ML@GT Announces First Ph.D. Fellowship Program

Beginning in January 2020, the Machine Learning Center at Georgia Tech (ML@GT) will be supporting select Ph.D. students through a new ML@GT Fellows Program. Created with the intent to foster novel Ph.D. research in machine learning (ML) or artificial intelligence (AI), the program expands Georgia Tech’s rapidly growing presence in the field.

“ML and AI is an increasingly important field in all aspects of life. As a center that works to train the next generation of leaders in socially and ethically responsible ways, we hope that this program will allow more students who are interested in the field to pursue their education,” said ML@GT Director Irfan Essa.

The center anticipates selecting four students who will receive roughly half of a graduate research assistant (GRA) appointment. While ML is a collaborative field with many subfields, the focus of projects must be on advancing machine learning and artificial intelligence methods that enable applications rather than creating the applications themselves.

The program is open to any Georgia Tech Ph.D. student who has a mentor or advisor that is affiliated with the center. Preference will also be given to students who are not already supported by a fellowship.

Applications for the fellowship are due on Tuesday, Oct. 15, 2019 at 12 p.m. with award notices being sent by early January 2020.

Visit the application portal for more information and to apply.

About the Machine Learning Center at Georgia Tech

The Machine Learning Center at Georgia Tech is an interdisciplinary research center bringing together more than 190 faculty members and 60 machine learning Ph.D. students from across the institute for meaningful collaboration and innovation in machine learning and artificial intelligence. Students and faculty are experts in areas including, but not limited to computer vision, natural language processing, robotics, deep learning, ethics and fairness, computational finance, information security, and logistics and manufacturing. For more information, visit www.ml.gatech.edu