Jacob Aguirre is a recipient of the NSF award titled "Equitable and Comprehensible Machine Learning for Expert-in-the-loop Decisions in Medicine." 

His research focuses on developing computationally efficient optimization algorithms and data-driven heuristics for Predictive and Prescriptive Analytics.

Aguirre’s primary interests revolve around tackling challenges in the domains of Causal Inference, Personalized Medicine, and Medical Decision-Making. He is grateful to be advised by Dr. Turgay Ayer and Dr. Gian-Gabriel Garcia, and his work aims to advance the field of machine learning in healthcare.

Joseph Boone, a recipient of the NSF Graduate Research Fellowship Program (GRFP) award, is interested in game theoretic optimization, logistics in contested environments, supply chain and infrastructure network resiliency, and applied statistics and simulation for risk-aware system design.

His primary research goals involve developing methods to solve complex, multilevel optimization problems that arise in optimizing supply chain and infrastructure networks against worst-case or adversarial disruptions. 

Joseph aspires to apply these methods in various fields, including military logistics, equity in healthcare, and disaster relief efforts.

Alina Gorbunova, a recipient of the NSF GRFP, focuses her research on systems monitoring, diagnostics, and prognostics using high-dimensional and high-variety data with machine learning and data analytics techniques.

Her research interests lie in creating a scalable causation-based quality improvement framework. This framework aims to monitor, diagnose, and control multistage manufacturing systems by leveraging high-dimensional and high-variety data. 

Gorbunova's work contributes to advancing the field of quality improvement in manufacturing systems through the application of machine learning and data analytics.

Author: Camille C. Henriquez