🔍 Research Interests
🤖 Artificial Intelligence & Machine Learning
I am passionate about developing data-driven and AI-powered solutions that are interpretable, reliable, and impactful. My work focuses on:
- Predictive Modeling & Statistical Learning – building robust ML models for classification, forecasting, and decision-making.
- Explainable AI (XAI) – improving model interpretability to ensure transparency and trust in real-world applications.
- Human-Centered AI – designing systems that keep humans in the loop, ensuring fairness, accountability, and ethical deployment.
💰 FinTech & Financial Analytics
I explore how AI and data science can transform financial systems by improving decision-making, reducing risk, and driving innovation. My interests include:
- Credit Risk Modeling and Fraud Detection using machine learning and anomaly detection techniques.
- Algorithmic Trading & Portfolio Optimization powered by statistical modeling and reinforcement learning.
- Financial Inclusion – leveraging AI to create accessible, data-driven financial services for underserved populations.
🏥 Public Health & Social Good
I aim to apply statistical and machine learning methods to improve healthcare outcomes and public policy. My research explores:
- Epidemiological Modeling – understanding patterns of disease spread and intervention effectiveness.
- Health Data Analytics – extracting insights from clinical, demographic, and behavioral data for better decision-making.
- Fair & Safe AI in Healthcare – mitigating model biases and ensuring equitable, patient-centered applications.
📑 Journal Papers
Bayesian Approaches to Explainable AI for Clinical Decision Support
Md. Sawrab, Co-authors
Journal of Biomedical Informatics, 2025 ▪ [DOI] ▪ Healthcare AI ▪ Explainability ▪ Bayesian Modeling
Causal Inference for Credit Risk Modeling in Emerging Markets
Md. Sawrab, Co-authors
Quantitative Finance and Economics, 2025 ▪ [DOI] ▪ FinTech ▪ Risk Analysis ▪ Causal ML
Data-Driven Epidemiological Forecasting: A Comparative Study of ML Models for Infectious Disease Spread
Md. Sawrab, Co-authors
International Journal of Public Health Data Science, 2025 ▪ [DOI] ▪ Public Health ▪ Time Series ▪ Predictive Analytics
📰 Conference Papers
Understanding Sampling Strategies for Reliable Machine Learning
Md. Sawrab
NeurIPS 2025 (Oral, Top 1%) ▪ [PDF] ▪ Code ▪ Data ▪ ML ▪ Statistics
AI-Driven Forecasting for Public Health: A Data-Driven Approach
Md. Sawrab, Co-authors
ICML 2025 ▪ [Preprint] ▪ Public Health ▪ Time Series ▪ Epidemiology
Risk-Aware Models for Credit Scoring and Financial Inclusion
Md. Sawrab, Co-authors
KDD 2025 ▪ [PDF] ▪ Dataset ▪ FinTech ▪ Responsible AI
🧪 Ongoing Research Projects
- Trustworthy AI for Healthcare – Designing interpretable, uncertainty-aware ML models to support clinical decision-making.
- Causal ML in Financial Systems – Applying causal inference to improve credit scoring and reduce bias in lending decisions.
- Epidemiological Modeling with ML – Forecasting infectious disease spread and intervention impact using time series and ML pipelines.
🤝 Research Collaborations
I actively collaborate with peers and researchers on:
- AI Safety & Interpretability – Exploring alignment techniques and explainability frameworks.
- Graph Neural Networks (GNNs) – Applying GNNs to healthcare, supply chain analytics, and financial data modeling.
- Multilingual NLP – Understanding language model generalization for low-resource and regional languages.