Dhafer Malouche
Welcome to My Academic Hub
Welcome to my professional website! As a Professor of Statistics at Qatar University with over 25 years of experience across international institutions, I’ve created this space to share my academic journey and resources with students, colleagues, and fellow researchers.
What You’ll Find Here
- Interactive Tutorials: Step-by-step guides on statistical methods, data science techniques, and programming with R, Python, H2O, and Spark
- Research Portfolio: Access to my published papers, ongoing projects, and collaborations in graphical models, public health, data quality, and more
- Teaching Materials: Course resources, lecture notes, and educational tools for statistics and data science
- Interactive Applications: Explore data through custom Shiny apps, dashboards, and visualization tools I’ve developed for research and education
- Data Analysis Projects: Case studies demonstrating statistical modeling, machine learning, and data visualization techniques
- Professional Background: Details about my academic journey, including my work at universities in Qatar, Egypt, Tunisia, and my time as a Fulbright Scholar in the United States
My expertise spans statistical theory and practical applications using modern computational tools. Whether you’re interested in graphical models, public health analytics, consumer preference analysis, or machine learning implementations, you’ll find relevant content throughout this site.
Feel free to contact me with questions, collaboration ideas, or feedback on any of the resources shared here.
ORCID ID
Biography
Dr. Dhafer Malouche is a Professor of Statistics at Qatar University, with more than 25 years of academic experience across Europe, North Africa, the Middle East, and North America. He earned his Ph.D. in Statistics and Applied Mathematics from Paul Sabatier University and has held faculty positions in Tunisia, Egypt, and Qatar.
His international experience includes appointments as a Fulbright Scholar at Stanford University (2011) and the University of Michigan (2016–2017), as well as long-term research collaborations with Yale University through the MacMillan Center (2014–2019). His work during these periods addressed survey methodology, data quality, governance, and applied statistical modeling in social and health sciences.
Dr. Malouche’s current research focuses on advanced statistical modeling for population health, with particular emphasis on Bayesian networks, graphical models, epidemiological modeling, and reproducible data science. He is actively involved in large-scale biomedical research using data from the Qatar Biobank and primary healthcare systems, leading and co-authoring studies on chronic pain, cardiometabolic risk, autoimmune diseases, respiratory health, and mental health indicators. His recent work also examines the reliability of AI-detection tools in scientific publishing and the integration of reproducible statistical workflows in medical research.
In teaching, Dr. Malouche delivers advanced courses at both undergraduate and doctoral levels, including stochastic processes, actuarial statistics, and advanced biostatistics for PhD students in the biological sciences. He makes extensive use of R, Bayesian methods, and real-world health data, and regularly develops reproducible teaching materials, Shiny applications, and LaTeX-based resources to support applied statistical training.
Beyond academia, Dr. Malouche has served as a statistical consultant for international organizations, including the World Health Organization, contributing to analyses of COVID-19 impacts on mental health and health systems. At Qatar University, he continues to lead interdisciplinary research, supervise graduate students, and contribute to methodological innovation at the intersection of statistics, public health, and data science.
Download my resumé.
- Graphical & Statistical Models
- Public Health & COVID-19
- Research & Development
- Well-being & Quality of Life
- Survey Methodology
- Data Quality & Analysis
- Consumer Preferences & Sensory Analysis
- Medical & Clinical Applications
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Habilitation (Tenure), Problèmes autour de la probabilité et de la statistique, Méthodes et Applications., September 2009
Université de Tunis ElManar Ecole National d'Ingénieurs de Tunis, Tunisia.
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Ph.D. in Statistics and Probability, October 1997
Paul Sabatier University, Toulouse, France
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Master in Applied Mathematics, 1993 - 1994
Paul Sabatier University, Toulouse, France
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Bachelor (Maîtrise) in Mathematics, 1989 – 1993
Ecole Normale Supérieure de Bizerte, Tunisia
Recent Publications
Preprints
Probabilistic pathways from chronic pain to suicidal ideation in Qatar: a Bayesian-network study
Authors: Aisha Al Khinji, Dhafer Malouche
Status: Under Review — Journal of Translational Medicine
Abstract: Chronic pain frequently co-occurs with depressive and anxiety symptoms and doubles or triples the risk of suicidal ideation. Yet the joint pathways linking these conditions remain under-explored in Middle-Eastern settings. We analyzed questionnaire data from 2,363 Qatari adults enrolled in the Qatar Biobank using a data-driven Bayesian Network to map probabilistic interdependencies between chronic pain, mental-health symptoms and demographic factors, with a focus on sequences culminating in suicidal thoughts. The final BN identified self-regret and psychomotor change as the strongest direct predictors of suicidal thoughts, with an early-symptom cascade (sleep problems → anhedonia → depression) amplifying suicide risk. Fatigue linked chronic pain to self-regret, illustrating a physical–psychological bridge.
Comparative Cardiovascular Risk Index Across Autoimmune Conditions in a Large Middle-East Health System
Authors: Aisha Al Khinji, Dhafer Malouche, Abdullatif Al-Hor, Hadeel Ashwal, et al.
Status: Posted
Abstract: Autoimmune diseases confer excess cardiovascular disease (CVD) risk, yet comparative profiles across phenotypes in Gulf health systems are unclear. Using routinely collected primary-care records, we analyzed 14,616 adults and derived a composite CVD risk index from a Framingham-based algorithm. Relative to the non-autoimmune reference, adjusted odds of high risk were higher in rheumatoid arthritis (OR 1.20, 95% CI 1.06–1.37), lower in Hashimoto’s thyroiditis (0.49, 0.40–0.60), and not clearly different in SLE or multiple-autoimmune phenotypes. These findings support systematic CVD risk assessment and targeted management in autoimmune populations within Gulf health systems.
