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Michael Park is an accomplished scientist with extensive experience in data science and theoretical physics. Currently serving as Chief Scientist at Unknot.id since January 2022, Michael specializes in sequence modeling and map-matching algorithms, employing advanced techniques such as reinforcement learning and GAN-based synthetic data generation. Prior roles include Data Scientist at Appliedinfo Partners, Inc. and FocusVision, where Michael developed custom search engines and automated speech-to-text systems respectively. Academic contributions include a Lectureship at the University of Washington, postdoctoral research at Stanford University, and a Graduate Research Fellowship at Rutgers University, focusing on quantum field theory and particle physics. Michael holds a Ph.D. in Theoretical Physics from Rutgers University and a Bachelor of Science in Computational and Applied Mathematics from Carnegie Mellon University.