Yanling Chang, Ph.D.

 Y Chang headshot

Yanling Chang, Ph.D.

Associate Professor of Operations Research & Engineering Management

Office Location: Caruth 341

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Education

  • B.S., Electronic & Info Science, Peking University, China
  • M.S., Mathematics, Georgia Institute of Technology, United States
  • Ph.D., Operations Research, Georgia Institute of Technology, United States

Biography

Dr. Yanling Chang holds a PhD in Operations Research from the School of Industrial and Systems Engineering in Georgia Tech. Prior to °ÄÃÅÁùºÏ²ÊÔ¤²â, Dr. Chang was an assistant professor at Texas A&M University. Her research area is to develop dynamic decision-making models, learning algorithms, and data analytics, to tackle challenges in supply chain operations and risk management. She is a recipient of NSF CAREER award in 2023. Her work has appeared in flagship journals including Operations Research, IEEE Transactions on Automatic Control, Psychological Review, and IISE Transactions. She also won the “Best Paper Award” in the 2022 IISE Transactions, supply chain focus, and another top cited paper award on blockchain in supply chain management. Her work is supported by several NSF awards, and she also closely works with distribution industry on supply chain network design and data analytics. 

Honors & Awards

  • TEES Young Faculty Fellow Award, Texas A&M University, 2024
  • NSF CAREER Award, 2023
  • Scheduling and Logistics Best Paper, IISE Transaction, 2022
  • Scialog MZT Travel Fellowship, Research Corporation for Science Advancement, the USDA, 2021
  • A Top cited article published in 2020 of the Internal Journal of Production Research, 2020
  • Corrie and Jim Furber '64 Faculty Fellow, Texas A&M University, 2018
  • Faculty Excellence Award in Research, Texas A&M University, 2018

Research 

  • Supply Chain Risk Management and Security
  • Partially Observable Markov Decision Processes and Markov Games
  • Structural Estimation and Dynamic Discrete Choice Models
  • Mental Fatigue
     

Publications

  • Chang, Y., Garcia, A., Wang, Z., & Sun, L. (2023). Structural Estimation of Partially Observable Markov Decision Processes. IEEE Transactions on Automatic Control, vol. 68, no. 8, pp. 5135-5141, Aug. 2023, doi: 10.1109/TAC.2022.3217908
  • Wang, Z., Chang, Y., Schmeichel, B., & Garcia, A. (2022). The Effects of Mental Fatigue on Effort Allocation: Modeling and estimation. Psychological Review, 129, 1457–1485, https://doi.org/10.1037/rev0000365
  • Chang, Y., Keblis, M. F., Li, R., Iakovou, E., & White III, C. C. (2022). Misinformation and Disinformation in Modern Warfare. Operations Research, 70, 1577–1597. https://doi.org/10.1287/opre.2021.2253
  • Chang, Y., & White III, C. C. (2022). Worst-Case Analysis for a Leader-follower Partially Observable Stochastic Game. IISE Transactions, 54, 376–389. https://doi.org/10.1080/24725854.2021.1955167
  • Chang, Y., Sun, L., Keblis, M. F., & Jie, Y. (2021). Uniform-price Auctions in Staffing for Self-scheduling Service. IISE Transactions, 53, 719–734. https://doi.org/10.1080/24725854.2020.1841345