Logo
Queen's University LogoComputing

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
2026Journal
ASMa: Asymmetric Spatio-temporal Masking for Skeleton Action Representation Learning

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

Published in: Transactions on Machine Learning Research (TMLR)

2026Journal
InfGraND: An Influence-Guided GNN-to-MLP Knowledge Distillation

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

Published in: Transactions on Machine Learning Research (TMLR)

2026Journal
SK-DGCNN: Human activity recognition from point cloud data with skeleton transformation

Zhang, Z., Anand, A., Zulkernine, F.

Published in: Machine Learning with Applications

2026Conference
DepthPulse+: A Depth and Vital Sign Based Method for Face Presentation Attack Detection

Sadman, N., Alaca, F., Zulkernine, F.

Published in: IEEE International Conference on Communications (ICC) (Communication and Information Systems Security)

A thanks to our current and previous sponsors

Icon
Icon
Icon
Icon
Icon
Icon
Icon
Icon
Icon
Icon
Icon
Icon
Icon
Icon
Icon
Icon
Icon
Icon