Yuan-Mao Kao
Asst Professor
Zicklin School of Business
Department: N. P. Loomba Dept of Mgt
Areas of expertise:
Email Address: yuan-mao.kao@baruch.cuny.edu
> View CV- Biography
- Teaching
- Research and Creative Activity
- Grants
- Honors and Awards
- Service
Education
Ph.D., Operations Management, Duke University Durham NC
M.S., Industrial Engineering, National Taiwan University Taipei Taiwan
BBA, Business Administration, National Taiwan University Taipei Taiwan
| Semester | Course Prefix | Course Number | Course Name |
|---|---|---|---|
| Fall 2025 | ODA | 75100 | Stochastic Modeling: Fndmntls |
| Fall 2025 | BUS | 89500 | Independent Study |
| Spring 2025 | QNT | 2020 | Foundations of Predictive Anal |
| Spring 2025 | QNT | 2020 | Foundations of Predictive Anal |
| Spring 2025 | BUS | 89500 | Independent Study |
| Fall 2024 | ODA | 75200 | Stochastic Optmztn: Dyn Modls |
| Spring 2024 | QNT | 2020 | Foundations of Predictive Anal |
| Fall 2023 | OPM | 3000 | Service Operations Management |
| Spring 2023 | OPM | 3000 | Service Operations Management |
| Fall 2022 | OPM | 3000 | Service Operations Management |
| Fall 2022 | ODA | 75200 | Stochastic Optmztn: Dyn Modls |
| Spring 2022 | OPM | 3000 | Service Operations Management |
| Spring 2022 | OPM | 3000 | Service Operations Management |
| Fall 2021 | OPM | 3000 | Service Operations Management |
Journal Articles
(2026). Price Competition in Global Operations: Considering the Effect of Parallel Trade. Annals of Operations Research, 356. 949-983.
Kao, Y., Keskin, N., & Shang, K. (2022). Impact of Information Asymmetry and Limited Production Capacity on Business Interruption Insurance. Management Science, 68(4). 2377-3174.
Presentations
Kao, Y., Keskin, N., & Shang, K. (2026, May 19). Bayesian Dynamic Pricing and Subscription Period Selection with Unknown Customer Utility. POMS 2026 Conference. Reno, NV: Production and Operations Management Society.
Elver, I. E., & Kao, Y. (2026, May 19). Revenue Allocation with Promotions on Streaming Platforms. POMS 2026 Conference. Reno, NV: Production and Operations Management Society.
Kao, Y., & Lin, C. (2026, May 19). Dynamic Control of Add-On Services in Queueing Systems. POMS 2026 Conference. Reno, NV: Production and Operations Management Society.
Asadpour Rahimabadi, A., Kao, Y., & Zhuang, Y. (2026, May 19). Dynamic Pricing of Split Stays. POMS 2026 Conference. Reno, NV: Production and Operations Management Society.
Asadpour Rahimabadi, A., Kao, Y., & Zhuang, Y. (2026, October 19). Dynamic Pricing of Split Stays. INFORMS Annual Meeting. Atlanta, GA: INFORMS.
Asadpour Rahimabadi, A., Kao, Y., & Zhuang, Y. (2026, July 19). Dynamic Pricing of Split Stays. INFORMS Revenue Management and Pricing Section Conference. New York, NY: INFORMS.
Asadpour Rahimabadi, A., Kao, Y., & Zhuang, Y. (2026, May 19). Dynamic Pricing of Split Stays. POMS Annual Conference. Atlanta, GA: Production and Operations Management Society.
Kao, Y., & Lin, C. (2026, October 19). Dynamic Control of Add-On Services in Queueing Systems. INFORMS Annual Meeting. Atlanta, GA: INFORMS.
Asadpour Rahimabadi, A., Kao, Y., & Zhuang, Y. (2026, October 19). Dynamic Pricing of Split Stays. INFORMS Annual Meeting. Seattle, WA: INFORMS.
Kao, Y., Keskin, N., & Shang, K. (2026, October 19). Bayesian Dynamic Pricing and Subscription Period Selection with Unknown Customer Utility. INFORMS Annual Meeting. Seattle, WA: INFORMS.
