Summary of Research
My research focuses on developing autonomous systems that can adapt to complex environments. During my undergraduate studies in Computer Science at Grand Valley State University, I conducted research in evolutionary computation and reinforcement learning. One area of my work explored methods for improving the efficiency of evolutionary algorithms when solving computationally expensive problems. I also studied evolvability through computational modeling to better understand how long-term adaptive potential affects evolving systems. In my undergraduate capstone project, I designed control systems that enabled autonomous satellites to select from a diverse set of trajectories and execute docking maneuvers in simulation using reinforcement learning.
One major application of my research is autonomous spacecraft operations through an ongoing collaboration with the Air Force Research Laboratory (AFRL). In this area, I contribute to artificial intelligence systems that improve autonomous satellite maneuvers and reduce human workload during space operations. Beyond individual maneuvers, my research also examines how autonomous spacecraft can manage multiple mission objectives while minimizing fuel use and maintaining safety and performance. To support this work, I use software simulation platforms that model realistic spacecraft dynamics and scenarios. More recently, I have focused on verification, mode-switching behavior, and hardware implementation for autonomous satellites. Through this work, I have become more motivated to develop reliable AI systems that can adapt to real-world conditions.
As a Ph.D. student in Computer Science at Clemson University, my research will focus on Vision-Language-Action (VLA) models for autonomous ground vehicles through the Virtual Prototyping of Autonomy-Enabled Ground Systems (VIPR-GS) Center. This work will build on my experience in reinforcement learning and autonomous systems by extending these methods to the control and perception challenges of ground vehicle platforms. After earning my Ph.D., my goal is to contribute to autonomous systems research at a national laboratory or research institution.
Publications
Marcos Sanson and Austin J. Ferguson. (2025, October 6). A computational baseline for changes in long-term evolvability. In Proceedings of the 2025 Artificial Life Conference (ALIFE 2025). International Society for Artificial Life, Kyoto, Japan, 623-632. doi.org/10.1162/ISAL.a.895
Alexander Lalejini, Marcos Sanson, Jack Garbus, Matthew Andres Moreno, and Emily Dolson. (2024, August 1). Runtime phylogenetic analysis enables extreme subsampling for test-based problems. In Proceedings of the Genetic and Evolutionary Computation Conference Companion (GECCO '24 Companion). Association for Computing Machinery, New York, NY, USA, 511-514. doi.org/10.1145/3638530.3654208
Marcos Sanson (Presenter) and Austin J. Ferguson. (2026, April 24). A computational baseline for changes in long-term evolvability. Grand Valley State University Computing Innovation Day, Grand Rapids, Michigan, United States.
Marcos Sanson (Presenter), Alexander Lalejini, and Loren J. Anderson. (2026, April 8). Autonomous Spacecraft Docking Using Diverse Strategies With Reinforcement Learning. Grand Valley State University Student Scholars Day, Allendale, Michigan, United States.
Marcos Sanson (Presenter), Loren J. Anderson, and Alexander Lalejini. (2025, December 5). Diversity in Spacecraft Trajectories with Deep Reinforcement Learning. Grand Valley State University Computing Innovation Day, Grand Rapids, Michigan, United States.
Marcos Sanson (Presenter), Nikita Agrawal, Loren J. Anderson, John Beighle, Aaron Lee, Andrea Schefer, and Steven Tran. (2025, October 18). Reinforcement Learning for Spacecraft Rendezvous and Docking. Rice University Gulf Coast Undergraduate Research Symposium (GCURS), Houston, Texas, United States.
Marcos Sanson (Presenter) and Austin J. Ferguson. (2025, October 7). A computational baseline for changes in long-term evolvability. International Society for Artificial Life, Artificial Life Conference (ALIFE), Kyoto, Japan.
Marcos Sanson (Presenter), Nikita Agrawal, Loren J. Anderson, John Beighle, Aaron Lee, Andrea Schefer, and Steven Tran. (2025, September 4). Artificial Intelligence Spacecraft Control Using Reinforcement Learning. University of New Mexico McNair Scholars Research Conference, Albuquerque, New Mexico, United States.
Marcos Sanson (Presenter), Nikita Agrawal, Loren J. Anderson, John Beighle, Aaron Lee, Andrea Schefer, and Steven Tran. (2025, August 6). Artificial Intelligence for Spacecraft Control in Contested Environments. Air Force Research Laboratory Scholars Summer Showcase, Kirtland Air Force Base, Albuquerque, New Mexico, United States.
Marcos Sanson (Presenter) and Austin J. Ferguson. (2025, April 9). Identifying Evolvability-Enhancing Mutations via Computational Models. Grand Valley State University Student Scholars Day, Allendale, Michigan, United States.
Marcos Sanson (Presenter), Jack Garbus, Matthew Andres Moreno, Emily Dolson, and Alexander Lalejini. (2024, July 16). Runtime phylogenetic analysis enables extreme subsampling for test-based problems. Genetic and Evolutionary Computation Conference (GECCO), Melbourne, Australia.
Marcos Sanson (Presenter), Jack Garbus, Matthew Andres Moreno, Emily Dolson, and Alexander Lalejini. (2024, April 10). Exploiting Phylogenetic Analysis to Improve Evolutionary Search Algorithms. Grand Valley State University Student Scholars Day, Allendale, Michigan, United States.
Awards
Barry Goldwater Scholar, 2025
Air Force Research Laboratory Scholar, 2025 & 2026
Outstanding Session Presentation, Gulf Coast Undergraduate Research Symposium, 2025
Peter C. Cook Leadership Academy Fellow, 2025-2026
Ronald E. McNair Post-Baccalaureate Achievement Program Scholar, 2024-2026
Kindschi Undergraduate Research Fellowship in the Sciences, 2024, 2025
Grand Valley State University Dean's List, 2022, 2023, 2024, 2025, 2026
Hispanic Scholarship Fund Scholar, 2023, 2024, 2025