G M Fahad Bin Mostafa

Assistant Professor,

School of Mathematical and Natural Sciences

Other ASU affiliations

Senior Global Futures Scientist, Global Futures Scientists and Scholars

[email protected]

FAB, N209; 13591 N. 47th Ave. Phoenix, AZ 85051
Campus: West
‌

Long Bio

G M Fahad Bin Mostafa, a.k.a. Fahad Mostafa, is an assistant professor of Biostatistics and Data Science at the Interdisciplinary Arts and Sciences of Arizona State University. He works in the fields of biomedical statistics, health data science, epidemiology, computational biology, UQ and AI in biology and medicine.

Mostafa's PhD dissertation, "Statistical Machine Learning Models for Biomedical Science", highlights his passion for learning algorithms in medical statistics. He is primarily interested in developing machine learning methodologies for predictive modeling, and clinical trial design for the AI/ML framework. Using statistical learning/AI, Mostafa aims to enhance disease diagnosis and monitoring, with a special emphasis on EHR, histology images, genomics, and medical imaging. His recent focus has been on identifying patterns in biomedical data, particularly in infectious diseases, Cancer, CVD, Liver, and Alzheimer’s disease.

As a biostatistician, his research program centers on developing and applying rigorous statistical and machine learning methods to advance population health, with a particular emphasis on chronic disease prevention, management, and outcomes. He works on integrating large-scale observational data, EHRs, and real-world evidence to generate robust, reproducible insights that inform healthcare delivery. His work includes the design and analysis of AI/ML models, risk prediction modeling, causal inference in longitudinal settings, and the evaluation of interventions aimed at improving equity, quality of care, and patient-centered outcomes. Through close collaboration with clinicians, epidemiologists, and health system leaders, he aims to translate methodological innovation into actionable evidence that strengthens healthcare systems and improves health outcomes at both individual and population levels.

Another key area of his work includes infectious disease modeling, where he applies dynamic predictive frameworks to track outbreaks, seasonal transmission patterns, and support public health interventions. Leveraging large-scale biomedical data and real-time analytics, Mostafa designs models that adapt to evolving disease landscapes, enabling earlier detection and more targeted responses. Through this fusion of data science, statistics, and clinical relevance, Mostafa is driving forward the next generation of intelligent healthcare systems, and making significant contributions to the advancement of biomedical statistics and health science.

I am seeking graduate students to join my lab.

Education

Postdoctoral Fellow in Biostatistics. CU Anschutz School of Medicine 2024-25
ORISE Fellow in Machine Learning & Biostatistics. NCTR, and CDRH, FDA 2023,25
Ph.D. Statistics, Texas Tech 2024
MS. Statistics, Texas Tech 2022
MS. Applied Mathematics, University of Dhaka, Bangladesh 2016
BS. Mathematics, University of Dhaka, Bangladesh 2014

Research Interests

Biostatistics & Epidemiology
Clinical Trial Design
Statistical Learning
Biomedical Data Science
AI in Medicine and Genomics

Graduate College Faculty

Research Group

Graduate Students at my Lab

Mehriban Aghagulova
Mary Jane Lindner
Melissa Scott-Preusse
Mohsina Jannat

Recent Graduates from ASU

Chung Yu, BDS MS
Catalina Zamora, BDS MS
Kushagra Sharma, Statistics MS
Nosizo Lukhele, BDS MS

