Physics PhD
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I provide mathematical modeling and analytical analysis for problems in physics, computer science and finance. I can derive models, compute analytical approximations, and design simulations to test theoretical predictions. Expert validation.
I develop machine learning models for scientific data analysis, pattern recognition, physical systems modeling, and financial prediction, combining statistical learning with domain knowledge in physics. Expert validation.
Dr. Quezada studied Physics and Mathematics at the Faculty of Sciences of the National Autonomous University of Mexico (UNAM) from 2007 to 2015. He continued in the Graduate Program in Physical Sciences at UNAM, where he obtained his M.Sc. (2012–2014) and later his Ph.D. (2014–2018). He also holds a second Master’s degree in Computer Science from the Center for Computing Research of the National Polytechnic Institute (IPN) (2020–2022). He carried out a one-year postdoctoral fellowship at the Center for Complexity Sciences at UNAM in 2019, followed by a two-year postdoctoral appointment at the Center for Innovation and Technological Development in Computing at IPN (2020–2022). From 2011 to 2022, he served as a teaching assistant and adjunct lecturer at the Faculty of Sciences at UNAM, where he taught courses including Introduction to Quantum Computing, Quantum Mechanics, and Contemporary Physics. In January 2022, he co-organized the Winter School on Quantum Computing at the Center for Advanced Computing Studies at UNAM. From 2022 to 2025, he worked as a research assistant at the Quantum Research Center of Huzhou University in the People’s Republic of China. He is currently a visiting professor at the Center for Computing Research of IPN. His research interests include quantum information and quantum computing, quantum game theory, quantum optics, mathematical physics and machine learning.