Rate My Professor Ali Eshragh

Eshragh's research focuses on statistical modelling integrated with machine learning for big data analysis, forecasting of large time series datasets, Markov Decision Processes, reinforcement learning, randomized numerical linear algebra, and supply chain optimization. He leads the ForBiD research group on Forecasting and Big Data, securing 15 grants totaling $3,619,951, including projects on demand forecasting for Australian food and beverage supply chains with partners like Coca Cola Amatil and Sanitarium, and collaborations with Yale University, University of California Berkeley, and others. Key publications include "A Tradeoff Model for Green Supply Chain Planning: A Leanness-Versus-Greenness Analysis" (Omega, 2015), "Tactical Supply Chain Planning under a Carbon Tax Policy Scheme: A Case Study" (International Journal of Production Economics, 2015), "Modeling the Dynamics of the COVID-19 Population in Australia: A Probabilistic Analysis" (PLoS ONE, 2020), and "LSAR: Efficient Leverage Score Sampling Algorithm for the Analysis of Big Time Series Data" (Journal of Machine Learning Research, 2022). Awards encompass the Australian Society for Operations Research Rising Star Award (2017), University of Newcastle Staff Excellence Award (2020), and Teaching Excellence Team Award runner-up (2016). He has served as Associate Editor for Environmental Modeling & Assessment, Deputy Head of School for Industry Engagement, on the Faculty of Science Board, and organized international workshops.