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About Me.

I am SeyedAbolfazl (Herlock) Rahimi, a Ph.D. student at Yale University, specializing in Optimization and Optimal Transport. My academic foundation is built on a dual Bachelor's degree in Computer Engineering and Mathematics from Sharif University of Technology, where I developed a strong grounding in both mathematical theory and computational techniques.

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My passion for mathematics began with my high school experience in the Mathematics Olympiad, particularly with a focus on geometry. This interest grew as I pursued my Bachelor’s in Computer Engineering and subsequently added a second Bachelor’s in Mathematics. My curiosity led me to explore graduate courses early on, where I became captivated by the intersection of statistics, machine learning, and modern geometry.

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At Yale, my research continues along this interdisciplinary path, where I delve into innovative approaches that bridge mathematics and machine learning. I am especially interested in advancing our understanding of optimization methods and exploring how large language models (LLMs) intersect with mathematical principles.

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Thank you for visiting my page. I am always eager to connect with others who share an enthusiasm for pushing the boundaries of Science!

Education

2023 - Ongoing

Yale University

Ph.D. Electrical Engineering

2018 - 2023

Sharif University of Technology

BS of Mathematics

During my undergraduate studies at Sharif University of Technology, I built a strong foundation in advanced mathematical concepts and theory. My coursework included rigorous studies in Differential Geometry, Differential Manifolds, Galois Theory, Information Theory, and Statistical Inference. These courses provided a deep understanding of mathematical structures and their applications, which continue to inform my research today.

2018 - 2023

Sharif University of Technology

BS of Computer Engineering

In addition to my foundation in mathematics, I pursued a Bachelor’s degree in Computer Engineering at Sharif University of Technology.

Notable courses included Reinforcement Learning, Advanced Machine Learning, and Deep Learning.

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