Zhikun

I am Zhikun Lu (卢智坤), an assistant professor of operations and business analytics at NYU Shanghai. I obtained my PhD in Business from the Goizueta Business School at Emory Univeristy, advised by Ruomeng Cui and Tianshu Sun. My research focuses on causality-driven decision making with applications in online retail, supply chains, and digital entertainment, utilizing techniques from causal inference, machine learning, and optimization. I have collaborated with leading tech companies, including Alibaba, Amazon, DiDi, and Tencent, developing causal-machine-learning-based optimization models to improve various business outcomes, such as sales, customer lifetime value, and user engagement. I am passionate about discovering innovative ideas, mastering new skills, and applying them to address real-world business challenges.

Contact: zhikun.lu[at]nyu.edu
Google Scholar	SSRN Author Page	NYU Profile (中文)	Curriculum Vitae

Publication

1. "The Value of Last-mile Delivery in Online Retail" with Ruomeng Cui, Tianshu Sun, and Lixia Wu, forthcoming at Management Science (a top business journal, UTD24, FT50)
– Second Place, 2025 INFORMS Service Science Best Student Paper Award
– Runner-up, 2025 POMS College of Service Operations Best Student Paper Competition
– Runner-up, 2024 POMS College of Supply Chain Management Best Student Paper Competition

2. "Sooner or Later? Promising Delivery Speed in Online Retail," with Ruomeng Cui, Tianshu Sun, and Joe Golden, 2024, Manufacturing & Service Operations Management (a top business journal, UTD24, FT50)
– Finalist, 2023 MSOM Student Paper Competition
– Winner, 2021 ICIS Best Paper in Track Award (Digital and Mobile Commerce)

Work in Progress

3. "Incentives in Online Gaming: Optimal Policy Design with Dynamic Causal Machine Learning" with Ruomeng Cui and Yang Su
– INFORMS Annual Meeting (2024); POMS Hong Kong (2026)
– Seminar @ CityU, PolyU, HKU (2025)

4. "Food Delivery Platform Expansion Strategies: A Structural Approach" with Ruomeng Cui and Wenchang Zhang
– POMS Annual Meeting, Atlanta, GA (2025)

Pre-Doctoral Research

1. "How Contagious Was the Panic of 1907? New Evidence from Trust Company Stocks," with Caroline Fohlin, 2021, AEA Papers and Proceedings

2. "Short Sale Bans May Improve Market Quality During Crises: New Evidence from the 2020 Covid Crash" with Caroline Fohlin and Nan Zhou
– See VoxEU Column for a non-technical summary

3. "Preferential Credit Policy with Sectoral Markup Heterogeneity" with Kaiji Chen, Yuxuan Huang, Xuewen Liu, and Yong Wang, Revise & Resubmit

4. "A Model of China’s Economic Vertical Structure" with Xi Li, Xuewen Liu, and Yong Wang, Revise & Resubmit

Teaching

Applied Machine Learning for Business, Undergraduate, Spring 2026, New York University, Shanghai

This course equips students with practical ML/AI skills for careers at the forefront of data-driven innovation—whether as data scientists developing advanced models or as business leaders collaborating with data science teams. It focuses on two complementary areas: 1) predictive ML to forecast business outcomes, and 2) causal ML to design business interventions. Through hands-on Python projects, students learn to bridge cutting-edge methods with real-world decision-making, tackle complex problems, and create business value. Prior exposure to ML is recommended but not required.

Causal Inference for Business (in the Age of AI), MSBA, Fall 2026, New York University, Shanghai

This course teaches students how causal inference and modern causal ML/AI can power real-world business decisions. Students learn the full pipeline from program evaluation to policy optimization, with an emphasis on business impact and ROI. Topics include experiments and quasi-experiments, uplift modeling and causal ML, and AI-based policy design. Through hands-on Python projects, students develop the ability to communicate causal insights to business stakeholders and build scalable, evidence-based decision systems. Prior exposure to ML or statistics is recommended but not required.