Research
Md Roungu Ahmmad
About
The Chi-Square: Significant Relationships Research Lab focuses on advancing statistical modeling, machine learning, and data science to address critical questions in public health, cancer disparities, mental health, developmental disorders, and exposure. By leveraging large-scale national datasets (e.g., SEER, NSCH, NHATS, SAMSHA, ADNI) and innovative biostatistical methods (e.g., survival analysis, competing risks, causal inference, frailty model, joint model, machine learning), we aim to improve prevention, early detection, and treatment strategies.
Our mission is to make complex data accessible and actionable, ensuring research findings translate into improved health outcomes for diverse populations.
Md Roungu Ahmmad, PhD — Lab Director / Principal Investigator
Dr. Md Roungu Ahmmad received his PhD and MS in Biostatistics and Data Science from the University of Mississippi, School of Population Health, and holds MSc and BSc degrees in Statistics, Biostatistics and Informatics from University of Dhaka. His doctoral training emphasized advanced biostatistical modeling, data science, and the application of machine learning methods to biomedical and public health research.
Dr. Ahmmad has over a decade of experience working with government agencies (US Food and Drug and Administration, FDA), private research institutions (Jackson Heart Study, JHS), and universities (University of Mississippi Medical Center, UMMC, University of Southern Mississippi, USM, University of Pittsburgh, UPitt, Jagannath University, JNU, University of Information Technology and Science, UITS, World University of Bangladesh, WUB, etc.) both domestically and internationally. Before joining USF Health, he collaborated on interdisciplinary projects involving cancer research, mental health, chronic disease epidemiology, and developmental disorders.
He is currently an Assistant Professor of Biostatistics and Data Science at the USF Health College of Nursing and serves as the Director and Principal Investigator of the Chi-Square: Significant Relationships Research Lab. His research focuses on developing innovative biostatistical and machine learning methodologies to address critical challenges in public health and clinical research. Current projects span a wide range of areas, including:
- Data Science/ AI-Driven Mental Health Monitoring Using Weather-Related Social Media Data in Deep South
- Cancer disparities and survivorship, with emphasis on rural–urban differences and socioeconomic determinants.
- Autism Spectrum Disorder risk prediction, integrating chronic health conditions, sleep patterns, and genetic factors.
- Mental health and substance use, with a focus on regional disparities and the effects of environmental and social determinants.
- Exposome and multi-omics research, applying advanced causal inference and predictive modeling to Alzheimer’s disease and cancer.
Dr. Ahmmad employs statistical modeling, causal inference, survival analysis, and machine learning approaches to generate insights that improve prevention, diagnosis, and treatment strategies. His work is highly collaborative, spanning epidemiology, medicine, data science, and health outcomes with the ultimate goal of translating complex data-driven decisions and actionable solutions for population health.
Expertise: Biostatistical modeling, cancer epidemiology, autism, machine learning, mental health-substance abuse, and multi-omics analysis.
Get Involved in Our Research
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We are actively recruiting MS/PhD students and undergraduate volunteers interested in biostatistics, epidemiology, and data science research. We welcome motivated students and researchers to join our lab—interested applicants should submit a cover letter outlining their relevant experience and research interests, along with a current CV, to Dr. Md Roungu Ahmmad at Ahmmad@USF.edu.
Undergraduate students are expected to contribute 4–6 hours per week as research volunteers. Graduate students and trainees may also have opportunities to participate in ongoing projects, depending on interest and availability.
For all other inquiries please email Ahmmad@USF.edu.
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- Dr. Xiaoli Zhang – Public Health & Nursing Research
- Dr. Ukamaka Oruche – Mental Health Research
- Dr. Fazlay Faruque – Geospatial & Environmental Health Research
- Dr. Laurie McCrae – Nursing Research
- Dr Christina McCrae – Sleep research expertise
- Dr. Md Tareq Ferdous Khan – Health Science and biostatistician
- Dr Harry Pantazopoulos, Ph.D. – Neuroscience expertise
- Rodney Rocconi, Ph.D. – Oncologist
Research Highlights & Resources
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- Mental Health Surveillance and Weather Events: Insightful from social media data.
- Sleep Sufficiency & Autism Risk: Investigating sleep duration and patterns as predictors of ASD in children.
- Substance Use & Mental Health: Exploring associations between mental health, and substance use disorders with demographic disparities.
- Mental Health & Weather Disasters (Text Data): Using AI and NLP to study mental health signals during natural digester.
- Exposome & Cancer Disparities (SEER, NHATS, ADNI, cBioprotal): Multi-omics and causal inference approaches to identify biomarkers for cancer and disease progression.
- Processed Foods & Health Outcomes: Evidence for better population health
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- Ahmmad, M.R., et al. (2025). Substance Use and Co-Occurring Mental Health Disorders in the Deep South. BMC Psychology (under review).
- Ahmmad, M.R., et al. (2025). Machine Learning Approaches for Alzheimer’s Disease Risk Prediction Using Multi-Omics Data. Discover Oncology.
- Ahmmad, M.R., et al. (2025). Rare Sigmoid Colon Adenocarcinoma Survival Disparities by Rural-Urban Continuum Codes. Journal of Cancer Survivorship.
- Ahmmad, M.R., et al. (2025). Sleep Pattern Consistency and Autism Risk in U.S. Children. Journal of Autism and Developmental Disorders (under review).
More publications: Google Scholar | PubMed
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- Awarded SHINE Pilot Project Grant (2025) to study age-specific sleep sufficiency and autism spectrum disorder risk.
- Manuscript under review: Substance Use and Mental Health Disorders in the Deep South (BMC Psychology).
- Submitted manuscript: ASD Risk Prediction Framework Using Causal and Machine Learning Approaches.
- Presented at Joint Statistical Meetings (JSM) 2025 on causality and machine learning for Heart Cancer patients’ survivorship and evaluating multimodal interventions.
- Ongoing collaborations with USF SHINE Center, University of Mississippi Medical Center, and FDA ORISE fellows.
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Assistant Professor of Biostatistics
USF Health, 12912 USF Health Dr, Tampa, FL 33612
University of South Florida, Tampa, Florida, 33612
Email: Ahmmad@USF.edu, Personal: RounguJU@gmail.com