Alex Rodríguez receives Georgia Tech Outstanding Doctoral Dissertation Award

Alexander (Alex) Rodríguez, assistant professor of computer science and engineering (CSE), has received the Outstanding Doctoral Dissertation Award from the Georgia Tech College of Computing. The award recognizes the excellence and impact of his doctoral research on harnessing artificial intelligence (AI) to monitor and predict the spread of epidemics. 

The Outstanding Dissertation Award is the College of Computing’s highest honor for doctoral research, and seeks to recognize dissertations that exhibit exceptional novelty, impact, and quality. 

As the Covid-19 pandemic starkly showed, there is a crucial need for more robust tools to monitor and forecast epidemics. The ability to accurately survey and predict an outbreak’s spread is fundamental to good decision-making at all levels, from the government to individuals. However, the complexities of an epidemic, from mobility patterns to difficulties in data collection, make such an effort a herculean challenge. 

Seeking to address this, Rodríguez’s dissertation, titled “Artificial Intelligence for Data-centric Surveillance and Forecasting of Epidemics,” explored the development of various AI methods to pave the way for more precise data-driven models for epidemic surveillance and forecasting. He specifically looked at methods that overcame the unique obstacles seen in epidemiology, including data sparsity, poor data quality, and distributional changes. His work also addresses the practical challenges of operationalizing these models, drawing on his experience in supporting the Centers for Disease Control and Prevention (CDC)’s response to Covid-19 and influenza.

His dissertation research is a significant step forward in the improvement and implementation of AI data-centric models in epidemiology, helping inform more accurate and timely decisions in epidemics and other public health contexts.

Prior to joining the faculty at CSE, Rodríguez completed his PhD in Computer Science at Georgia Tech. Before that, he earned an MS in Data Science at the University of Oklahoma. An extension of his doctoral research, his current work focuses on developing AI methods to model spatiotemporal dynamics in complex systems. This includes applications in public health, environmental modeling, climate science, and more.