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Prof. Robert Heath Gives Keynote at IEEE ML4COM

June 19, 2018
Prof. Robert Heath delivered a keynote speech at IEEE Communication Society’s 2018 International Conference on Communications (IEEE ICC).
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Digesting Big Data

Aug. 24, 2015
With Big Data playing a role in the lives of companies and individuals across the globe, and data being collected on everything from apps to electronic health records to parking meters, society debates how best to use this mass of information. “The stormy sea of Big Data can lead to data indigestion,” WNCG Associate Director Prof. Constantine Caramanis states. “We are interested in the application of data for engineering problems, from petroleum to health to recommendation engines.”
An image of a man with glasses in front of a binary code.

Digesting Big Data

Aug. 24, 2015
With Big Data playing a role in the lives of companies and individuals across the globe, and data being collected on everything from apps to electronic health records to parking meters, society debates how best to use this mass of information. “The stormy sea of Big Data can lead to data indigestion,” WNCG Associate Director Prof. Constantine Caramanis states. “We are interested in the application of data for engineering problems, from petroleum to health to recommendation engines.”
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Prof. Joydeep Ghosh Receives Two NSF Grants for Work with Complex Data

Oct. 3, 2014
UT ECE professor Joydeep Ghosh has received two research awards from the National Science Foundation (NSF) totalling more than $1 Million focusing on topics in complex data modeling in the healthcare field..
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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.