Computing
200+
Publications
4000+
Citations
20+
Team Members
10+
Active Projects
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.
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.
Stay updated with the latest achievements, events, and announcements from the lab.
Aman Anand and Amir Eskandari were selected to attend the 2026 AI in Finance Summer School, organized by RBC Borealis at the University of British Columbia (UBC) in Vancouver, and both received financial support from RBC Borealis to participate. During the summer school, each presented a poster based on their published Transactions on Machine Learning Research (TMLR) paper: Aman presented 'ASMa: Asymmetric Spatio-temporal Masking for Skeleton Action Representation Learning,' while Amir presented 'InfGraND: An Influence-Guided GNN-to-MLP Knowledge Distillation.' Amir also took part in the program's challenge and won it. Congratulations to both Aman and Amir!
Our paper 'ElderBench: Benchmarking Personalized Open-Source LLMs for Older Adults,' was presented at the IEEE Annual Computers, Software, and Applications Conference (COMPSAC) 2026, as part of the Symposium on Cognitive Robotic Systems. The work introduces a benchmark for evaluating how well personalized, open-source large language models can understand and support the needs of older adults, contributing to the lab's broader research on AI-driven tools for healthy aging.
Our paper 'ASMa: Asymmetric Spatio-temporal Masking for Skeleton Action Representation Learning' by Aman Anand, Amir Eskandari, Elyas Rashno, and Dr. Farhana Zulkernine has been accepted at Transactions on Machine Learning Research (TMLR).
Our paper 'SK-DGCNN: Human activity recognition from point cloud data with skeleton transformation' by Zihan Zhang, Aman Anand, and Dr. Farhana Zulkernine has been accepted at Elsevier's Machine Learning with Applications journal.
Explore our latest contributions to top-tier conferences and journals, showcasing our ongoing commitment to academic excellence and scientific discovery.
Yu, E., Tian, H., Mohamad, F., Schultz, K., McEwen, L., Gauthier, S., Braund, H., Cofie, N., Dalgarno, N., Szulewski, A., Zulkernine, F., Kwan, B. Y. M.
Published in: Canadian Medical Education Journal
Mohamad, F., Zulkernine, F.
Published in: CVR-CIAN Conference 2025: The Brain and Integrative Vision, York University (Poster Abstracts)
Mohamad, F., Zulkernine, F., Sears, K.
Published in: IEEE/ACM Conference on Connected Health (CHASE)
Eskandari, A., Tao, J., Zulkernine, F., Morningstar, M., Poppenk, J., Herrmann, B.
Published in: IEEE Annual Computers, Software, and Applications Conference (COMPSAC)
