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Research Interests
Biomedical Statistics & Clinical trial design
Machine Learning & Health Data Science
Epidemiology (chronic, infectious, medicine)
Computational Biology
My research sits at the intersection of machine learning, statistical modeling, and public health decision science, with a central focus on early disease detection and clinically deployable AI systems. Broadly, my work spans EHR data analysis, integrating development of statistical learning models, and AI to enhance diagnosis, monitoring, and population-level health strategies. I am very interested in collaborative opportunities that bridge methodological rigor with real-world implementation.
Recent Publications
Mostafa, F., Sharma, K. & Khan, H. Development and validation of dementia diagnosis in adults through machine learning frameworks: a cross-sectional study using clinical and imaging data. BMC Med Inform Decis Mak (2026). https://doi.org/10.1186/s12911-026-03837-y
Khan, Hafiz, Lamourdia Dayamba, Aamrin Rafiq, Effat W. Nizia, and Fahad Mostafa. "Impact of aging and body mass index on self-reported cognitive impairment among West Texas adults: Implications for better lifestyle." Journal of Alzheimer's Disease Reports 10 (2026): 25424823261460418.
Lukhele, Nosizo, and Fahad Mostafa. "Development of Explainable Machine Learning Framework for Early Detection and Risk Stratification of Diabetes in Age Specific Variations." medRxiv (2026): 2026-04.
Dananjani Liyanage, Mahmudul Bari Hridoy and Fahad Mostafa, Modeling the Hazard Function with Non-linear Systems in Dynamical Survival Analysis. arXiv preprint; https://doi.org/10.48550/arXiv.2602.06322 (2026)
Mostafa, Fahad, and Hafiz Khan. "Functional Random Forest with Adaptive Cost-Sensitive Splitting for Imbalanced Functional Data Classification." arXiv preprint arXiv:2512.07888 (2025).
Chakroborty, Sajal, and Fahad Mostafa. "SEIRV epidemiological model for COVID 19 with Holling type II functional response." Nature Scientific Reports (2026).
Hassan, Riad,..., Fahad Mostafa, and Md Mostafijur Rahman. "An efficient dual-line decoder network with multi-scale convolutional attention for multi-organ segmentation." Biomedical Signal Processing and Control 112 (2026): 108611.
Volchenkov, Dimitri, Nuwanthika Karunathilaka, Vichithra Amunugama Walawwe, and Fahad Mostafa. "Trust as a Stochastic Phase on Hierarchical Networks: Social Learning, Degenerate Diffusion, and Noise-Induced Bistability." Dynamics 6, no. 1 (2026): 4.
Mostafa, Fahad, Kannon Hossain, Dip Das, and Hafiz Khan. "Deep Learning Approaches with Explainable AI for Differentiating Alzheimer’s Disease and Mild Cognitive Impairment." AppliedMath sec. Optimization and Machine Learning 5, no. 4 (2025): 171.
Mostafa, Fahad. "A Statistical Framework for Model Selection in LSTM Networks." Model Assisted Statistics and Applications (2025): 15741699251410081.
Mostafa, Fahad, and Minjun Chen. "Machine learning and artificial intelligence methods for predicting liver toxicity." In Machine Learning and Artificial Intelligence in Toxicology and Environmental Health, pp. 139-152. Academic Press, 2026.
Mostafa, Fahad, and Linda JS Allen. "Periodic mean-reverting stochastic differential equations and parameter estimations for seasonal data." Stochastic Models (2024): 1-32.
Mostafa, Fahad, Hafiz Khan, Fardous Farhana, and Md Ariful Haque Miah. "Application of Deep Learning Framework for Early Prediction of Diabetic Retinopathy." AppliedMath sec. Optimization and Machine Learning 5, no. 1 (2025): 11.
