Congratulations to Guillaume Fevrier and Francesco Insulla, Vanguard Scholars for the 2025-26 academic year!
The Vanguard Scholars program, established in 2021 in memory of Annie J. Easley, supports and promotes rising stars in the field of computer science, data science, and computational mathematics. Easley (1933 – 2011) was a computer scientist, mathematician, and rocket scientist at the NASA Lewis Research Center. She was a leading member of the team that developed software for the Centaur rocket stage. ICME established the scholarship with the generous support of Vanguard Fintech Ventures.
Guillaume Fevrier is a master's student in Computational and Mathematical Engineering at Stanford University, and previously earned his engineering degree in Applied Mathematics from École Polytechnique in France. His academic background spans optimization, statistics, algorithms, stochastic methods, and quantum computing, with particular interest in the mathematical structures underlying decision-making and learning.
Guillaume has pursued research in interpretable optimization, adaptive experimentation, and quantum systems. At ICME, he contributed to a project applying contextual bandits to charitable giving, demonstrating how data-driven experimentation can improve outcomes. He is currently studying optimization for qubit initialization in neutral-atom quantum computing, a field connecting computational mathematics with quantum technologies.
Guillaume has been involved in teaching and mentorship. He has tutored students in mathematics, volunteered with charitable organizations, and was recognized by École Polytechnique for his contributions to campus life and community engagement.
Guillaume plans to pursue a PhD and an academic career centered on optimization, learning, and data-driven decision-making. He aims to develop transparent, equitable, and efficient systems that advance scientific understanding and contribute to societal progress.
Francesco Insulla earned his bachelor's degree in Physics with a concentration in Mathematical Physics, as well as an Honors degree in Mathematical and Computational Science, from Stanford University. He is currently pursuing his master's in Computational and Mathematical Engineering, continuing his work in mathematics, physics, and computation. His research experience ranges from condensed-matter physics for dark-matter detection to optimization, sampling, and the foundations of machine learning.
Before returning to Stanford for graduate study, Francesco worked as a quantitative researcher at Goldman Sachs, where he developed systematic investment strategies using statistics and machine learning. This experience shaped his interest in the statistical and computational trade-offs involved in efficient learning.
At ICME he studies how data structure influences the performance of modern inference methods and how algorithms can exploit these properties. He is developing algorithms for sparse regression with hierarchical interactions to improve sample efficiency and interpretability in high-dimensional settings.
Francesco is also active in teaching and mentorship. He has served as a teaching assistant in several courses, mentored students through the Summer Science Program, interviewed prospective undergraduates for Stanford, and contributed to community-building within the Stanford Italian Society.
Francesco plans to pursue a doctoral degree and contribute to the study of efficient learning and sampling methods, uniting theory, computation, and real-world impact.