Kao, Y., Keskin, N., & Shang, K. (2026, October 19). Bayesian Dynamic Pricing and Subscription Period Selection with Unknown Customer Utility. INFORMS Annual Meeting. Phoenix, AZ: INFORMS.
Kao, Y., Keskin, N., & Shang, K. (2026, October 19). Bayesian Dynamic Pricing and Subscription Period Selection with Unknown Customer Utility. INFORMS Annual Meeting. Indianapolis, IN: INFORMS.
Kao, Y., Keskin, B., & Shang, K. (2021, June 30). Bayesian Dynamic Pricing and Subscription Period Selection with Unknown Customer Utility. MSOM Virtual Conference.
Kao, Y., Keskin, N., & Shang, K. (2026, October 19). Bayesian Dynamic Pricing and Subscription Period Selection with Unknown Customer Utility. INFORMS Annual Meeting. Anaheim, CA: INFORMS.
Kao, Y., Keskin, B., & Shang, K. (2020, November 30). Bayesian Dynamic Pricing and Subscription Period Selection with Unknown Customer Utility. Virtual INFORMS Annual Meeting.
Kao, Y., Keskin, B., & Shang, K. (2020, November 30). Impact of Information Asymmetry and Limited Production Capacity on Business Interruption Insurance. Virtual INFORMS Annual Meeting.
Kao, Y., Keskin, B., & Shang, K. (2019, May 31). Design and Pricing of Subscription Services under Model Uncertainty. POMS Conference. Washington, District of Columbia
Kao, Y., Keskin, B., & Shang, K. (2019, October 31). Bayesian Dynamic Pricing and Subscription Period Selection with Unknown Customer Utility. INFORMS Annual Meeting. Seattle, Washington
Kao, Y., Keskin, B., & Shang, K. (2019, June 30). Bayesian Dynamic Pricing and Subscription Period Selection with Unknown Customer Utility. MSOM Conference. National University of Singapore, Singapore
Kao, Y., Keskin, B., & Shang, K. (2018, November 30). Dynamic Pricing-and-Learning Strategies in Service Operations. INFORMS Annual Meeting. Phoenix, Arizona
Kao, Y., Keskin, B., & Shang, K. (2018, November 30). Impact of Information Asymmetry and Limited Production Capacity on Business Interruption Insurance. INFORMS Annual Meeting. Phoenix, Arizona
Kao, Y., Keskin, B., & Shang, K. (2018, July 31). Impact of Information Asymmetry and Capacity Constraints on Business Interruption Insurance. MSOM iFORM SIG Conference. Dallas, Texas
Kao, Y., Keskin, B., & Shang, K. (2017, October 31). The Impact of Demand Uncertainty on Business Interruption Insurance. INFORMS Annual Meeting. Houston, Texas
Research Currently in Progess
Kao, Y.(n.d.). Personalized Dynamic Pricing of Subscription Services with Learning Endogenous Customer Features. In Progress.
Lin, C., & Kao, Y.(n.d.). Dynamic Control of Add-On Services in Queueing Systems. In Progress.
Kao, Y., Keskin, N., & Shang, K.(n.d.). Bayesian Dynamic Pricing and Subscription Period Selection With Unknown Customer Utility.
We consider a service provider that offers customers a subscription plan, specified by a price and a subscription period, over a planning horizon. The customers decide whether to subscribe according to a utility model. The provider has a prior belief about the customer utility model and updates its belief based on the transaction data of new customers and the usage data of existing subscribers. The provider aims to minimize its regret---the expected profit loss relative to a clairvoyant who knows the customer utility model. To analyze regret, we first study the clairvoyant's full-information problem, noting that the resulting dynamic program suffers from the curse of dimensionality. We characterize the optimal policy for the full-information problem via a customer-centric approach that balances the provider's immediate and future profits from a customer. When the provider does not have full information, we find that the simple and commonly used certainty-equivalence policy exhibits poor performance. We illustrate that this can be caused by incomplete or slow learning but can also occur because of offering a suboptimal contract with a long subscription period. We develop an adaptive learning policy, namely the information-threshold policy, that focuses on learning until the provider's accumulated information exceeds a chosen threshold. We show that this policy achieves asymptotically optimal performance with its regret growing logarithmically in the planning horizon. Our results indicate that offering a long subscription period could be costly when the provider knows little about customers' usage preferences and the service cost is uncertain. We also find that learning from transaction and usage data could be complements: learning from usage data can alleviate the profit loss when the provider aims to accelerate learning from transaction data.
