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The left panel shows a raw image of Xenopus embryo tissue. This tissue is well-known to be extremely scattering, which is evident by the fact that we cannot resolve any structures in the raw image. The right panel shows the result of our computational scatter-correction method, which drastically improves imaging capability. After scatter-correction, cellular boundaries, nuclei, and yolk platelets can be clearly identified with subcellular resolution.

Published at Dell Medical School Visionaries Your brain can be taught to signal a computer. If José del R. Millán has his way, brain-computer interfaces will one day make wheelchairs obsolete. Ten years after his stroke, a man paralyzed from the waist down starts “walking,” operating an exoskeleton with his mind. Researchers led by José del R. Millán, Ph.D., study the electrical activity of the man’s brain indicating when he wants to use the robot to take a right step versus a left one. 

WNCG faculty member Jon Tamir has been named among the inaugural cohort of Oracle Research Fellows. The Oracle Research Fellows program identifies potentially transformative research proposals from current and future research luminaries, and provides them with the funding, Oracle Cloud computing and collaborative opportunities they need. Tamir will work to develop fast, standardized MRI reconstruction methods for faster and cheaper diagnosis and monitoring.

This news feature is part of Dell Med's Voices, a series of profiles that highlight the people of Dell Med as they work to improve health with a unique focus on our community. How can a computer help someone regain motor function? José del R. Millán, Ph.D., a professor in Texas ECE and the Dell Medical School Department of Neurology, tackles this question as he designs brain-computer interfaces that empower people to surpass their limits. Q&A WHAT’S THE PROBLEM YOU’RE TRYING TO SOLVE, AND HOW DID YOU COME TO RECOGNIZE IT?

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.