Montpellier Business School

CHEN Yi Ting

CHEN Yi Ting
Position Assistant Professor
Academic department Operations, Information, & Decisions
Contact yt.chen@montpellier-bs.com
Short Bio

Dr Claire Y.T. Chen is currently an Associate Professor of Computer Science in the Department of Operations, Information and Decisions at MBS, France. She received her BSc., MSc. and Ph.D. degrees in Computer Science from National Chiao Tung University, Taiwan. Prior to joining MBS, Dr Chen held visiting professorships at KEDGE Business School, France, and the School of Business Informatics and Mathematics at the University of Mannheim, Germany. With a dynamic background, she is co-founder of three AI companies in Asia and Europe. Dr Chen also holds adjunct professorships at Texas Tech University, USA, and National Cheng Kung University, Taiwan, and has a partnership with the Karlsruhe Institute of Technology (KIT) in Germany. Her research expertise lies in the innovative integration of Internet of Things (IoT) and artificial intelligence to optimise operations, with notable applications in smart cities, transportation, energy, telecommunications and agriculture. Her high-impact contributions have been published in prestigious journals such as the European Journal of Operations Research, IEEE Internet of Things Journal, International Journal of Production Economics, Annals of Operational Research, Computational Economics, and Studies in Nonlinear Dynamics and Econometrics.

Selected intellectual contributions

POSEDEL ŠIMOVIĆ, P., Y.-T. CHEN, E. W. SUN, "Classifying the variety of customers online engagement for churn prediction with a mixed-penalty logistic regression.", Computational Economics, January 2023, vol. 61, no. January 2023, pp. 451-485

LAI, W., Y.-T. CHEN, E. W. SUN, "Risk Factor Extraction with Quantile Regression Method.", Annals of Operations Research, September 2022, vol. 316, no. 2, pp. 1543-1572

CHEN, Y.-T., E. W. SUN, M. F. CHANG, Y. B. LIN, "Pragmatic real-time logistics management with traffic IoT infrastructure: Big data predictive analytics of freight travel time for Logistics 4.0.", International Journal of Production Economics, August 2021, vol. 238, no. August 2021, pp. 108157

LAI, W., Y.-T. CHEN, E. W. SUN, "Comonotonicity and low volatility effect.", Annals of Operations Research, April 2021, vol. 299, no. 1/2, pp. 1057-1099

CHEN, Y.-T., E. W. SUN, Y. B. LIN, "Machine learning with parallel neural networks for analyzing and forecasting electricity demand.", Computational Economics, October 2020, vol. 56, no. 2, pp. 569-597

CHEN, Y.-T., E. W. SUN, Y. B. LIN, "Merging anomalous data usage in wireless mobile telecommunications: Business analytics with a strategy-focused data-driven approach for sustainability.", European Journal of Operational Research, March 2020, vol. 281, no. 3, pp. 687-705

SUN, E. W., T. KRUSE, Y.-T. CHEN, "Stylized algorithmic trading: satisfying the predictive near-term demand of liquidity.", Annals of Operations Research, October 2019, vol. 281, no. 1/2, pp. 315-347

CHEN, Y.-T., W. LAI, E. W. SUN, "Jump detection and noise separation by a singular wavelet method for predictive analytics of high-frequency data.", Computational Economics, August 2019, vol. 54, no. 2, pp. 809-844

CHEN, Y.-T., E. SUN, M. F. CHANG, Y. B. LIN, "Enhancing travel time prediction with deep learning on chronological and retrospective time order information of big traffic data." Forthcoming Annals of Operations Research

CHEN, Y. T., Y.-B. LIN, W.-L. CHEN, K.-C. WU, E. SUN, Y.-W. LIN, C.-Y. LIU, "The CWT IoT Device for Detecting Rare Events of Orchid Disease", IEEE Internet of Things Journal,

Research themes

Artificial intelligence, Big data, Business intelligence, Data analysis, Business intelligence, Statistics, Business intelligence, Creativity methods, Econometrics, Fintech, Research methodology - Transportation and storage, Water supply; sewerage, waste

Teaching disciplines

Analytics and Data Science, Logistics, transport(s) and operations management, Information system, Digital tools, Finance, Research methodology

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