Azure Networking

Bin is a Seattle-area software studio focused on advanced visualization for science, engineering, and education. We specialize in the use of novel visualization techniques applied to the full spectrum of modern and upcoming devices and platforms.Our primary work focuses on data-intensive visualization, simulation and modeling using the emerging generation of affordable VR, AR, and holographic hardware.Our public-facing demo of some of these capabilities is called 6000 Moons. It is an accessible demonstration of our visualization engine's capabilities, as well as a demonstration of the educational and outreach potential of these devices and techniques. A well-received early version of this work has been released for the Oculus Rift, but we continue to refine it to explore and demonstrate the capabilities of other immersive/holographic platforms.We are currently deeply focused on bringing these capabilities to researchers, industry domain specialists, and educators to incorporate immersive visualization systems and techniques into their engineering, scientific, and teaching work.

Member of the Bing Core Relevance team. Responsible for designing and building systems that improved the pace and quality of innovation in search relevance. Responsibilities included:• Architectural oversight of a machine learning and data mining coordination system supporting relevance experimentation. This system efficiently dispatched thousands of compute hours of work across thousands of machines.• Architecture, design, project management, and development of a new tool set for deep diagnostics and visualization of query serving and machine learned model behavior.

Member of the Technical Computing Group. Worked on startup/incubation products focused on cloud execution of large-scale parallel scientific and technical workloads.Work included the design and implementation of a number of distributed service components for running and maintaining a large-scale compute service running on Windows Azure, built for redundancy, fault recovery, and arbitrary scale-out to thousands of compute nodes. This included the implementation of a scheduling system for distributing large, compute-intensive work graphs typical in scientific and technical computing scenarios.

Member of the Live Search/Bing Relevance Team. Led a team focused on applied machine learning. Shipped a number of significant improvements to the measured quality of web search results. Extensive data mining, development of new relevance signals, and refinement of training data, structure, and training process for machine learned models.

MSN Money: Worked on backend transaction processing systems, box product feature work, and bill payment partner integration.MSN Message Boards: Member of development team building threaded discussion solution for multiple MSN and Microsoft properties. Focused on content quality facilitation, scalability, performance optimization, and stability.