Husar, Kateryna, Dana C. Pittman, Johnny Rajala, Fahad Mostafa, and Linda JS Allen. "Lyme disease models of tick-mouse dynamics with seasonal variation in births, deaths, and tick feeding." Bulletin of Mathematical Biology 86, no. 3 (2024): 25.
Khan, Hafiz, Fardous Farhana, Fahad Mostafa, et al. "Gender differences in cognitive impairment among the elderly in rural West Texas counties." Journal of Alzheimer’s Disease (2025): 13872877241305772.
Khan, Hafiz, Fardous Farhana, Fahad Mostafa, Rumana Atique et al. "Comparative Study of Risk Factors Associated with Normal Cognition and Cognitive Impairment in Rural West Elderly Texans." Journal of Alzheimer's Disease Reports 8, no. 1 (2024): 1133-1151.
Mostafa, Fahad, Victoria Howle, and Minjun Chen. "Machine learning to predict drug-induced liver injury and its validation on failed drug candidates in development." Toxics: Predictive Toxicology 12, no. 6 (2024): 385.
Mostafa, Fahad, and Minjun Chen. "Computational models for predicting liver toxicity in the deep learning era." Frontiers in Toxicology (sec. Computational Toxicology and Informatics), 5 (2024): 1340860.
Jaffry, Mustafa, Owais M. Aftab, Fahad B. Mostafa, Kamel Jedidi, Hafiz Khan, and Nizar Souayah. "Optic neuritis after COVID-19 vaccination: an analysis of the vaccine adverse event reporting system." Journal of Neuro-Ophthalmology 43, no. 4 (2023): 499-503.
Hasan, M. E., F. Mostafa, M. S. Hossain, and J. Loftin. Machine-learning classification models to predict liver cancer with explainable AI to discover associated genes. AppliedMath 3 (2): 417–445. 2023.
Jaffry, M., F. Mostafa,..., and N. Souayah. "No significant increase in Guillain-Barré syndrome after COVID-19 vaccination in adults: A vaccine adverse event reporting system study." Vaccine 40, no. 40 (2022): 5791-5797.
Mostafa, Fahad, Easin Hasan, Morgan Williamson, and Hafiz Khan. "Statistical machine learning approaches to liver disease prediction." Journal Livers 1, no. 4 (2021): 294-312.
Mostafa, Fahad, Pritam Saha, Mohammad Rafiqul Islam, and Nguyet Nguyen. "GJR-GARCH volatility modeling under NIG and ANN for predicting top cryptocurrencies." Journal of Risk and Financial Management 14, no. 9 (2021): 421.
Fahad Mostafa, Linda JS Allen, Modeling Seasonal Time Series with Periodic Mean-Reverting Stochastic Differential Equations, JSM 25, Nashville, TN, Aug 2-7.
Md AH Miah, Fahad Mostafa, “Deep Learning for Diabetic Retinopathy", The IISE Annual Conference, Atlanta, Georgia, Jun 3, 2025
Fahad Mostafa, Linda JS Allen, Stochastic Modeling for Periodic Time Series Data, SIAM AN24, Spokane, Washington, July 8-12.
Fahad Mostafa, Victoria Howle, Minjun Chen, Predictive Modeling for Drug-Related Hepatotoxicity and Validation on Unsuccessful Drug Candidates in Development, 23rd Texas Tech Graduate School Research Poster Competitions, Mar 8, 2024.
Fahad Mostafa, Victoria Howle, ''Improved Graph Based Clustering Methods'', Advances in Statistical and Computational Methods for Analysis of Biomedical, Genetic 2023, and Omics Data, UTD, TX.
Fahad Mostafa, Victoria Howle, Leif Ellingson, Katharine Long, ''Machine Learning with Multivariate Statistical Analysis for Prediction of Breast Cancer'', 2022 SIAM Conference on Mathematics of Data Science.
“AI/ML Explained Variable Selection Technique for Personalized Diagnosis of Disease”, coauthor with Dr. Hafiz Khan at 21st Texas Tech Graduate School Research Competitions, 2022.
