Carnegie Mellon scientist wins international award for computational biology

The International Society for Computational Biology (ISCB) has awarded its Overton Prize for outstanding accomplishment to Ziv Bar-Joseph, associate professor in Carnegie Mellon University's Lane Center for Computational Biology and Machine Learning Department.

The Overton Prize is awarded annually to an early- to mid-career scientist who has made a significant contribution to the field of computational biology. In recognition of the award, Bar-Joseph will give a keynote address this July at the annual International Conference on Intelligent Systems for Molecular Biology in Long Beach, Calif.

Bar-Joseph, who joined CMU's School of Computer Science (SCS) faculty in 2003, applies machine learning, statistical algorithms and signal processing techniques to the analysis of high-throughput biological data. He has led international research efforts that have identified genes important to human cell division, including a subset associated with cancer cells, which have uncovered new insights into gene regulatory networks.

In a study published last year in the journal Science, he and his colleagues observed methods that have evolved to organize cells during nervous system development. The same methods, they concluded, could be used to improve the deployment of wireless sensor networks and other distributed computing applications.

"It's stunning how he is able to handle such a diverse set of technical methods," said Burkhard Rost, president of the ISCB. "He's a perfect example of a new generation of scientists."

Alfonso Valencia, chair of the ISCB Awards Committee, added that the committee members were impressed not only by the quality of Bar-Joseph's scientific contribution, but by the novelty of the approaches he has developed.

"I am very pleased that Ziv has been chosen for this very richly deserved honor," said Robert F. Murphy, director of SCS's Lane Center for Computational Biology. "Ziv's work represents an outstanding example of computational biology research: the use of novel and appropriate machine learning methods in deep collaborations with accomplished experimental biologists to yield significant biological results."

Provided by Carnegie Mellon University