Problem-led
Begin with consequential clinical and health-system needs.
Bridging Informatics in Health
I work at the intersection of clinical medicine, epidemiology, and health informatics, applying digital health, real-world health data, and artificial intelligence to strengthen disease surveillance, support clinical decision-making, and improve health system performance. My experience spans clinical practice, clinical trials, public health surveillance, and national health programmes, with a particular interest in translating research into scalable digital solutions.
I am interested in interdisciplinary research that integrates health informatics, implementation science, and data-driven analytics to address complex healthcare challenges and improve population health, particularly in low- and middle-income settings.
About me
I am a physician–researcher with more than eight years of interdisciplinary experience spanning clinical medicine, field epidemiology, clinical research, disease surveillance, and public health programme implementation. Throughout my career, I have worked across frontline healthcare, large-scale randomized controlled trials, national disease surveillance systems, and government-led health programmes, contributing to evidence generation, health systems strengthening, and the implementation of digital health initiatives. These experiences have provided me with a practical understanding of how clinical care, public health, and health information systems intersect in real-world settings.
My work is driven by a simple observation: health systems generate vast amounts of valuable data, yet much of it remains underused for improving patient care, informing policy, and strengthening health systems. I am interested in bridging this gap by integrating clinical expertise with epidemiological methods, health informatics, and data science to transform routinely collected health data into actionable evidence that supports better clinical decisions, population health, and policy development.
Currently, I am pursuing the Joint Master's Programme in Health Informatics at Karolinska Institutet and Stockholm University as a Swedish Institute Scholar, where I am expanding my expertise in electronic health records, clinical decision support systems, health data analytics, interoperability, machine learning, and artificial intelligence for healthcare. My research interests lie at the intersection of medicine, epidemiology, digital health, and AI, with a particular focus on developing evidence-based, equitable, and implementable digital solutions that strengthen healthcare delivery and public health systems, especially in low- and middle-income countries.
Looking ahead, I aim to pursue a PhD in Health Informatics or a related discipline, contributing to translational research that connects clinical practice, health data, and emerging technologies to address complex healthcare challenges. I welcome opportunities for interdisciplinary collaboration in digital health, health data science, artificial intelligence, and implementation research.
View academic CV overviewBegin with consequential clinical and health-system needs.
Select methods that fit the question, context, and available evidence.
Design research with real workflows, constraints, and users in mind.
The question guiding my work
How can routinely collected health data, combined with responsible digital technologies, improve clinical decision-making, public health, and health-system performance in resource-constrained settings?
Research agenda
My research is grounded in practical health-system needs. I connect clinical context with rigorous methods to move from problem definition to implementable evidence.
Digital health systems
I study workflow, interoperability, data quality, and adoption to understand how electronic records and decision-support systems can serve clinicians and health managers.
Surveillance & epidemiology
I am interested in integrating routine and real-world data to improve disease surveillance, outbreak readiness, and population-health decisions.
Responsible analytics
I explore statistical learning and AI as tools for clinically relevant questions, with attention to validation, implementation, and the realities of low-resource settings.
Experience
My trajectory connects patient care, field epidemiology, government health services, and applied research.
Worked on health-system strengthening, workforce capacity, routine-data use, and surveillance infrastructure.
Supported the connection between health-service delivery, information systems, and public-health decision-making.
Contributed clinical and public-health expertise during the pandemic response.
Worked across NCD surveillance.
Worked across an iron-fortified lentil clinical trial.
Worked across micronutrient-supplementation research.
Research practice
A problem-solving toolkit spanning study design, analysis, digital health standards, and implementation.
Quantitative, qualitative, mixed-methods, action, and evaluation research.
Descriptive and inferential statistics, feature engineering, and supervised and unsupervised learning.
Workflow mapping, interoperability, terminology systems, EHRs, and clinical decision support.
Python, R, SPSS, Excel, Power BI, Tableau, and QGIS.
Academic preparation
Karolinska Institutet & Stockholm University, Sweden
Swedish Institute Scholarship for Global ProfessionalsInstitute of Epidemiology, Disease Control and Research, Bangladesh
Rangpur Medical College, University of Rajshahi, Bangladesh
Projects
A selection of research, digital health, and data science projects reflecting my interests in health informatics, epidemiology, and healthcare innovation.
Designing a FHIR-based clinical decision support platform integrating electronic health records, clinical workflows, and evidence-based recommendations.
View Project →Designing a comprehensive digital platform integrating electronic medical records, appointment scheduling, laboratory services, pharmacy, and telemedicine.
View Project →Statistical analysis and visualization of human anthrax surveillance data in Bangladesh using R, Python, and GIS for publication and public health decision-making.
View Project →Exploring machine learning and real-world health data to improve surveillance, risk prediction, and clinical decision support.
View Project →Curriculum vitae
A concise summary of my academic preparation, research experience, and methodological profile.
Download CVCurrent appointment
Karolinska Institutet & Stockholm University
Swedish Institute Scholar, 2025-2027
Academic background
Research domains
Technical profile
I welcome opportunities for research collaboration, interdisciplinary partnerships, and academic discussion. Please feel free to reach out via email.
Collaboration
I welcome conversations about doctoral research, health informatics, digital health, epidemiology, and interdisciplinary collaborations focused on strengthening healthcare systems and improving population health.
📧 School e-mail📍 Widerstomoska Huset, Karolinska Institutet, Solna, Stockholm, Sweden
📞 +46 767417088 (SE) 📞 +88 01745937644 (BD)