The Machine Learning Center at Georgia Tech (ML@GT) has recognized six Ph.D. students as its first ML@GT Fellows. The students – Mehrdad Ghadiri, Maksim Sorokin, Shixiang (Woody) Zhu, Sejoon Oh, Austin Xu, and Sajad Khodadadian – were selected based on their research projects that are focused on the advancement of machine learning (ML) and artificial intelligence (AI).
“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.
Each of the recipients will receive funding equal to half of a graduate research assistant appointment for Spring and Summer 2021.
About the Recipients
Ghadiri is a multidisciplinary computer science Ph.D. student in the School of Computer Science (SCS) and Algorithms, Combinatorics, and Optimization (ACO) program and is advised by SCS Frederick G. Storey Chair in Computing and Professor Santosh Vempala. His research interests focus on combinatorial and convex optimization, theoretical machine learning, fairness, differential privacy, data mining, and design and analysis of algorithms. The fellowship will further support his work explored in Socially Fair k-Means Clustering.
Sorokin is a robotics Ph.D. student in the School of Interactive Computing (IC) advised by IC Assistant Professor Sehoon Ha and Stanford University Associate Professor Karen Liu. His research examines the applications of vision-based reinforcement learning in real-world robotics. He is currently tackling outdoor navigation and environment interaction problems for quadrupedal robots. Sorokin’s supported project is titled, Vision based Urban Navigation for Autonomous Robots.
Inspired by the applications of ML and data science to areas like police operation, intelligent transportation, power grid resilience, and financial security, Zhu will continue to explore these areas in his project titled, Adversarial learning of point process generative models. Zhu is advised by ML@GT Associate Director and H. Milton Stewart School of Industrial Systems and Engineering (ISyE) Harold R. and Mary Anne Nash Early Career Professor and Associate Professor Yao Xie.
Oh’s research interests include recommender system, adversarial machine learning, and data mining, and parallel computing. He is a second-year computer science Ph.D. student in the School of Computational Science and Engineering (CSE) and is advised by Srijan Kumar, an assistant professor in CSE.
Xu, a second-year electrical engineering Ph.D. student with a concentration in machine learning, is advised by ML@GT Associate Director and School of Electrical and Computer Engineering Associate Professor Mark Davenport. He is interested in facilitating human-machine collaboration in an interpretable and mathematically grounded manner.
“I believe that such collaboration requires two-way communication; machines need to be able to quickly understand human preferences, while humans need to understand a machine’s decision-making process,” said Xu.
Third-year operations research Ph.D. student, Khodadadian is researching theoretical reinforcement learning with an emphasis on convergence analysis of policy space methods in reinforcement learning. Khodadadian is advised by ISyE Fouts Family Early Career Professor and Assistant Professor Siva Theja Maguluri.
ML@GT is an interdisciplinary research center bringing together more than 200 faculty members and 94 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
Story by Allie McFadden, Communications Officer, firstname.lastname@example.org