Category: James L. Carmon Scholarship Award

Omid Arhami, a doctoral student in the Institute of Bioinformatics (IoB), is recognized for innovative research that advances the ability to predict infectious disease threats by linking viral evolution to population-level outbreaks. Working under the advisement of IoB’s Pejman Rohani, Arhami conducts research at the intersection of bioinformatics, statistics, and epidemiology, developing new computational methods to identify which viral mutations drive meaningful changes in immune response—an essential challenge in vaccine design and pandemic preparedness. Arhami is the developer of Topolow, a novel algorithm that transforms sparse and incomplete viral data into accurate antigenic maps, overcoming limitations that have constrained the field for decades. Published in Bioinformatics and released as open-source software, the method is already being adopted by researchers across immunology and related life sciences. Arhami’s work also connects molecular change to epidemic dynamics, with applications to influenza and other rapidly evolving pathogens, reflecting an ambitious and impactful research profile. 

Qitao Tan, a doctoral student in the School of Computing, is recognized for research that advances how powerful artificial intelligence models can be trained and deployed on resource-limited devices. Working under the advisement of Geng Yuan, Tan develops new algorithms that make it possible to adapt large-scale AI models—such as large language models—without their typical heavy computational demands. His work addresses a longstanding disconnect between AI algorithms and the hardware on which they must ultimately run. Tan has already published multiple first-authored papers in highly selective international venues. One of his papers received a Best Paper nomination at the International Conference on Computer-Aided Design, a top-tier conference in electronic automation design. Together, these accomplishments reflect an unusually strong record of innovation, productivity, and interdisciplinary impact. 

James L. Carmon Scholarship (Honorable Mention) 2025

Aiman Munir, Ph.D. candidate in the School of Computing, develops advanced algorithms for robotics and multi-robot systems, focusing on energy-efficient informative path planning (IPP) and task coordination in GPS-denied environments. Under the mentorship of Assistant Professor Ramviyas Nattanmai Parasuraman, she has pioneered novel approaches to IPP and coverage control, optimizing how robots explore unknown environments while conserving energy. Her research has led to multiple peer-reviewed publications in top robotics conferences and has applications in precision agriculture, environmental monitoring, and search-and-rescue operations. Munir’s energy-aware coordination framework improves robot performance in resource-limited settings. She received the Outstanding Graduate Student Award from the School of Computing and has contributed to open-source robotics research. Munir’s work looks to the future of autonomous systems, enhancing their efficiency, adaptability, and real-world applications.

James L. Carmon Scholarship 2025

Luyang Fang, Ph.D. candidate in the Franklin College of Arts and Sciences Department of Statistics, develops innovative machine learning algorithms that improve the efficiency and reliability of large language models. Under the mentorship of professors Ping Ma and Wenxuan Zhong, she has pioneered Bayesian Knowledge Distillation (BKD), an advanced method for compressing AI models while quantifying uncertainty in their predictions—critical for deploying AI in healthcare, finance, and autonomous systems. Fang has published in top venues such as the International Conference on Machine Learning and IEEE Transactions on Smart Grid, and her BKD framework has been applied to computer vision and education assessment. She also co-developed MultiCOP, a machine learning tool for microbiome-metabolome analysis, and collaborates on AI-driven research across multiple disciplines. A recipient of multiple Georgia Statistics Day awards, Fang’s work helps shape the future of trustworthy AI and statistical machine learning.