Who I Am
Research Profile
- Researcher in Machine Learning, Explainable AI & Global Health Analytics
- 15 peer-reviewed papers in Nature, BMJ, Springer & Wiley journals
- Focus: dengue, malaria & TB prediction, spatial epidemiology, climate-health interactions
- Methods: SHAP, LIME, Bayesian deep learning, spatial statistics, ARIMA/ConvLSTM
- Peer reviewer for BMJ Global Health, PLOS Digital Health & 7 more journals
- Open to research collaboration & doctoral opportunities in ML for global health
Focus Areas
Research Interests
Machine Learning in Public Health
Tree-based & deep learning models for disease surveillance.
Explainable AI (XAI)
SHAP & LIME make predictions auditable, not black boxes.
Epidemiological Modeling
Tracing how disease burden shifts across time and place.
Spatiotemporal Analysis
Moran's I & Getis-Ord Gi* across regions and years.
Disease Prediction Systems
District & national early warning tools for outbreaks.
Climate & Health
Eco-climatic triggers linking rainfall & disease risk.
Toolkit
Technical Skills
Core Methods
Machine LearningSHAPLIMECausal AI
Programming & Tools
RPythonSPSSGit
Advanced Analytics
ARIMA/SARIMABayesianXGBoost
Application Domains
EpidemiologyClimate-Health
Built & Shipped
See all Projects & Tools Development
BD Health & Climate Atlas
Interactive atlas linking climate variables with health outcomes across Bangladesh.
Open atlas →
DaySpace
A web app for organizing your day: plan tasks, track time and keep daily focus.
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Games
Small browser games built for fun and learning, playable right on the portfolio.
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Dengue Dashboard
Interactive district-level early warning visualization for all 64 districts of Bangladesh.
Systematic Review Tool
Automated literature screening & data extraction workflow for health research.
R Package
Open-source R package for time-series modeling, SHAP analysis & visualization.
Continuous Learning
See all Courses & Training
Filter by Source
June 2026
B.Sc. in Statistics
Begum Rokeya University, Rangpur
Jan–Mar 2026
Scientific & Manuscript Writing Series
Researcher Academy, Elsevier
6 modules on Gen AI in research, manuscript & review writing
Mar 2025
Practical Time Series Analysis
State University of New York
Sep 2024
Climate Risk Informed Decision Analysis
UNESCO
Jan–Jun 2024
Data Science & ML Specialization
IBM
6 courses: ML with Python, R for Data Science, Data Science Methodology
Mar 2024
Successful Presentation
University of Colorado Boulder
Dec 2023
Research Methodology Series
RSBRU / Queen Mary / University of London
4 courses on interdisciplinary research methods
Dec 2023
Hypothesis Testing: Intro to Statistical Analysis
SAS
Nov 2023
UNICEF Training Series
UNICEF
Education in Emergencies; Leadership for Adolescents
Jul–Dec 2016
Diploma in Computer Science
Bangladesh Computer Education (BCE)
2019
Higher Secondary Certificate (Science)
Police Lines School and College, Rangpur
Peer-Reviewed Output
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Publications
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Explainable Machine Learning for Predicting Global Malaria Incidence From 2000 to 2022 With Climatic and Extreme Weather Conditions Across 78 Endemic Countries: A Retrospective Ecological Study
Shiddik, Sohan. Health Sci Reports, 9(10), e73311 (2026)
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District-level measles outbreak prediction in Bangladesh using geospatial artificial intelligence
Shiddik, Rahman. BMC Infectious Diseases (2026)
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Bayesian spatiotemporal modelling of child mortality (2000–2022) in 41 Asian countries
Rahman, Shiddik. BMJ Open, 16(2), e108632 (2026)
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District-Level Dengue Early Warning using Hybrid XAI & Bayesian Deep Learning
Shiddik et al. Trop. Med. Infect. Dis., 11(3), 73 (2026)
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Spatiotemporal patterns of climate-sensitive vector-borne diseases in Bangladesh
Rahman, Shiddik, Chowdhury. BMC Medicine (2026)
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Global Dengue Prediction using XAI-Driven ConvLSTM integrating environmental & socio-economic data
Shiddik. Health Sci Reports, 9(4), e72280 (2026)
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Explainable AI in Healthcare: Current Landscape, Challenges & Future Directions
Shiddik. Health Sci Reports, 9(3), e72172 (2026)
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Stillbirth, Neonatal & Child Mortality in Bangladesh: Progress & Persistent Challenges
Rahman, Shiddik. Public Health Challenges, 5(3), e70312 (2026)
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AI to predict socioeconomic, environmental & healthcare drivers of tuberculosis globally
Rahman, Shiddik. Sci Rep 15, 13619 (2025)
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Dengue Early Warning System & Outbreak Prediction in Bangladesh using Interpretable Tree-Based ML
Rahman, Amrin, Shiddik. Health Sci Reports, 8(5), e70726 (2025)
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Explainable AI for predicting dengue outbreaks in Bangladesh using eco-climatic triggers
Rahman, Shiddik. Global Epidemiology, 100210 (2025)
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Unraveling global malaria incidence & mortality using ML-driven spatial analysis
Rahman, Shiddik. Scientific Reports, 15(1), 28334 (2025)
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Reflections on explainable AI for predicting dengue outbreaks in Bangladesh
Rahman, Shiddik. Global Epidemiology, 100230 (2025)
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Data-Driven Dengue Prevention Strategies in Bangladesh using XAI & Causal Inference
Rahman, Shiddik. Int. J. Statistical Sciences, 25(2), 101–113 (2025)
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XAI & spatial analysis for communicable diseases in Asia (2000–2022)
Rahman, Shiddik. Int. J. Health Geographics, 24(1), 45 (2025)
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Giving Back to the Field
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Reviewer
PLOS Digital Health→
ReviewerBMJ Global Health→
ReviewerBMJ Public Health→
ReviewerGlobal Public Health→
ReviewerHealth Science Reports→
ReviewerTechnology and Health Care→
ReviewerSpatial & Spatio-temporal Epidemiology→
ReviewerKnowledge-Based Systems→
Verified Reviewer · 27 reviews across 9 journals
Active peer reviewer for BMJ, PLOS & 7 other journals in global public-health & AI research since 2025.
Teaching & Community
Academic Content & Outreach
Video tutorials, community updates, and hands-on programming notes to complement the research.
Analytical Byte Scripts
Facebook Page
● Python
Python for ML & Data Science
A complete Python playlist covering data manipulation, visualization, and machine learning workflows for research applications.
● R Programming
R Programming & Visualization
Covers R programming fundamentals, statistical analysis, and data visualization using ggplot2 and tidyverse packages.
Open Science
Explore Open Data & Code Lab
Writing
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Beyond the Lab
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