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Tingting Zhou, Ph.D.

Visiting Professor of Management Science and Operations Management

Address: J.C. Long 332
Office Hours: M: 1:30-4:30; TR: 4:30-6:00 pm
Phone: 843.953-1468
Curriculum Vitae: Download


Ph.D., Rutgers University, May 2018

  • Area of Study: Management Science/Operations Management
  • Dissertation Topic: Inventory and Revenue Management when Demand Distribution is Unknown

Master of Quantitative Finance
, Rutgers University, May 2011

B.S ., Nankai University, Tianjin, China, August 2009

  • Area of Study: Mathematics and Applied Mathematics

Research Interests

  • Stochastic Inventory Control
  • Dynamic Pricing
  • Machine Learning
  • Data Analytics

Courses Taught

  • Decision Science
  • Production and Operations Management

Honors and Awards

  • Recipient, Dean’s fund for summer research, 2014
  • Recipient, Master of Quantitative Finance Fellowship from Rutgers Business School, 2010

CERTIFICATES:Completed the Chartered Financial Analyst (CFA), level I, 2011


[1] Xia, Y., J. Yang and T. Zhou (2016). “Revenue Management under Ran- domly Evolving Economic Conditions,” Naval Research Logistics, forth- coming.

It considers a dynamic pricing model in which the rate of demand ar- rival process is dependent on price charged and the present state of the world. The value of being  better informed on  the state of  the world is established. When reasonable conditions are met, better economic conditions are shown to lead to higher prices.


[1] Katehakis, M.N., J. Yang and T. Zhou (2017). “Dynamic Inventory and Price Controls Involving Unknown Demand on Discrete Nonperishable Items”, under review at Mathematics of Operations Research.

It studies adaptive policies that combat unknown demand in a dynamic inventory and price control setting. Inventory control is achieved by tar- geting newsvendor ordering quantities that correspond to empirical de- mand distributions learned over time. On top of that, demand-affecting prices are selected in order to balance between exploration and exploita- tion. Bounds for the absolute regret in inventory control over a T -period horizon can range from constants to the orders of T 1/2. When pricing is involved, we devise LwD(µ) policies that facilitate learning while doing in the joint inventory-price control framework, and make methodolog- ical innovations to evaluate the policies’ performances. Regret bounds between the orders of T 3/4 and those of T 5/6 are established.


[1] “Learning and Forgetting in Robotic Total Knee Surgery" (2017) .

Using data collected from an orthopedic surgeon, we study the impact of forgetting, measured by the time gaps between repeated surgeries, on the duration of surgery which is commonly used as a measure of both productivity and health outcomes.


[1]      “Inventory Management with Unknown Demand Patterns”, Fall 2014 Con- ference of INFORMS, San Francisco, California

[2]      ”Dynamic Inventory and Price Control In the Face Of Unknown Demand”, Fall 2016 Conference of INFORMS, Nashville, Tennessee