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BAM Lab

Big Data & Analytics Management Lab

Pioneering the future of intelligent systems through advanced research in big data, cloud computing, and cognitive science at Queen's University.

Queen's University

200+

Publications

4000+

Citations

20+

Team Members

10+

Active Projects

Research Focus Areas

Cutting-edge research spanning cognitive science, big-data analytics, and cloud computing infrastructures

AI & Cognitive Computing

Exploring the intersection of human cognition and artificial intelligence to create more intuitive and adaptive systems.

Big-Data Analytics

Developing scalable algorithms and frameworks to extract actionable insights from massive, complex datasets.

Cloud Computing

Optimizing distributed systems for performance, scalability, and security in next-generation cloud environments.

We have open positions

About This Lab

The Big Data & Analytics Management (BAM) Lab is a hub for innovation, where students and researchers collaborate to solve real-world problems using cutting-edge technologies.

Collaborative Environment

We foster a culture of teamwork and mentorship, encouraging cross-disciplinary collaboration among students and industry partners.

Well-Funded Research

Our projects are supported by leading grants and industry partnerships, providing resources for impactful research and development.

Diverse Research Topics

From healthcare analytics to autonomous systems, we tackle a wide range of challenges pushing the boundaries of technology.

BAM Lab Picnic

Latest News

Stay updated with the latest achievements, events, and announcements from the lab.

View All News

Recent Publications

Explore our latest contributions to top-tier conferences and journals, showcasing our ongoing commitment to academic excellence and scientific discovery.

All Publications
2025Conference
Depth-guided self-supervised human keypoint detection via cross-modal distillation

Anand, A., Rashno, E., Eskandari, A., Zulkernine, F.

Published in: International Conference on Machine Vision

2025Journal
CNN-CCA: A Deep Learning Approach for Anomaly Detection in Metro Rail Sensor Time-Series Data

Rao, V., Eskandari, A., Zulkernine, F., Helwa, M., Beach, D.

Published in: Machine Learning with Applications

2025Journal
A two-phase hybrid clustering framework exploring transitional activities in HAR

Woo, M., Abdulsalam, H.M., Zulkernine, F., Harby, A.A.

Published in: Discover Artificial Intelligence

2025Workshop
Transformer-based human action recognition using skeleton heatmap

Teng, A., Anand, A., Zulkernine, F.

Published in: International Conference on Current and Future Trends of Information and Communication Technologies in Healthcare (ITCH)

A thanks to our current and previous sponsors

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