Kate Hasur, ... Fahad Mostafa, Linda J.S. Allen, “Tick-Mouse Stochastic Models for Lyme Disease with Seasonal Variation”, 4th SIAM-TXLA Meeting, South Padre Island, Texas, NOV 5-7, 2021.
Fahad Mostafa, Lief Ellingson, “Multivariate Statistical Learning Methods for Imbalanced Classification Applied to Cancer Inferences”, Conference of TEXAS Statisticians, OCT 09, 2021.
Fahad Mostafa, “Machine Learning for Liver Disease Detection”, Modeling in a Heterogeneous World, XVIII Red Raider Minisymposium’’, AUG 20-21, 2021
Fahad Mostafa, Easin Hasan, Sakhawat Hossain, “Can Statistical Machine Learning Technique Overperform the Prediction to Find the Level of Obesity?”, Obesity Research Institute 6th Annual Meeting and Oral Competitions, Texas Tech, MAY 12, 2021
Fahad Mostafa, “Optimal Matrix Decomposition and its Regularization with Large Scale Data Reduction” IMA workshop on Optimal Control, Optimal Transport, and Data Science, University of Minnesota, NOV 9-12, 2020.
Fahad Mostafa, and Rejuan Haque, "Optimization of Robust Clustering from Graphs" 3rd SIAM-TXLA Meeting, Texas A&M, OCT 16, 2020.
Fahad Mostafa and Leif Ellingson, "Inferring Presence of Breast Cancer using Machine Learning with Multivariate Statistical Analysis". Texas Tech Grad School Research Competitions, MAR 11, 2020.
Fahad Mostafa, “Combined effect of viscous dissipation, radiation and heat generation on a stretching plate for unsteady case”, “19th International Conference of Mathematics, Host: Bangladesh Mathematical Society”, Date: May 1, 2016.
Research Projects with Graduate Students
Arthur Yu, Thesis title: "Interpretable Bootstrap Weighted Oblique Random Forests for Multiclass Classification with Application to Biomedical Data" (2026)
Catalina Amurrio Zamora, Thesis title: "Cross-Tissue Cellular Responses on a Dehydrated Placenta-Derived Extracellular Matrix Scaffold Revealed by Quantitative Imaging and Unsupervised Clustering" (2026)
Nosizo Lukhele, "Enhancing Early Detection and Risk Stratification of Diabetes Using Explainable Machine Learning Approaches" (2026)
Kushagra Sharma, "Early Dementia Diagnosis in Older Adults through Machine Learning: A Cross-Sectional fMRI Data Analysis" (2025)
Current Projects
ML method developments for DILIrank dataset. ( Project with Dr. Minjun Chen, NCTR, Arkansas)
A Novel Missing Data Imputation Method for EHR. (Current Project with Dr. Katie Colborn at CU Anschutz)
Improved Graph-based data Clustering techniques with image processing applications. (Current Project with Dr. Victoria Howle & Dr. Dimitri Volchenkov)
Statistical Machine Learning Methods to Variable Selection and Prediction from Breast Cancer Dataset. (Current Project with Dr. Dimitri Volchenkov)
Regularized functional regression for periodic data. (Current Project with Dr. Dimitri Volchenkov)
Stochastic process for non-linear periodic data: Environmental Variability and Mean Reverting processes with SDEs. (Extension of REU/Current Project with Dr. Linda Allen)
Machine Learning/AI-based Gene Expression Analysis for Discovering Hepato Cellular Carcinoma. (Collaboration with Dr. Md. Easin Hasan, JohnHopkins University)
Statistical Learning for Liver Disease, AI-based model selections, and Inferences, (Collaboration with Dr. Hafiz Khan of TTUHSC)
UQ and Machine Learning Mathematical Model Development for Breast Cancer Treatments Monitoring. (Delayed)
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