James L. Carmon Scholarship Award

The James L. Carmon Award is presented to University of Georgia graduate students who have used computers in innovative ways. Named for the late James L. Carmon, a UGA faculty member for 36 years who helped make the university a leader in computing research and development, the award was established by the Control Data Corp. Each year, graduate students may be selected as Carmon Scholars or for Honorable Mention.

2023 Recipients

Alison Banks, a doctoral student in the Department of Geography, applies critical state-of-the-art Earth system modeling to understand how climate change affects global air quality. Each rainfall event cleanses the atmosphere of harmful pollutants (such as particulate matter), but climate change is redistributing rainfall patterns and frequency across the globe. Conventional representation of rainfall in climate models tends to bias rainfall as “too-light-and-too-frequent.” By crafting a simulation where particle emissions are held constant, combined with a new technique that represents clouds at all scales, Banks is developing a model that could help identify areas where air quality is deteriorating because of climate change. Her research advances scientific understanding of rainfall patterns and the health impacts of harmful air quality events.

Ehsan Latif, a Ph.D. student in the School of Computing, is an exceptional scientist who has demonstrated significant productivity, creativity and insightfulness. Latif works at the computing intersection of advanced algorithms and wireless networking innovations for multi-robot systems. He is developing methods to improve multi-robot systems in coordinating their actions for localization and exploration. These systems must know their individual locations while continuously communicating with each other so they can perform complex tasks and interact with human observers (such as collaborating with first responders in post-disaster search and rescue efforts. Latif’s research has led to algorithms that provide high localization accuracy and efficient exploration for robots while reducing communication and computational demands. He also explores the use of network-related devices like sensors to solve problems, such as localizing robots in unknown environments and designing new frameworks for multiple robots to collaborate. He exploits dynamic reinforcement learning with optimized usage to provide efficient exploration.

Past Recipients