I’m thrilled to announce that my student, Antone Chacartegui, a standout in our MS in Mathematics program at Boise State, has earned the prestigious National Science Foundation Graduate Research Fellowship (NSF GRFP). This highly competitive award will support Antone as he transitions to the PhD in Computing program here at Boise State.
Innovating Ocean Modeling
Antone has been working on developing machine-learning-assisted adaptive solvers for partial differential equations. His current focus involves deep reinforcement learning to decide where to increase or decrease grid resolution to optimize the solution’s accuracy and computational cost. He is adapting an approach developed by Foucart et al. (2023) at MIT and extending it to shallow water equations. In his Ph.D. work, Antone will expand this research to full ocean models, allowing for previously inaccessible high-resolution adaptive simulations of ocean processes, potentially transforming the ocean modeling field and improving weather forecast, extreme weather resilience, and our understanding of this crucial component of the Earth system.
Increasingly Competitive Fellowship
This year, the NSF awarded only 1,000 fellowships, a significant reduction from the more than 2,000 fellowships awarded in previous years. This decrease is a direct result of recent federal budget cuts, highlighting Antone’s exceptional achievement in receiving one of these highly sought-after awards.
Growing Excellence at Boise State
Antone’s success reflects the vibrant research community we’ve cultivated at Boise State. Over the past decade, the number of GRFP recipients choosing to pursue their graduate education here has grown dramatically—from the occasional student to seven active fellows this past academic year (2023/24). This growth highlights Boise State’s increasingly strong reputation in advanced research and mentoring.
Congratulations, Antone, on this well-deserved recognition, and thank you for exemplifying the excellence and innovation we strive for at Boise State, in the Math Department, and in my research group. I am excited to continue working with you and witness your success and groundbreaking contributions!
References
Foucart, C., Charous, A., & Lermusiaux, P. F. (2023). Deep reinforcement learning for adaptive mesh refinement. Journal of Computational Physics, 491, 112381.
