Kazi Tanvir Hasan

Postdoctoral Associate in Psychiatry at Yale School of Medicine with a PhD in Public Health specializing in Biostatistics and Data Analytics. Expertise in genetically-informed research on substance use disorders and psychiatric conditions utilizing large datasets and biobanks. Proficient in advanced statistical methods, machine learning, and Bayesian analysis to assess neurodegenerative disease risk. Committed to reproducible research and mentoring, currently focused on causal Bayesian networks and longitudinal data analysis.

• American Statistical Association (ASA), Member
• Dhaka University Statistics Department Alumni Association, Lifetime Member

0000-0002-7723-4177

• Graduate Assistantship , Florida International University, 2021-2025
• Multiple travel scholarships, FIU Robert Stempel College of Public Health & Social Work, GPSC, 2022-2025
• Bangladesh Sweden Trust Fund Scholarship, 2022
• Graduate Teaching Assistantship , Illinois State University, 2018-2020

MANUSCRIPT

1. Hasan, K. T., & Akman, O. (2021). Mortality modeling under stochastic frailty. Missouri Journal of

Mathematical Sciences, 33(1). https://doi.org/10.35834/2021/3301105

2. Ahamed, H., Hasan, K. T., Islam, Md. T., & Galib, F. C. (2020). Lockdown policy dilemma:

COVID-19 pandemic versus economy and mental health. Journal of Biomedical Analytics, 3(2),

37-58. https://doi.org/10.30577/jba.v3i2.60

PREPRINT/IN REVISION/IN PRESS

1. Hasan, K. T., Odom, G., Bursac, Z., Lucchin, R., & Ibrahimou, B. (2025). Enhancing Bayesian

kernel machine regression: A dynamic thresholding framework to address variability and skewness

in high-dimensional environmental health data. medRxiv Preprint.

https://doi.org/10.1101/2025.04.14.25325822

2. Hasan, K. T., Odom, G., Bursac, Z., & Ibrahimou, B. (2024). The sensitivity of Bayesian kernel

machine regression (BKMR) to data distribution: A comprehensive simulation analysis. arXiv

Preprint. https://doi.org/10.48550/arXiv.2411.00286

3. Ibrahimou, B., Hasan, K. T., et al. (2024). Assessing the risk of heart attack: A Bayesian kernel

machine regression analysis of heavy metal mixtures. Res Sq Preprint.

https://doi.org/10.21203/rs.3.rs-4456611/v1

CONFERENCE PRESENTATIONS

1. Hasan, K. T., Ibrahimou, B., & Odom, G. (2025). Adaptive Thresholding in Bayesian Kernel Machine Regression: Improving Sensitivity and Reliability, 2025 Joint Statistical Meeting, Nashville, Tennessee, USA. 

2. Hasan, K. T., Ibrahimou, B., & Odom, G. (2023). Impact of heavy metal mixtures on cognitive

decline and Alzheimer's disease and related dementias (ADRD), 2023 Joint Statistical Meeting, Toronto, Ontarian, Canada. 

3. Hasan, K. T., Ibrahimou, B., & Burchfield, S. (2022). A Bayesian kernel machine regression

approach to assessing the effect of heavy metal mixtures in blood and urine on the risk of heart

attack. 2022 Joint Statistical Meeting, Washington DC, USA 

4. Hasan, K. T. (2020). Infectious disease mortality prediction. Illinois State University.