Song’s elevation comes on the heels of a stellar stint as an assistant professor for the School of Computational Science and Engineering (CSE). Over the past six years, Song has won several accolades for his research, which include a National Science Foundation CAREER award and six best paper awards.

Furthermore, Song has attracted approximately $4.2 million in external research grants and played a vital role in establishing the new Center for Machine Learning at Georgia Tech, which opened in July 2016. He is regularly published in top scientific journals and holds leadership positions with many premier conferences and editorial boards.

“Le is an internationally recognized leader in machine learning,” CSE Chair David Bader said. “His research is highly regarded among his peers, and he is becoming well-known for applying his findings in exciting and profound new ways. We are all excited about his promotion.”

Song’s research uses machine learning to answer complex data science questions. He uses the information he collects to build scalable frameworks to solve challenging real-world problems. Most recently, he and his students won a best paper award for their recommendation systems research, which has the potential to help create patient-specific medications.

“I am extremely excited about this recognition,” Song said. “I look forward to collaborating with my peers and students from across campus on more solution-focused research.”

Song received his master's and Ph.D. degrees in computer science from the University of Sydney, Australia in 2004 and 2008, respectively. He was also a Ph.D. student in the Statistical Machine Learning program at the National Information and Communications Technology, Australia's excellence center. Before joining Georgia Tech, Song was a research scientist with Google Research and completed a postdoctoral fellowship at Carnegie Mellon University.

Song’s promotion will take effect in July 2017.