Brandon Haynes

关于

I’m a Principal Scientist in the Gray Systems Lab (GSL), where we design novel database system technologies with a focus on translating cutting-edge research into production within Azure Data. My work spans the design, implementation, and performance optimization of large-scale data-intensive systems, with a growing emphasis on leveraging modern hardware (e.g., GPUs) and agentic approaches to accelerate analytics for large-scale and heterogeneous data workloads.

I received my Ph.D. and M.Sc. from the Paul G. Allen School of Computer Science at the University of Washington, where my research focused on video analytics and video data management, particularly for emerging AI-driven systems and large-scale multi-camera networks. I continue to collaborate with the University on advancing video analytics and related areas, exploring AI-driven and agentic (LLM-based) approaches to improving the intelligence and adaptability of video data systems. I also hold a master’s degree from Harvard University and a B.Sc. from the University of Illinois at Urbana–Champaign.

My work has been recognized with several awards (e.g., Best Research Paper Runner-Up at VLDB 2023 and Best Demo Honorable Mention at SIGMOD 2017), and I serve regularly on program and review committees for top-tier data management venues such as SIGMOD and VLDB. I will serve as proceedings chair for SIGMOD 2027.