Chihoon Lee (clee4)

Chihoon Lee

Professor and Associate Dean of Graduate Studies

School of Business

Babbio Center 426
(201) 216-5385

Education

  • PhD (2008) University of North Carolina at Chapel Hill (Statistics)
  • BS (2003) Seoul National University (Statistics and Mathematics)

Research

Application Areas:
Operations Management: Large-scale Service Operations
Stochastic Networks: Fluid & Diffusion Analysis
Infectious Disease Modeling & Control
Financial Engineering

Methodological Areas:
Statistics for Stochastic Processes
Applied Probability, Queueing Theory
Stochastic Control, Simulation
Computational Statistics, Statistical Learning

General Information

Dr. Lee’s research lies within the disciplines of applied probability (service operations, stochastic networks, stochastic control) and statistics (sequential learning, inference for stochastic processes), but also crosses the boundaries between the two toward developing analytical tools that will facilitate the use of the extensive operational data available nowadays to improve large-scale service operations, production systems and retailers. He has collaborated actively with researchers in several other disciplines of engineering (communication networks, financial engineering), biology (population dynamics) and epidemiology (infectious disease control).

Experience

Stevens Institute of Technology, Associate Dean of Graduate Studies, School of Business, 2020-Present

Stevens Institute of Technology, Professor, School of Business, 2023-Present

Stevens Institute of Technology, Associate Professor, School of Business, 2015-2023

Colorado State University, Associate Professor, Department of Statistics, 2014-2015

Colorado State University, Assistant Professor, Department of Statistics, 2008-2014

Institutional Service

  • Graduate Curriculum Committee Member
  • School of Business Gradudate Marketing Committee Chair
  • Search Committee for the Dean of College of Online and Professional Education Member
  • Nominating Committee Member
  • Search Committee for Associate Provost Member
  • Business Statistics Faculty Search Committee Chair
  • Financial Engineering and Financial Analytics Programs Committee Member
  • Business Intelligence and Analytics Programs Committee Member
  • Institute Curriculum Committee Member
  • Graduate Curriculum Committee Member
  • o Organizing Committee for the International Conference on High Frequency Finance and Data Analytics (HF2019) Member
  • Financial Engineering PhD Programs Committee Member

Professional Service

  • External evaluator on a CRT (Collaborative Research Team) proposal
  • Associate Editor of Journal of Korean Statistical Society

Honors and Awards

2022 EURO Best Paper Award in Innovative Applications of Operations Research, from the Association of European Operational Research Societies

Visiting Scholar at Institute for Data and Decision Analytics (iDDA), Chinese University of Hong Kong, Shenzhen, China, July 16-August 7, 2018

New Researcher Fellowship, Statistical and Applied Mathematical Sciences Institute (SAMSI), NC: Involved with working group on Infectious Diseases Modeling, 2014-2015

Short-term Visiting Fellow, National Institute for Mathematical and Biological Synthesis (NIMBioS), TN, 2014

Early Career Statistician Award, Korean Statistical Society, Seoul, South Korea: Awarded to a single researcher in the Society within 5 years of receiving Ph.D., 2012

Excellence in Teaching Award, Department of Statistics and Operations Research, University of North Carolina at Chapel Hill: For excellence in teaching undergraduates, 2007

Wassily Hoeffding Award, Department of Statistics and Operations Research, University of North Carolina at Chapel Hill: For the best performance in the first year of Ph.D. program, 2004

Professional Societies

  • INFORMS Member
  • IMS – Institute of Mathematical Statistics Member

Grants, Contracts and Funds

Fast Quantum Methods for Financial Risk Management, CRAFT Research Project, $100,000, Co-PI. (2022-2023)

Probabilistic and statistical analysis of complex stochastic networks, Army Research Office, $122,722, Sole-PI. (2014-2017)

New Researcher Fellowship at SAMSI, National Science Foundation, $37,297, Sole-PI. (2014-2015)

Stability and control for stochastic networks with fractional Brownian motions, National Security Agency, $40,000, Sole-PI. (2012-2014)

Stationary solutions of constrained stochastic differential equations driven by fractional Brownian motions, Army Research Office, Short Term Innovative Research program, $50,000, Sole-PI. (2012-2013)

Controlled stochastic networks with fractional Brownian motions, The Simons Foundation Collaboration Grants, $7,000 (Original funding period was 5 years and terminated due to other funding), Sole-PI. (2011-2012)

Statistical analysis of complex networks: Applied probability approaches, National Security Agency, $40,000, Sole-PI. (2015-2017)

Selected Publications

Conference Proceeding

  1. Xiong, Z.; Liu, R.; Chen, Y.; Lee, C. (2023). Overcoming the Novelty Discount: The Roles of Open-source Development in the Initial Coin Offerings (ICOs). Academy of Management Proceedings.
  2. Shen, H.; Huang, J. Z.; Lee, C. (2007). Forecasting and dynamic updating of uncertain arrival rates to a call center. 2007 IEEE International Conference on Service Operations and Logistics, and Informatics. 2007 IEEE International Conference on Service Operations and Logistics, and Informatics (pp. 1--6).
  3. Chen, L.; Lee, C.; Budhiraja, A.; Mehra, R. K. (2007). PFLib: an object-oriented MATLAB toolbox for particle filtering. Signal Processing, Sensor Fusion, and Target Recognition XVI. Signal Processing, Sensor Fusion, and Target Recognition XVI (vol. 6567, pp. 65670S).
  4. Saeidi, S.; Khodadadi, J.; Johnson, C.; Lee, C.; Yang, E. (2005). Computational Modeling of a Piezoelectrically Actuated Microvalve for the Control of Liquid Flowrate. NSTI-Nanotech (vol. 3).