Asadpour Rahimabadi, A., Kao, Y., & Zhuang, Y.(n.d.). Dynamic Pricing of Split Stays. In Progress.
We study dynamic pricing for split stays, in which a multi-night stay can be partitioned across two hotels. The seller can adjust the prices for both single-stay and split-stay products in each period. Customers choose products via a linear random-utility model. We formulate the problem as a finite-horizon dynamic program and derive the optimal pricing policy, which depends on the time and the available inventory. We find that split stays are used to balance inventory when the room availability is imbalanced across hotels on different nights, and they are not sold under balanced inventory. The split-stay offering strategy also depends on time. Early in the selling horizon, the seller anticipates sufficient demand and conserves inventory for high-value bookings. Near the end, the seller becomes myopic, favoring high-quality products for immediate revenue. Therefore, the mid-horizon becomes the optimal window for offering split stays, without a strong expectation of large demand and immediate revenue pressure. We show that split stays are not merely an additional option, but an effective tool for balancing inventory.
Asadpour Rahimabadi, A., Kao, Y., & Yi, Z.(n.d.). Pricing and Allocation of Presale Vouchers. In Progress.
Elver, I. E., & Kao, Y.(n.d.). Revenue Allocation with Promotions on Streaming Platforms. In Progress.
| Title | Funding Agency Sponsor | Start Date | End Date | Awarded Date | Total Funding | Status |
|---|---|---|---|---|---|---|
| Personalized Dynamic Pricing of Subscription Services with Learning Endogenous Customer Features | PSC CUNY 53 | 07/01/2022 | 12/31/2023 | 04/15/2022 | 3500 | Funded - In Progress |
| Honor / Award | Organization Sponsor | Date Received | Description |
|---|---|---|---|
| Doctoral Student Fellowship | Duke University | 2016 | |
| Honorable Member | Phi Tau Phi Scholastic Honor Society of Taiwan | 2015 |
College
| Committee Name | Position Role | Start Date | End Date |
|---|---|---|---|
| PhD 1st-Year Exam Committee | Committee Member | Present | |
| PhD 2nd-Year Exam Committee | Committee Member | Present | |
| Omega Seminars | Organizer | Present | |
| Department | Secretary | Present | |
| PhD Admission Committee | Committee Member | Present |
Professional
| Organization | Position Role | Organization State | Organization Country | Start Date | End Date | Audience |
|---|---|---|---|---|---|---|
| Production and Operations Management | Reviewer, Journal Article | 1/1/2021 | Present | |||
| Management Science | Reviewer, Journal Article | 1/1/2020 | Present | |||
| Operations Research | Reviewer, Journal Article | 9/5/2024 | Present | International | ||
| MSOM Conference | Reviewer, Conference Paper | Singapore | United States | 3/13/2026 | Present | International |
| Decision Sciences | Reviewer, Journal Article | United States | 2/24/2026 | Present | International | |
| Judge for MSOM Student Paper Competition | Member | 7/1/2023 | Present | International | ||
| NYC Operations Day Organizing committee | Committee Member | New York | United States | 4/8/2025 | Present | Regional |
| Journal of Economic Dynamics and Control | Reviewer, Journal Article | Netherlands | 1/9/2025 | Present | International | |
| European Journal of Operational Research | Reviewer, Journal Article | Netherlands | 12/8/2025 | Present | International | |
| Journal of Revenue and Pricing Management | Reviewer, Journal Article | Germany | 2/2/2026 | Present | International | |
| Humanities and Social Sciences Communications | Reviewer, Journal Article | Germany | 2/18/2026 | Present | International | |
| ACM Conference on Economics and Computation (EC) | Reviewer, Conference Paper | 1/1/2021 | 12/31/2021 | |||
| IMFORMS Annual Meeting | Session Chair | 1/1/2018 | 10/27/2021 | International | ||
| MSOM Conference | Session Chair | Singapore | 6/1/2019 | 6/30/2019 |