Georgia Institute of Technology

The vision of this center is become an international thought leader in the area of Machine Learning. This includes training the next generation of machine learning experts, developers, engineers, and practitioners. As such, the proposed center will also play an active role in education initiatives. Such a synergistic connection between research and education is essential and therefore will be considered part of the core mission of the center to support the academic units at GA Tech in this area. Working with the three colleges (College of Computing, College of Engineering, and College of Sciences), we are in the process of establishing a Ph.D. degree in Machine Learning.  This will be 2nd degree offering at the level of a Ph.D. in the world, as Carnegie Mellon University already offers such an advanced degree. Our proposed degree, which is a collaborative effort between the faculty of computing, engineering, and sciences, will be first of its kind to include all three of the major STEM areas. Currently the faculty members, who will be part of this center are working in developing a full proposal for this new degree program and will be seeking approval to launch this program within the next few months. A pre-proposal (a prospectus) for this Ph. D. program has already been approved by all the participating schools, colleges, the institute graduate committee, and committee of the Regents of the University of Georgia System.

This new ML PhD program training will focus on: (i) foundational principles and essential algorithms for analyzing modern data sets and (ii) the integration of machine learning methods to interdisciplinary applications spanning all areas of data sciences. The planned initial themes are: (a) theory, aimed at the study of the principle foundations of data analytics and inference; (b) algorithms, aimed at studying, exploring, and developing new algorithms for machine inference from data; and (c) applications, aimed at both leveraging and expanding the ever-growing demands of data analytics in a variety of disciplines including medical data, smart cities, materials analysis, manufacturing, autonomous systems, and business analytics. It is important to note that these themes unite all of the six Colleges at Georgia Tech via a targeted approach to interdisciplinary, multi-scale research challenges spanning mathematical theory and statistics, computer science algorithms, and applications. The Machine Learning Ph.D. will complement existing Ph.D. programs at GT, and faculty involvement from Colleges of Computing, Engineering, Sciences, and Business will be instrumental in achieving program goals. The diversity of the faculty and their core disciplines adds to the uniqueness of what GT can offer and is world class in this area. Here we briefly describe some of the areas covered.

ML@GT will be a crucial entity as it will take leadership in working with the partner schools to establish and coordinate this program. As noted in the management plan, the Associate Director of Academics of the Center will serve as the lead who will work with Associate Deans of Academics for the Colleges and Associate Chair of Graduate Programs for each of the schools to ensure offering of relevant research exposure and mentorship to the Ph.D. students. While all these students will be admitted and enrolled in their respective colleges and schools, they will become core members of the center, as they will be working with the faculty who are members of this center.  Furthermore, the center will provide opportunities for these students to interact with our industrial partners and sponsors.  The Center also aims to establish funding mechanisms for Ph.D. students working in ML. Just recently, we have been able to work with Raytheon E-Systems to sponsor one Ph.D. student for their first two years in the program. We hope to pursue similar relationships with other potential funding entities.

ML@GT will also engage with the Interdisciplinary Master’s in Analytics offered by SCB, CoC and CoE and the Master of Science in Quantitative and Computational Finance (QCF) offered by SCB, ISyE, and Math. However, the center’s focus will remain on collaborating with the respective host schools of these degrees and supporting them, and not take any leadership or administrative role in these degrees. After the first few years, the center will also explore other education opportunities like developing an executive MS in ML with Georgia Tech Professional Education.