Miguel Angel Alvarez Ballesteros
Miguel Angel research focuses on Monte Carlo methods for parameter inference in partially observed stochastic systems.
Biography
Miguel Alvarez is a Ph.D. candidate in Applied Mathematics and Computational Science at King Abdullah University of Science and Technology (KAUST), advised by Professor Raúl F. Tempone and Professor Ajay Jasra (MBZUAI). His research focuses on Monte Carlo methods for parameter inference in partially observed stochastic systems. He has presented his work at international conferences including MCQMC in Linz, Austria (2022) and Waterloo, Canada (2024), ICIAM in Tokyo, Japan (2023), SIAM CSE in Amsterdam, Netherlands (2023), and MCM in Chicago, USA (2025). He was also a finalist in the Algo Trading Challenge 2025, held alongside Money 20/20 Riyadh. He received his M.S. in Applied Mathematics and Computational Science from KAUST in 2022 and his B.Sc. in Physics from the Industrial University of Santander (UIS), Colombia, in 2019.
Expertise and Interests
Miguel's research interests include Monte Carlo methods, Bayesian Statistics, Particle filtering and Machine learning.
Education
- Master of Science (M.S.)
- Applied Mathematics and Computational Science, King Abdullah University of Science and Technology (KAUST), Saudi Arabia, 2023
- Bachelor of Science (B.S.)
- Physics, Universidad Industrial de Santander (UIS), Colombia, 2019
