Dr. Hieu Pham Assistant Professor, Management Science Contact 301 Sparkman DriveBusiness Administration BuildingRoom 368Huntsville, AL 35899 Campus Map 256.824.2331hieu.pham@uah.edu Biography Curriculum Vitae Business Analytics Capstone Details Education Ph.D., Industrial Engineering - Operations Research and Analytics, Iowa State University, 2018 M.S., Mathematics, Kansas State University, 2015 B.S., Mathematics, Tennessee Technological University, 2014 Honors & Awards 1st Place in Image-Based Sorghum Head Object Detection and Counting Machine Learning Competition, 2019 Research Excellence Award - Iowa State University, 2018 Teaching Excellence Award - Iowa State University, 2017 Affiliations 2018 – Present: INFORMS, Membership Recent Publications K. Wood, C. Pyun, and H. Pham. “Beyond Green Labels: Assessing Mutual Funds’ ESG Commitments through Large Language Models”. Finance Research Letters. 2024; (ABDC Rank: A). H. Pham, Y. Tan, T. Singh, V. Pavlopoulos, and R. Patnayakuni. “A multi-head attention-like featureselection approach for tabular data”. Knowledge-Based Systems. 2024; 112250. (ABDC Rank: A). S. Khaki, N. Safaei, H. Pham, and L. Wang. “Wheatnet: A lightweight convolutional neural network for high-throughput image-based wheat head detection and counting". Neurocomputing. 2022; 489:78–89. S. Moeinizade, H. Pham, Y. Han, A. Dobbels, and G. Hu. “An applied deep learning approach for estimating soybean relative maturity from UAV imagery to aid plant breeding decisions". Machine Learning with Applications. 2022; 7:100233. S. Khaki, H. Pham, Z. Khalilzadeh, Z. Masoud, N. Safaei, Y. Han, W. Kent and L. Wang. “High-Throughput Image-Based Plant Stand Count Estimation Using Convolutional Neural Networks". PLOS One. 2022; 17(7), e0268762. M. Shahhosseini, G. Hu., and H. Pham. “Optimizing Ensemble Weights and Hyperparameters of Machine Learning Models for Regression Problems". Machine Learning with Applications. 2022; 100251. S. Khaki, H. Pham, and L. Wang. “Simultaneous corn and soybean yield prediction from remote sensing data using deep transfer learning". Nature Scientific Reports. 2021; 11(1):1–14 Y. Han, J. Cameron, L. Wang, H. Pham, and W. Beavis. “Dynamic Programming for Resource Allocation in Multi-allelic Trait Introgression". Frontiers in Plant Science. 2021; 12:1181. S. Khaki, H. Pham, Y. Han, A. Kuhl, W. Kent, and L. Wang. “DeepCorn: A semi-supervised deep learning method for high-throughput image-based corn kernel counting and yield estimation". Knowledge-Based Systems. 2021; 106874. S. Moeinizade, Y. Han, H. Pham, G. Hu, and L. Wang. “A look-ahead Monte Carlo simulation method for improving parental selection in trait introgression". Nature Scientific Reports. 2021; 11(3918).