Journal Article

  1. Cui, Z.; Lee, C.; Zhu, L.; Zhu, Y. (2021). Non-convex isotonic regression via the Myersonian approach. Statistics & Probability Letters (vol. 179, pp. 109210). Elsevier BV.
    http://dx.doi.org/10.1016/j.spl.2021.109210.
  2. Choi, M. C.; Lee, C.; Song, J. (2021). Entropy flow and de Bruijn's identity for a class of stochastic differential equations driven by fractional Brownian motion. Probability in the Engineering and Informational Sciences (3 ed., vol. 35, pp. 369-380).
  3. Lee, C.; Liu, X.; Liu, Y.; Zhang, L. (2021). Optimal production rate for double ended queueing system under time varying demand. Stochastic Systems (vol. 11, pp. 140-173).
  4. Lee, C.; Ward, A. R.; Ye, H. (2021). Stationary Distribution Convergence of the Offered Waiting Processes in Heavy Traffic under General Patience Time Scaling. Queueing Systems: Theory and Applications (vol. 99, pp. 283–303 ).
  5. Cui, Z.; Lee, C.; Zhu, L.; Zhu, Y. (2021). On the optimal design of the randomized unbiased Monte Carlo estimators. Operations Research Letters (4 ed., vol. 49, pp. 477-484). Elsevier BV.
    http://dx.doi.org/10.1016/j.orl.2021.05.004.
  6. Chen, S.; Owolabi, Y.; Li, A.; Lo, E.; Robinson, P.; Janies, D.; Lee, C.; Dulin, M. (2020). Patch dynamics modeling framework from pathogens? perspective: Unified and standardized approach for complicated epidemic systems. PLoS One (vol. 15, pp. e0238186).
  7. Lee, C.; Ward, A. R.; Ye, H. (2020). Stationary distribution convergence of the offered waiting processes for GI/GI/1+ GI queues in heavy traffic. Queueing Systems (vol. 94, pp. 147--173).
  8. Sun, L.; Lee, C.; Hoeting, J. A. (2019). A penalized simulated maximum likelihood method to estimate parameters for SDEs with measurement error. Computational Statistics (vol. 34, pp. 847--863).
  9. Lee, C.; Ward, A. R. (2019). Pricing and capacity sizing of a service facility: Customer abandonment effects. Production and Operations Management (vol. 28, pp. 2031--2043).
  10. Creamer, G.; Lee, C. (2019). A multivariate distance nonlinear causality test based on partial distance correlation: application to energy futures via SVM. Quantitative Finance: Special Issue on AI and Machine Learning in Finance (vol. 19, pp. 1531–1542).
    https://www.tandfonline.com/doi/abs/10.1080/14697688.2019.1622300.
  11. Han, Z.; Hu, Y.; Lee, C. (2019). Optimal pricing barriers in a regime-switching regulated market. Quantitative Finance (vol. 19, pp. 491–499).
    https://www.tandfonline.com/doi/abs/10.1080/14697688.2018.1480835.
  12. Mai, F.; Tian, S.; Lee, C.; Ma, L. (2019). Deep learning models for bankruptcy prediction using textual disclosures. European Journal of Operational Research (2 ed., vol. 274, pp. 743-758).
  13. Chen, S.; Lanzas, C.; Lee, C.; Zenarosa, . L.; Arif, . A.; Dulin, M. (2019). Metapopulation Model from Pathogen’s Perspective: A Versatile Framework to Quantify Pathogen Transfer and Circulation between Environment and Hosts. Nature: Scientific Reports (pp. Article Number:1694).
    https://www.nature.com/articles/s41598-018-37938-0.
  14. Chen, S.; Lenhart, S.; Day, J.; Lee, C.; Dulin, M.; Lanzas, C. (2018). Pathogen transfer through environment-host contact: an agent-based queueing theoretic framework.. Mathematical Medicine and Biology: A Journal of the IMA (vol. 35, pp. 409–425).
    https://academic.oup.com/imammb/article/35/3/409/4585724.
  15. Cui, Z.; Lee, C.; Liu, Y. (2018). Single-transform formulas for pricing Asian options in a general approximation framework under Markov processes. European Journal of Operational Research (3 ed., vol. 266, pp. 1134-1139). Elsevier BV.
    http://dx.doi.org/10.1016/j.ejor.2017.10.049.
  16. Chen, S.; Sanderson, M. W.; Lee, C.; Cernicchiaro, N.; Renter, D. G.; Lanzas, C. (2016). Basic reproduction number and transmission dynamics of common serogroups of enterohemorrhagic Escherichia coli. Applied and Environmental Microbiology (vol. 82, pp. 5612--5620).
  17. Wei, S.; Lee, C.; Wichers, L.; Marron, J. (2016). Direction-projection-permutation for high-dimensional hypothesis tests. Journal of Computational and Graphical Statistics (vol. 25, pp. 549--569).
