AADR Lab

Alam AI & Data Research Lab

AADR Lab (Alam AI & Data Research Lab) envisions becoming a research hub that advances intelligent, trustworthy, and impactful data-driven solutions. Our vision is to empower the next generation of researchers and innovators by fostering rigorous research in Artificial Intelligence and Data Science, while promoting ethical, privacy-aware, and socially responsible AI systems. We aim to bridge academia and real-world challenges by developing scalable, interpretable, and efficient AI models that address critical problems in healthcare, intelligent systems, and emerging technologies. Through international collaboration, interdisciplinary research, and mentorship, AADR Lab aspires to cultivate research excellence, innovation culture, and global academic partnerships.

***Open for research collaboration/supervision***

Publications

2026

(Under Review)

"Emotion AI on the edge: a TinyML-driven framework for speech emotion recognition in social environments". Expert Systems (Q2, IF: 2.3)
"Enhancing land use and land cover classification through comparative analysis of deep learning architectures". Ecological Informatics (Q1, IF: 7.3)
"A Linguistic Lens into Vision-Language Models for Open-ended Question-Answers in Medical Visual Question Answering". Digital Health (Q2, IF: 3.3)
"Money Laundering Detection Using Machine Learning with Explainable AI Insights". Women in Data Science Conference (WiDS), Bangladesh

2025

"An approach for crop recommendation with uncertainty quantification based on machine learning for sustainable agricultural decision-making”. Results in Engineering. (Q1, IF: 7.9). DOI: https://doi.org/10.1016/j.rineng.2025.105505
"A Privacy-Preserving Federated Learning Framework with Graph Neural Networks for Enhanced Heart Attack Risk Prediction”. Array. (Q1, IF: 4.5). DOI: https://doi.org/10.1016/j.array.2025.100500

Ongoing Research

🔏Privacy-Preserving Multimodal Clinical Intelligence

The project aims at developing secure machine learning models for multimodal healthcare data, including medical images, physiological signals, and electronic health records. It employs federated learning and differential privacy to enable collaborative model training without sharing sensitive patient data. The research also emphasizes model interpretability and efficiency to ensure real-world clinical usability. The ultimate goal is to build trustworthy AI systems that protect privacy while improving healthcare decision-making.

News

[Feb 2026] AADR Lab Director Md. Sakib Bin Alam has commenced his PhD in Artificial Intelligence at Griffith University, focusing on advancing research in privacy-preserving and trustworthy AI systems.

Email: sakibsba.cs@gmail.com