Assistant Professor Michael Choi joined Yale-NUS College in July 2021. Prior to joining Yale-NUS, he was an Assistant Professor in the School of Data Science at The Chinese University of Hong Kong, Shenzhen. He received his PhD in Operations Research from Cornell University in 2017, and his undergraduate degree in Actuarial Science (First Class Honours) from The University of Hong Kong in 2013.
His research interests centre around stochastic processes and their broad applications and intersections with other fields such as data science, with a particular focus on Markov chains theory and stochastic algorithms driven by Markov chains. He has published extensively in leading journals of his area, including Transactions of the American Mathematical Society, Stochastic Processes and their Applications, Combinatorics, Probability and Computing, and Electronic Communications in Probability.
Asst Prof Choi’s research spans three closely intertwined topics. The first major direction involves developing the theory and applications of Markov chains and Markov processes. Grounded on the first direction, Asst Prof Choi is also interested in the design, analysis and application of stochastic algorithms, in particular algorithms that are widely used in Markov chain Monte Carlo (MCMC), such as Metropolis-Hastings, simulated annealing, Langevin dynamics and Hamiltonian Monte Carlo. Broadly speaking, he is enthusiastic about theoretical research in statistical physics, probability theory, stochastic optimisation and information theory, as well as more applied research in data science and machine learning.
Michael C.H. Choi. Hitting, mixing and tunneling asymptotics of Metropolis-Hastings reversiblizations in the low-temperature regime. J. Math. Anal. Appl. (2021) Volume 497 Issue 1 124853.
Michael C.H. Choi. An improved variant of simulated annealing that converges under fast cooling. Markov Process. Related Fields (2021) Issue 1.
Michael C.H. Choi, Pierre Patie. Analysis of non-reversible Markov chains via similarity orbit. Combin. Probab. Comput. (2020), Volume 29 Issue 4 pp. 508-536.
Michael C.H. Choi, Chihoon Lee and Jian Song. Entropy flow and De Bruijn’s identity for a class of stochastic differential equations driven by fractional Brownian motion. Probab. Engrg. Inform. Sci. (2021), Volume 35 Issue 3 pp. 369-380.
Michael C.H. Choi. Metropolis-Hastings reversiblizations of non-reversible Markov chains. Stochastic Process. Appl. (2020), Volume 130 Issue 2 Page 1041-1073.
Michael C.H. Choi and Lu-Jing Huang. On hitting time, mixing time and geometric interpretations of Metropolis-Hastings reversiblizations. J. Theoret. Probab. (2020), Volume 33 Issue 2 Page 1144-1163.
Michael C.H. Choi, Evelyn Li. A Hoeffding’s inequality for uniformly ergodic diffusion process. Statist. Probab. Lett. (2019), Volume 150 Page 23-28.
Michael C.H. Choi. On resistance distance of Markov chain and its sum rules. Linear Algebra Appl. (2019), Volume 571 Page 14-25.
Michael C.H. Choi, Pierre Patie. Skip-free Markov chains. Trans. Amer. Math. Soc. (2019), Volume 371 Number 10 Page 7301-7342.
Michael C.H. Choi. Velocity formulae between entropy and hitting time for Markov chains. Statist. Probab. Lett. (2018), Volume 141 Page 62-67.
Michael C.H. Choi. Hitting time and mixing time bounds of Stein’s factors. Electron. Commun. Probab. 23 (2018), paper no. 6.
Michael C.H. Choi, Pierre Patie. A sufficient condition for continuous-time finite skip-free Markov chains to have real eigenvalues. Proceedings of AMMCS-CAIMS 2015.
Michael C.H. Choi, Eric C.K. Cheung. On the expected discounted dividends in the Cramer-Lundberg model with more frequent ruin monitoring than dividend decisions. Insurance Math. Econom. (2014) Volume 59 121-132.
- Introduction to Data Science
