Associate Professor of Computer Science Charless Fowlkes has been awarded The Helmholtz Prize by the Institute of Electrical and Electronics Engineers (IEEE) for his paper “A database of human segmented natural images and its application to evaluating segmentation algorithms and measuring ecological statistics,” co-authored with then-UC Berkeley researchers David Martin, Doron Tal and Jitendra Malik in 2001.
Archives for December 2015
The Institute of Electrical and Electronics Engineers (IEEE) has named Computer Science Professor Michael Franz a 2016 IEEE Fellow. Franz is being recognized by IEEE for his contributions to just-in-time compilation as well as his contributions to computer security through compiler-generated software diversity.
The IEEE Grade of Fellow is conferred by the IEEE Board of Directors upon a person with an outstanding record of accomplishments in any of the IEEE fields of interest. It is the highest grade of membership and is recognized by the technical community as a prestigious honor and an important career achievement. The total number of fellows selected in any one year cannot exceed one-tenth of 1 percent of the total voting membership. “It is a great achievement receiving recognition from one’s peers and being included among such a distinguished group of IEEE members,” says Franz.
The IEEE is the world’s leading professional association for advancing technology for humanity with 400,000 members in 160 countries. Dedicated to the advancement of technology, the IEEE publishes 30 percent of the world’s literature in the electrical and electronics engineering and computer science fields, and has developed more than 900 active industry standards.
Although they emphasized that they would not be comfortable handing over this level of control to an algorithm, several speakers at the CERN workshop discussed how deep learning could be applied to physics. Pierre Baldi, an AI researcher at the University of California, Irvine who has applied machine learning to various branches of science, described how he and his collaborators have done research suggesting that a deep-learning technique known as dark knowledge might aid — fittingly — in the search for dark matter.
View the full story on the Nature website.