Dr. Wood is an associate professor of computer science at the University of British Columbia. Dr. Wood has also been an associate professor of information engineering at the University of Oxford, assistant professor of statistics at Columbia University, and postdoctoral fellow of the Gatsby Computational Neuroscience Unit of the University College London. His PhD in computer science is from Brown and his BS from Cornell. Dr. Wood has raised over $5M from DARPA, BP, Google, Intel, and Microsoft*. Prior to his academic career Dr. Wood was a successful entrepreneur.
In collaboration with NYU, LBNL, and Intel we propose to use probabilistic programming techniques to filter and explain high energy physics experiments by automatically inferring the structure of events, including the particles produced and their properties, directly from observed experimental results. The physics community already has a series of simulation software tools both for the underlying physics and for the modeling of the interaction of the underlying particles with experimental detector. Bringing these together using probabilistic programming powered by massively parallel high performance computing will enable us to tackle the fundamental inference problem in particle physics directly for the first time, offering a new way for particle physicists to tackle the detection of novel physics signatures, ultimately at Large Hadron Collider data and computation scale.