Research

Harshvardhan, M., Curtland, C., Ghozeil, A., Liu, C.

Foresight: The International Journal of Applied Forecasting, Issue 79 (2025)

Presents the HP forecasting project for practitioners, focusing on lessons from applying machine learning at enterprise scale.

Harshvardhan, M., Curtland, C., Hwang, J., VanDam, C., Ghozeil, A., Neto, P. A., Marie, F., Liu, C.

INFORMS Journal of Applied Analytics (2025)

Shows how a global forecasting system combines machine learning with expert judgement and becomes part of enterprise planning.

Ph.D. dissertation, Haslam College of Business, University of Tennessee, Knoxville (2025)

Connects forecasting models, deployment, human judgement, and decision optimisation within an enterprise forecasting framework.

Mao, W., Liu, C., Huang, Y., Zu, Z., Harshvardhan, M., Wang, L., Zheng, B.

Proceedings of the 29th ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD '23)

Jointly learns impression forecasts and advertising-contract allocations, training predictions around the decisions they support.

Harshvardhan, M., Kumar, B.

Case study and teaching note, Ivey Publishing (2021)

A teaching case examining how a robotics startup chooses its customers, positioning, pricing, and marketing strategy.

Ranjan, P., Harshvardhan, M.

Applied Statistical Methods: ISGES 2020, Springer Singapore (2022)

Applies dynamic Gaussian-process models to forecast vaccine coverage across 78 malaria-prone countries using existing vaccination data.

Harshvardhan, M., Ranjan, P.

Handbook of Research on Cloud Computing and Big Data Applications in IoT, IGI Global (2019), pp. 202-228

Reviews Gaussian-process surrogate modelling, numerical stability, and methods for handling large simulation datasets.