about me

I am an assistant professor in the Decision, Risk, and Operations division at Columbia Business School and a member of the Data Science Institute. My research interests lie at the interface of machine learning and decision-making. I develop robust and reliable methods for data-driven decision making, extending and connecting tools across ML, operations research, and causal inference. Outside of academia, I serve as a LinkedIn Scholar at LinkedIn's Trust and Responsible AI team.

I received my Ph.D. from Stanford University in 2019 and spent a year at Facebook Core Data Science as a research scientist. Here's a more formal bio in the third person.

I go by Hong; alternatively, here's a link for the correct pronunciation of my first name. My pronouns are he/him/his.

               namkoong@gsb.columbia.edu                CV


B8103: Business Analytics II (MBA and MS, Spring 2023)

B9145: Reliable Statistical Learning (PhD, Spring 2023)


Ari Boyarsky (Decision, Risk, and Operations)

Tiffany Cai (Statistics)

Ethan Che (Decision, Risk, and Operations; co-advised with Jing Dong)

Lin Fan (Postdoc at Amazon SCOT; co-advised with Garrett van Ryzin and Assaf Zeevi)

AYeong Lee (Decision, Risk, Operations)

Yuanzhe Ma (Industrial Engineering and Operations Research; co-advised with Garud Iyengar and Jay Sethuraman)

Daksh Mittal (Decision, Risk, and Operations)

Naimeng Ye (Decision, Risk, and Operations)

Yibo Zeng (Industrial Engineering and Operations Research; co-advised with Henry Lam)

Kelly Zhang (Postdoc, co-advised with Daniel Russo)

Student collaborators: Yuri Fonseca , Nicholas Galbraith, Yian Huang, Mike Li, Jiashuo Liu , Tianyu Wang , Shawn Xia, Yunbei Xu

recent talks

Analyzing the Sensitivity of Causal Findings Under Distribution Shifts

Towards Reliable Machine Learning via Distributional Robustness

Ph.D. Thesis