Two Georgia Tech machine learning Ph.D. students, Caleb Ziems and Kevin Tynes Jr., have been recognized as fellows of the National Science Foundation Graduate Research Fellowship Program (NSF GRFP).
NSF GRFP recognizes outstanding students in NSF-supported STEM fields who are pursuing research-based master’s or doctoral degrees at an accredited United States institution. Ziems and Tynes Jr. will each receive a five-year fellowship with three years of financial support. This includes an annual stipend of $34,000 and a $12,000 cost-of-education allowance.
Ziems, who is advised by School of Interactive Computing (IC) Assistant Professor Diyi Yang, studies the social implications of language on the web and aims to make the internet a safer, more civil, and enjoyable place for everyone. He examines anti-social behaviors like hate-speech and cyberbullying, political orientation of text, and framing effects. Drawing on methods from computational social science and natural language processing, Ziems’ research also studies the ethical behavior of language models that are trained on web-scale language data.
“The field is growing quickly, and the inherently interdisciplinary nature of the work I do can make for some messy problems. The fellowship will lift the burden of funding projects, allowing me to freely pursue important and challenging questions like what to do about problematic behavior on the internet,” said Ziems.
Tynes Jr., is currently earning a master’s in computer science at Georgia Tech and will continue his studies in the machine learning Ph.D. program in Fall 2021. He is in the process of finding an advisor.
“Being named an NSF Fellow means that my hard work is paying off and gives me confidence that I can achieve my goal of becoming a tenure-track professor. It also allows me to continue advocating for diversity in science, technology, engineering, and math fields where Black scientists and engineers are widely underrepresented,” said Tynes Jr.
He is interested in designing machine learning algorithms that can provide insights into the function of chemical and biological systems. His research will develop a neural network architecture that uses molecular symmetry functions and a modular design to learn properties of multi-molecular structures. This will address the need for accurate formation and function predictions in lipid nanoparticle-mediated mRNA drug delivery.
First-year machine learning Ph.D. student Andrew Szot was accorded an honorable mention. Szot is co-advised by the IC Associate Professor Dhruv Batra and Assistant Professor Zsolt Kira.
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