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Intel, NEC, Western Digital Join WNCG Industrial Affiliates

May 15, 2018
This Spring, WNCG welcomes Intel, NEC, and Western Digital as the newest WNCG Industrial Affiliates Program members. All three companies enter as Level 2 members. Since WNCG’s founding, the Industrial Affiliates Program has played a vital role in the group’s success. It facilitates “cooperation and mutual assistance” between WNCG and Affiliate companies. This collaboration between academia and industry embodies the group’s mission to “support research, provide highly relevant education and opportunities, [and] promote technical innovation, imagination and entrepreneurship.”
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Fujitsu Laboratories of America Joins WNCG Industrial Affiliate Program

April 13, 2017
Fujitsu Laboratories of America recently joined the WNCG Industrial Affiliate Program as a Level Two Member.
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Toyota ITC Joins IAP Program

Jan. 26, 2017
Toyota InfoTechnology Center, U.S.A. (Toyota ITC), recently joined the WNCG Industrial Affiliate Program as a level one member, and as a founding member of the UT SAVES research initiative.
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WNCG Welcomes Verizon Wireless to Industrial Affiliate Program

Dec. 8, 2016
Governmental regulations and privacy concerns restrict access to data sets for individuals and only provide researchers with data in aggregated form. However, current machine learning and data mining techniques are restricted in their ability to effectively use such data. These limitations create roadblocks for companies looking for ways to better respond to their consumers’ needs while also providing safety and security for individual users.  
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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.