Samuel Imose is a computer science student based in Atlanta with nine years of hands-on experience exploring software and algorithmic solutions at the intersection of technology and finance. He is currently researching the efficacy of reinforcement learning algorithms in simulated trading environments, combining practical coding skills with quantitative experimentation. Comfortable across core CS fundamentals, Samuel aims to bring ML-driven approaches to financial systems and trading strategy design. His background suggests a readiness to contribute to fintech or research teams that value experimentation, simulation, and the translation of academic techniques into practical trading tools.
9 years of coding experience
Bachelor of Science - BS, Computer Science, Bachelor of Science - BS, Computer Science at University of Oklahoma – Gallogly College of Engineering
Contributions:17 pushes, 1 branch in 1 year 4 months
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