Assistant Professor |
2145 Sheridan Road, Evanston, IL 60208-3109
Email: chang-han.rhee@northwestern.edu
http://chrhee.github.io/
Rare Event Analysis, Large Deviations, Metastability, Heavy Tails
Debiased Multilevel Monte Carlo
Markov chain Monte Carlo, Exact Estimation
Sensitivity Analysis, Gradient Estimation
Experimental Design
Machine Learning Theory
Although rare, rare events matter. For example, rare catastrophic events — such as large scale black out, market crash, and earthquake — have major impacts on society. Moreover, the need for understanding and controling (less dramatic) rare events frequently arises in many problems in statistics, science, and engineering. The theory of large deviations has a long and successful history in providing systematic tools for understanding rare events. (Varadhan won Abel Prize in 2007 for his contributions to the large deviations theory!) However, the classical large deviations theory often falls short when the underlying uncertainties are heavy-tailed. This is a serious problem, since heavy tails are observed in many man-made systems (e.g., degree distribution in social network, financial loss, CPU requirement and file size in computer systems, size of power outage) as well as natural phenomena (e.g., earthquake, flood). Moreover, in many applications it has been observed that there is a structural difference in the way system-wide rare events arise when the underlying uncertainties are heavy-tailed. Roughly speaking, in light-tailed settings, the system-wide rare events arise because everything goes wrong a little bit (conspiracy principle), whereas in heavy-tailed settings, the system-wide rare events arise because only a few things go wrong, but they go terribly wrong (catastrophe principle). Due to such a fundamental difference, as well as the ubiquitous presence of the heavy-tailed distributions in modern engineering systems, a comprehensive theory of large deviations for heavy-tailed rare events has long been called for. While the extreme value theory community has made impressive progress in understanding the catastrophe principle over the last couple of decades, most of the literature has been focused on model-specific results or results pertaining to rare events that are caused by a single extreme behavior. My recent papers [1, 2, 3] establish heavy-tailed large deviations theory and rigorously characterize the catastrophe principle for fundamental stochastic processes such as Levy processes and random walks with heavy-tailed increments; in particualr, our theory fully characterizes the heavy-tailed rare events that require more than one single extreme behavior. The new theory facilitates understanding of a wide range of rare events that arise in applications. In particular, we were able to solve long standing open problems in simulation and queueing theory — i.e., designing the universal and strongly efficient rare-event simulation algorithm [3] and identifying the queue length asymptotics of multiple server queue with heavy-tailed service times [2].
Ph.D., Computational and Mathematical Engineering, Stanford University
M.S., Computational and Mathematical Engineering, Stanford University
B.S., Mathematics and Computer Science, Seoul National University
NSF CAREER Award, 2022
INFORMS Simulation Society Outstanding Simulation Publication Award, 2016
George Nicholson Student Paper Competition Finalist, 2013
Best Student Paper Award (MS/OR focused), Winter Simulation Conference, 2012
Samsung Fellowship, 2008–2012
Jeffrey Wang (PhD at Northwestern IEMS)
Xingyu Wang (PhD at Northwetern IEMS)
Zhe Su (PhD at Northwestern IEMS)
Jingyi Zhao (MS at Northwestern ESAM): graduated in March 2020
Mihail Bazhba (PhD at CWI Stochastics): co-supervised with Bert Zwart; scheduled to defend in May 2021
Bohan Chen (PhD at CWI Stochastics): co-supervised with Bert Zwart; defended in December 2019
with P. Glynn
To appear in Mathematics of Operations Research
with M. Bazhba, B. Zwart
Special issue of Queueing Systems in honor of Masakiyo Miyazawa, 102: 25–52, (2022)
with M. Bazhba, J. Blanchet, and B. Zwart
Annals of Applied Probability, 30(6): 2695–2739, (2020)
with J. Blanchet and B. Zwart
Annals of Probability, 47(6): 3551-3605, (2019)
with B. Chen, J. Blanchet, and B. Zwart
Mathematics of Operations Research, 44(3): 919-942, (2019)
with M. Bazhba, J. Blanchet, and B. Zwart
Queueing Systems, 93(3–4): 195-226, (2019)
with B. Chen and B. Zwart
Advances in Applied Probability, 50(3): 805-832. (2018)
with P. Glynn
Operations Research, 63(5): 1026–1043. (2015)
2016 INFORMS Simulation Society Outstanding Simulation Publication Award.
with P. Glynn
Journal of Applied Probability (Special Jubilee Issue), 51A:377-389, (2014)
with B. Chen and B. Zwart
arXiv:2010.10751
with E. Zhou and P. Qiu.
arXiv:1710.11616
with M. Bazhba, J. Blanchet, B. Zwart
arXiv:2003.14381
with X. Wang
2023 Winter Simulation Conference, (2023)
with X. Wang and S. Oh
International Conference on Learning Representations, (2022)
with X. Wang
2020 Winter Simulation Conference, (2020)
with E. Zhou and P. Qiu
2014 Winter Simulation Conference, (2014)
with P. Glynn
2012 Winter Simulation Conference, (2012)
Best Student Paper Award (MS/OR focused)
with X. Wang
with Z. Su
with Z. Su
with X. Wang
with J. Wang.
with N. Vasmel, and B. Zwart
with B. Zwart and J. Blanchet
with P. Glynn