that enhance radionavigation security and robustness in the face of intentional spoofing and natural interference.
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Prof. Joydeep Ghosh Gives Keynotes at WDDL2013 and DMH 2013

Sept. 3, 2013
Prof. Joydeep Ghosh of UT ECE was the keynote speaker at the inaugural Workshop on Divergences and Divergence Learning (WDDl), held in Atlanta, June 2013. In his talk, entitled "Learning Bregman Divergences for Prediction with Generalized Linear Models," which reflects joint work with ECE and WNCG student Sreangsu Acharrya,  an efficient approach to learning a broad class of predictive models was introduced. What is most remarkable about this approach is that model parameters can be estimated even when the loss function is unknown.