Tonight we have two guest lecturers:
First we have Professor Mark Hansen, the new director of the David and Helen Gurley Brown Institute for Media Innovation at Columbia.
Mark joined the Columbia Journalism School in July of 2012, after a decade of shuttling between the west and east coasts. In Los Angeles, he held appointments in the Department of Statistics, the Department of Design Media Arts and the Department of Electrical Engineering at UCLA — literally forming a triangulation of data, art and technology — and was a Co-PI for the Center for Embedded Networked Sensing, an NSF Science and Technology Center devoted to the study of sensor networks. While in New York, Hansen was a long-standing visiting researcher at the New York Times R&D Lab and a consultant with HBO Sports. Hansen works with data in an essentially journalistic practice, crafting stories through algorithm, computation and visualization. In addition to his technical work, Hansen also has an active art practice involving the presentation of data for the public. His work with Ben Rubin at EAR Studio has been exhibited at the Museum of Modern Art in New York, the Whitney Museum, the Centro de Arte Reina Sofia, the London Science Museum, the Cartier Foundation in Paris, and the lobby of the New York Times building (permanent display) in Manhattan. Hansen holds a PhD and MA in Statistics from the University of California, Berkeley and a BS in Applied Math from the University of California, Davis.
Next up, we’ll have Ian Wong, who is in town from San Francisco.
Ian is an inference scientist at Square, where he builds tools to assess risk of entities and events in the payment network. Prior to Square, he did research on graphical models in his PhD studies at Stanford, where he received BS and MS degrees in Electrical Engineering, and MS in Statistics. Ian also worked as a data scientist at Facebook, a management consultant for Bain & Co., and an investment banker for Credit Suisse. He currently spends most of his time making machine learning work.
He will present a case study on how the Risk team at Square uses machine learning and data visualization techniques to fuel Square’s growth and provide world-class customer experience while ensuring the security of Square’s platform.
