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Splash Spring 2019 is May 4-5, 2019!

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ESP Biography



GEORGE JOHN, Stanford Alum, Investor, Teacher, CEO




Major: Computer Science

College/Employer: Stanford

Year of Graduation: 1997

Picture of George John

Brief Biographical Sketch:

George earned a BS, MS, and PhD with distinction from the Computer Science department at Stanford. His thesis focused on Machine Learning, and one of his papers on the topic of relevance became a top ten most-cited paper in global AI and ML literature. While at Stanford, he won a National Science Foundation fellowship and worked at NASA, earning his “rocket scientist” credentials. He has served as a reviewer for many journals and advised the NSF on its investment in AI research. At Stanford he taught a number of courses in Computer Science and AI, and now teaches entrepreneurial science to MBA and engineering students at Stanford.

He is a past CEO and Chairman of Rocket Fuel, board member of the Ad Council and a volunteer at Reading Partners, Girls STEM Stars, and Loc8Don8, a nonprofit founded by his daughters. He also served on the boards of directors of many startups using AI to benefit humanity, through his role as an operating partner at Khosla Ventures.



Past Classes

  (Clicking a class title will bring you to the course's section of the corresponding course catalog)

M7079: Human and Computer Vision in Splash Spring 2019 (May. 04 - 05, 2019)
The class covers human vision, computer image representation and manipulation, image recognition, and deep learning with convolutional neural networks. We'll use Python, Jupyter Notebooks, OpenCV, and TensorFlow. The class is taught in a lecture style and the focus is on exposing the students to these topics generally but concretely in the hope that they continue to explore these concepts after the class. For example, we'll actually train a neural network to recognize images during the class, but won't go through all of the behind-the-scenes math and computation. We'll also look at examples of cutting-edge startups that are using computer vision in healthcare, farming, and other applications. Handouts will give students pointers to online resources and open source software.


M6237: Human and Computer Vision in Splash Spring 2018 (May. 05 - 06, 2018)
The class covers human vision, computer image representation and manipulation, image recognition, and deep learning with convolutional neural networks. We'll use Python, Jupyter Notebooks, OpenCV, and TensorFlow. The class is taught in a lecture style and the focus is on exposing the students to these topics generally but concretely in the hope that they continue to explore these concepts after the class. For example, we'll actually train a neural network to recognize handwriting during the class, but won't go through all of the behind-the-scenes math and computation. We'll also look at examples of cutting-edge startups that are using computer vision in healthcare, farming, and other applications. Handouts will give students pointers to online resources and open source software.


M5938: Human and Computer Vision in Splash Fall 2017 (Nov. 11 - 12, 2017)
The class covers human vision, computer image representation and manipulation, image recognition, and deep learning with convolutional neural networks. We'll use Python, Jupyter Notebooks, OpenCV, and TensorFlow. The class is taught in a lecture style and the focus is on exposing the students to these topics generally but concretely in the hope that they continue to explore these concepts after the class. For example, we'll actually train a neural network to recognize handwriting during the class, but won't go through all of the behind-the-scenes math and computation. We'll also look at examples of cutting-edge startups that are using computer vision in healthcare, farming, and other applications. Handouts will give students pointers to online resources and open source software.


M5522: Human and Computer Vision in Splash Spring 2017 (Apr. 22 - 23, 2017)
The class covers human vision, computer image representation, computer image manipulation and generation (both simple and fractal patterns), image search, and image recognition using deep learning with Google's inception network. This is a huge amount to cover in a single class, so the class is taught in a lecture style and the focus is more on exposing the students to these topics versus attaining any specific proficiency. Handouts will give students pointers to online resources and open source software in the hope that they continue to explore these concepts after the class.