Publications

Selected Recent Works

Mostafa, F., Sharma, K., & Khan, H. (2026). 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).
Lukhele, N., & Mostafa, F. (2026). Development of an Explainable Machine Learning Framework for Early Diabetes Detection, Multiclass Classification, and Risk Stratification Across Age Groups. Front. Digit. Health (2026).
Khan, H., Dayamba, L., Rafiq, A., Nizia, E. W., & Mostafa, F. (2026). 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: 1-17.
Hassan, R.,., Mostafa, F., & Rahman, M. M. (2026). An efficient dual-line decoder network with multi-scale convolutional attention for multi-organ segmentation. Biomedical Signal Processing and Control, 112, 108611.
Mostafa, F., & Chen, M. (2026). Machine learning and artificial intelligence methods for predicting liver toxicity. Machine Learning and Artificial Intelligence in Toxicology and Environmental Health, Elsevier Academic Press, (pp. 139-152).
Khan, H., Farhana, F., Mostafa, F., & Reddy, P. H. (2025). Gender differences in cognitive impairment among the elderly in rural West Texas counties. Journal of Alzheimer’s Disease, 2025; 103 (3): 687-705.
Khan, H., Farhana, F., Mostafa, F.,... & Reddy, P. H. (2024). Comparative Study of Risk Factors Associated with Normal Cognition and Cognitive Impairment in Rural West Elderly Texans. Journal of Alzheimer's Disease Reports, 8(1), 1133-1151.
Mostafa, F., Howle, V., & Chen, M. (2024). Machine learning to predict drug-induced liver injury and its validation on failed drug candidates in development. Toxics (Predictive Toxicology), 12(6), 385.
Jaffry, M., Aftab, O. M., Mostafa, F., et al. (2023). Optic neuritis after COVID-19 vaccination: an analysis of the vaccine adverse event reporting system. Journal of Neuro-Ophthalmology, 43(4), 499-503.
Jaffry, M., F. Mostafa, et al. (2022) "No significant increase in Guillain-Barré syndrome after COVID-19 vaccination in adults: A vaccine adverse event reporting system study." Vaccine, no. 40: 5791-5797.
Chakroborty, S., & Mostafa, F. (2026). SEIRV epidemiological model for COVID 19 with Holling type II functional response. Scientific Reports.
Mandava, K., Jaffry, M., Mostafa, F., Jaffry, K., Khan, H., & Souayah, N. (2022). Machine Learning Analysis of Anosmia and Ageusia after COVID-19 Vaccination: A Vaccine Adverse Event Reporting System (VAERS) Study. Neurology, 98, 2214.
Mostafa, F., Ganji, R., St. John., Khan, H. (2021). Coronavirus (COVID-19) Spike in Georgia: An Epidemiologic Study of Data, Modelling, and Policy Implications to Understand the Gender-and Race-Specific Variations. Am J Biomed Sci & Res. 2022 - 16(2). DOI: doi.org/10.1101/2021.12.09.21267571
Mostafa, F., Hasan, E., Williamson, M., & Khan, H. (2021). Statistical machine learning approaches to liver disease prediction. Journal Livers 1, 294–312 .
Liyanage, D., Hridoy, M. B., Mostafa, F. (2026). Modeling the Hazard Function with Non-linear Systems in Dynamical Survival Analysis. doi.org/10.48550/arXiv.2602.06322
Volchenkov, D., Karunathilaka, N., Walawwe, V. A., & Mostafa, F. (2026). Trust as a Stochastic Phase on Hierarchical Networks: Social Learning, Degenerate Diffusion, and Noise-Induced Bistability. Dynamics, 6(1), 4.
Mostafa, F., Hossain, K.,& Khan, H. (2025). Deep Learning Approaches with Explainable AI for Differentiating Alzheimer Disease and Mild Cognitive Impairment. doi.org/10.48550/arXiv.2510.00048
Mostafa, F.,, & Khan, H. (2025). Functional Random Forest with Adaptive Cost-Sensitive Splitting for Imbalanced Data Classification. doi.org/10.48550/arXiv.2512.07888
Mostafa, F. (2026). A Statistical Framework for Model Selection in LSTM Networks. Model Assisted Statistics and Applications, 21 (1), 27-49.
Mostafa, F., & JS Allen, L. (2025). Periodic mean-reverting stochastic differential equations and parameter estimations for seasonal data. Stochastic Models, 1-32.
Mostafa, F., Khan, H., Farhana, F., & Miah, M. A. H. (2025). Application of Deep Learning Framework for Early Prediction of Diabetic Retinopathy. AppliedMath (Optimization and Machine Learning), 5(1), 11.
Husar, K.,., Mostafa, F., & Allen, L. J. (2024). Lyme disease models of tick-mouse dynamics with seasonal variation in births, deaths, and tick feeding. Bulletin of Mathematical Biology, 86(3), 25.
Hasan, M. E., Mostafa, F., Hossain, M. S., & Loftin, J. (2023). Machine-learning classification models to predict liver cancer with explainable AI to discover associated genes. AppliedMath, 3(2), 417-445.
Hasan, E., Mostafa, F., Hossain, M. S., & Loftin, J. (2022). Explainable-AI to Discover Associated Genes for Classifying Hepato-cellular Carcinoma from High-dimensional Data. medRxiv, 2022-08.
Mostafa, F., Saha, P., Islam, M. R., & Nguyen, N. (2021). GJR-GARCH volatility modeling under NIG and ANN for predicting top cryptocurrencies. Journal of Risk and Financial Management, 14(9), 421.

Courses

2027 Spring

Course Number	Course Title
STP 315	Statistical Computing
STP 452	Multivariate Statistics
STP 452	Multivariate Statistics
STP 485	Statistics Capstone

2026 Fall

Course Number	Course Title
STP 485	Statistics Capstone
STP 494	Special Topics
STP 494	Special Topics
LSC 598	Special Topics
LSC 598	Special Topics
TPH 592	Research
TPH 504	Appl Biostats Public Health
TPH 504	Appl Biostats Public Health
MAT 592	Research