A record of research, professional experience, leadership, and the disciplines that continue to shape my path.
Contact MeI am a Mechanical Engineering student at Columbia University through the Columbia–Brandeis dual-degree engineering program. I completed my B.S. in Applied Mathematics and Computer Science at Brandeis in May 2026 and expect to graduate from Columbia in May 2028. I continue to work as an academic researcher in computational fluid dynamics and computational neuroscience.
Feel free to take your time exploring the specific details behind each section.Computational fluid dynamics, vortex structures, and machine-learning-based flow analysis.
Modeling learning, habit formation, and dopamine-dependent behavioral transitions.
Differential equations, dynamical systems, optimization, and simulation-based methods.
I am involved in several cross-institutional projects that began through Yale SUMRY and continue with faculty at Yale University, Jackson State University, Texas A&M, and UT Arlington. These projects use CFD simulation data to apply machine-learning and deep-learning methods to vortex structures, flow-control behavior, and high-dimensional fluid dynamics, including time-series prediction with transformer models and 3D prediction of vortex structures.
This project is ongoing. I defended my senior honors thesis at Brandeis in May 2026, and the work continues in collaboration with Prof. Laurent Venance’s lab at Collège de France, where the experiments were conducted. The project models basal ganglia transitions from goal-directed to habitual learning using behavioral data, differential equations, and reinforcement-learning tools.
Mai Al Shaaban, Joey Takei, Annamaria Palmiero, Leya Dereje, Sam Panitch, Caixia Chen, Yong Yang, Yonghua Yan
Published in MDPI Computation
Read paperYong Yang, Caixia Chen, Yonghua Yan, Mai Al Shaaban
Published in MDPI Processes
Read paperI’m usually working on jigsaw puzzles, crocheting, or getting outside on hiking trails!
Machine-learning and scientific-computing methods for high-dimensional data, simulation outputs, and research workflows.
Abstract accepted; presenting as primary author under IEEE.
Brandeis University, Department of Mathematics
Thesis in Applied Mathematics: When Learning Becomes Automatic in Neural Dynamics, Stochastic Dynamics of Movement and Behavioral Patterns
Walter E. Washington Convention Center · Washington, DC
Poster: Estimating Flows of Partial Differential Equations using Time-Series Analysis
Massachusetts Institute of Technology · Cambridge, MA
Poster: Machine Learning-Based Vortex Evolution in 3D High-Speed Flows
University of Arkansas · Fayetteville, AR
Talk: A spatiotemporal decomposition-based prediction method for High-Speed Complex Fluid Dynamics
Ohio State University · Columbus, OH
Talk: Estimating Flows of Partial Differential Equations using Time-Series Analysis
Yale University · New Haven, CT
Talk: Estimating Flows of Partial Differential Equations using Time-Series Analysis
New York, NY
Poster: Machine Learning-Based Vortex Evolution in 3D High-Speed Flows
Brandeis University · Waltham, MA
Talk: Clifford Algebra Overview
Recognized for leadership and community service contributions through my role in the Student Union.
For outstanding leadership and service as Head Treasurer of the Brandeis Student Union.
Received competitive funding to present as the primary presenter at MIT URTC.
Gift membership awarded through the Joint Mathematics Meetings organizers.
Awarded by the Brandeis Mathematics Department for mentorship during Spring 2025.
Recognized for service and leadership in Student Union treasury work.
Scholarship supporting my education at Brandeis University.
Recognized across my studies at SIUE and Brandeis University.
Recognized twice for undergraduate research involvement through SIUE URCA.
Scholarship awarded to support refugees pursuing higher education.
A curated record of conferences, talks, and academic events