معرفی
Mohammad Javad Latifi is a Researcher at Dartmouth College's Department of Mathematics. He holds a PhD in Mathematics from the University of Arizona and focuses on Mathematical Physics, Geometry, Applied Mathematics, and Data Science. His research bridges theoretical work in quantum field theory and dynamical systems with applied areas like machine learning and numerical modeling of physical systems.
Research Highlights:
- Developed kernel smoothing techniques for sea-ice dynamics modeling.
- Advanced the theory of star transforms and V-line tomography in imaging and inverse problems.
- Contributed to tensor network approximations of Koopman operators in nonlinear dynamics.
- Collaborated on multi-level graph spanners and optimization algorithms.
Teaching: Taught undergraduate courses including Differential Equations (Math 23) and Linear Algebra (Math 22). Designed visualizations for vector calculus and ODEs, emphasizing conceptual understanding and real-world applications.
Software & Projects:
- Developed KSPoly, a Python package for smooth field approximations in polygonal geometries.
- Created a sound visualization tool exploring numerical sequences as musical patterns.
- Contributed to Lunewave's radar and autonomous vehicle software, implementing C++ algorithms for object tracking and data analysis.
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