Kang Liu

Kang Liu

Marketing Data Science Director at American Express

About Me

Currently I am a Marketing Data Science Director at American Express Company, working on enterprise deep learning research and capability development. I obtained my Ph.D. degree from University of Michigan, Eletrical Engineering and Computer Science department, majoring in Control System, under the guidance of Professor Semyon M. Meerkov, in August 2021, and the B.S.E. degree from Harbin Institute of Technology (HIT), China, in 2016.

Research

My research interests include:

  • Natural Language Processing.
  • Stochastic System Analysis and Control.
  • Application of Probability Theory.
  • Optimization.

Dissertation

The (α, β) Precision Theory for Production System Monitoring and Improvement, defended successfully on June 16th, 2021. [Video] [PDF] Committee members: Semyon M. Meerkov, Roman Kapuscinski, S. Sandeep Pradhan, Lei Ying.

Publication

  1. K. Liu (sole author), “SetCSE: Set Operations using Contrastive Learning of Sentence Embeddings,” The 12th International Conference on Learning Representations (ICLR 2024). [OpenReview]

  2. K. Liu, N. Li, I. Kolmanvosky, A. Girard, “A Vehicle Routing Problem with Dynamic Demands and Restricted Failures Solved Using Stochastic Predictive Control,” Proceedings of the 2019-Acc. [IEEE]

  3. P. Alavian, Y. Eun, K. Liu (main), S. M. Meerkov and L. Zhang (alphabetical order), “The (α, β)-Precise Estimates of MTBF and MTTR: Definitions, Calculations, and Induced Effect on Machine Efficiency Evaluation,” Proceeding of the Manufacturing Modeling, Management and Control - 9th MIM 2019, Berlin, Germany. [ScienceDirect]

  4. P. Alavian, Y. Eun, K. Liu (main), S. M. Meerkov and L. Zhang (alphabetical order), “The (α, β)-Precise Estimates of MTBF and MTTR: Definition, Calculation, and Observation Time,” IEEE Transactions on Automation Science and Engineering, 2020. [IEEE]

  5. P. Alavian, Y. Eun, K. Liu (main), S. M. Meerkov and L. Zhang (alphabetical order), “The (αX, βX)-Precise Estimates of Production Systems Performance Metrics,” International Journal of Production Research, 2022. [Taylor & Francis Online]

  6. Y. Eun, K. Liu (main) and S. M. Meerkov (alphabetical order), “Production Systems with Cycle Overrun: Modeling, Analysis, Improvability, and Bottlenecks,” International Journal of Production Research, 2022. [Taylor & Francis Online]