Abstract:
This talk explores a novel approach to speeding up complex database queries by combining two powerful ideas: factorization and vectorized execution. Instead of processing large intermediate results (which can be slow and memory-intensive), the method represents data in a compact, factorized form that avoids redundancy. It also applies vectorized processing techniques to process data efficiently at the hardware level.
Speaker Bio:
Sunny Yasser is a PhD student in Computer Engineering at Polytechnique Montréal, in the Data and AI Systems (DAIS) Lab under Prof. Amine Mhedhbi. He is also associated with MILA, the premier AI research lab started by Prof. Yoshua Bengio. His research centers on high-performance query processing, with an emphasis on compression-aware execution on modern hardware. His broader work explores low-level system optimizations for scalable analytical and AI-driven data workloads. He has published papers at VLDB and SIGMOD previously.
