Ahmad Mousavi

Ahmad Mousavi serves as an Industrial Associate at the University of Florida's Informatics Institute and a part-time lecturer in American University's Data Science Program within the College of Arts and Sciences, teaching graduate courses including Advanced Machine Learning and Statistical Machine Learning through Fall 2025. His academic foundation spans applied mathematics with specialized expertise in computational optimization.

His educational background includes:

• Ph.D. in Applied Mathematics, University of Maryland, Baltimore County (2013-2019)
• M.S. in Applied Mathematics, Sharif University of Technology (2008-2011)
• B.S. in Applied Mathematics, University of Guilan (2004-2008)

Dr. Mousavi's research centers on machine learning and optimization theory, with significant contributions to sparse recovery algorithms, portfolio optimization, and support vector machine development. His work bridges theoretical mathematics with practical financial and data science applications, particularly in developing constrained optimization frameworks for real-world problems requiring sparsity and volatility control.

Analysis of his publication record reveals a consistent focus on optimization techniques applied to machine learning, with recurring themes in sparse modeling for financial portfolios, kernel methods for classification, and theoretical foundations of compressive sensing. His 2020 survey paper demonstrates expertise in synthesizing complex technical domains.

His professional development includes postdoctoral research at the University of Florida's Informatics Institute and the University of Minnesota's Institute for Mathematics and its Applications, where he collaborated on interdisciplinary projects connecting mathematical theory with data-intensive applications.