  18. Lee, C.; Song, J. (2016). On drift parameter estimation for reflected fractional Ornstein--Uhlenbeck processes. Stochastics (formerly Stochastics and Stochastics Reports) (vol. 88, pp. 751--778). Stochastics (formerly Stochastics and Stochastics Reports).
  19. Han, Z.; Hu, Y.; Lee, C. (2016). Optimal pricing barriers in a regulated market using reflected diffusion processes. Quantitative Finance (vol. 16, pp. 639--647). Quantitative Finance.
  20. Sun, L.; Lee, C.; Hoeting, J. A. (2015). A penalized simulated maximum likelihood approach in parameter estimation for stochastic differential equations. Computational Statistics and Data Analysis (vol. 84, pp. 54--67).
  21. Lee, C.; Puhalskii, A. A. (2015). Non-Markovian state-dependent networks in critical loading. Stochastic Models (vol. 31, pp. 43--66).
  22. Hu, Y.; Lee, C.; Lee, M. H.; Song, J. (2015). Parameter estimation for reflected Ornstein--Uhlenbeck processes with discrete observations. Statistical Inference for Stochastic Processes (vol. 18, pp. 279--291).
  23. Sun, L.; Lee, C.; Hoeting, J. A. (2015). Parameter inference and model selection in deterministic and stochastic dynamical models via approximate Bayesian computation: modeling a wildlife epidemic. Environmetrics (vol. 26, pp. 451--462).
  24. Lee, C.; Ward, A. R. (2014). Optimal pricing and capacity sizing for the GI/GI/1 queue. Operations Research Letters (vol. 42, pp. 527--531).
  25. Hu, Y.; Lee, C. (2013). Drift parameter estimation for a reflected fractional Brownian motion based on its local time. Journal of Applied Probability (vol. 50, pp. 592--597).
  26. Lee, C. (2012). Bounds on exponential moments of hitting times for reflected processes on the positive orthant. Statistics and Probability Letters (vol. 82, pp. 1120--1128).
  27. Lee, C.; Bishwal, J. P.; Lee, M. H. (2012). Sequential maximum likelihood estimation for reflected Ornstein--Uhlenbeck processes. Journal of Statistical Planning and Inference (vol. 142, pp. 1234--1242).
  28. Lee, C. (2011). A geometric drift inequality for a reflected fractional Brownian motion process on the positive orthant. Journal of Applied Probability (vol. 48, pp. 820--831).
  29. Lee, C.; Weerasinghe, A. (2011). Convergence of a queueing system in heavy traffic with general patience-time distributions. Stochastic Processes and their Applications (vol. 121, pp. 2507--2552).
  30. Budhiraja, A.; Ghosh, A. P.; Lee, C. (2011). Ergodic rate control problem for single class queueing networks. SIAM Journal on Control and Optimization (vol. 49, pp. 1570--1606).
  31. Lee, C. (2011). On moment stability properties for a class of state-dependent stochastic networks. Journal of the Korean Statistical Society (vol. 40, pp. 325--336).
  32. Lee, C. (2011). On the return time for a reflected fractional Brownian motion process on the positive orthant. Journal of Applied Probability (vol. 48, pp. 145--153).
  33. Lee, C.; Weerasinghe, A. (2011). Stationarity and control of a tandem fluid network with fractional Brownian motion input. Advances in Applied Probability (vol. 43, pp. 847--874).
  34. Lee, C.; Wang, J. C. (2011). Waiting time probabilities in the M/G/1+ M queue. Statistica Neerlandica (vol. 65, pp. 72--83).
  35. Lee, C. (2010). V-uniform ergodicity for state-dependent single class queueing networks. Queueing Systems (vol. 65, pp. 93--108).
  36. Budhiraja, A.; Lee, C. (2009). Stationary distribution convergence for generalized Jackson networks in heavy traffic. Mathematics of Operations Research (vol. 34, pp. 45--56).
  37. Budhiraja, A.; Chen, L.; Lee, C. (2007). A survey of numerical methods for nonlinear filtering problems. Physica D: Nonlinear Phenomena (vol. 230, pp. 27--36).
  38. Chen, L.; Lee, C.; Mehra, R. K. (2007). How to tell a bad filter through Monte Carlo simulations. IEEE Transactions on Automatic Control (vol. 52, pp. 1302--1307).
  39. Budhiraja, A.; Lee, C. (2007). Long time asymptotics for constrained diffusions in polyhedral domains. Stochastic Processes and Their Applications (vol. 117, pp. 1014--1036).

Courses

• BIA 654 Experimental Design
• BIA 652 Multivariate Data Analytics
• MGT 719 Research Methods
• MGT 620 Statistical Models
• FE 541 Applied Statistics with Financial Applications