Peter Norvig is a Fellow of the American Association for Artificial Intelligence and the Association for Computing Machinery. At Google Inc he was the Director of Search Quality, responsible for the core web search algorithms from 2002-2005, and has been a Director of Research from 2005 on.

Previously he was the head of the Computational Sciences Division at NASA Ames Research Center, making him NASA's senior computer scientist. He received the NASA Exceptional Achievement Award in 2001. He has taught at the University of Southern California and the University of California at Berkeley, from which he received a Ph.D. in 1986 and the distinguished alumni award in 2006. He was co-teacher of an Artifical Intelligence class that signed up 160,000 students, helping to kick off the current round of massive open online classes. He has over fifty publications in Computer Science, concentrating on Artificial Intelligence, Natural Language Processing and Software Engineering, including the books Artificial Intelligence: A Modern Approach (the leading textbook in the field), Paradigms of AI Programming: Case Studies in Common Lisp, Verbmobil: A Translation System for Face-to-Face Dialog, and Intelligent Help Systems for UNIX. He is also the author of the Gettysburg Powerpoint Presentation and the world's longest palindromic sentence.

There has been a recent flurry of websites and online courses designed to teach more people to be competent at programming computers. In this talk we look at some of the data that is available on what works and what doesn't work, and in particular at the features of Python that come into play when it is used as a vehicle for learning programming and computer science.

Fernando Pérez received his PhD in theoretical physics from the University of Colorado and did his post-doctoral work there in applied mathematics, working on fast algorithms for partial differential equations. He is currently a research scientist at UC Berkeley’s Helen Wills Neuroscience Institute, focusing on the development of new analysis methods for brain imaging problems and high-level scientific computing tools.

Towards the end of his graduate studies, he became involved with the development of Python tools for scientific computing. He started the open source IPython project in 2001 when he needed an efficient interactive workflow for everyday scientific tasks. He continues to lead IPython, as part of a growing team of talented developers.

He remains committed to the development of open, high-level tools to tackle the current challenges in computationally-based scientific research and education across disciplines. He is a member of the matplotlib development team and has contributed to numpy, scipy, sympy, mayavi, nipy and nitime. He regularly organizes workshops and lectures aimed at teaching the use of these tools to audiences at levels ranging from high-school students to research scientists. He is also a member of the Python Software Foundation.

When not glued to a computer, Fernando tries to spend as much time as possible with his wife outdoors hiking and backpacking, as well as climbing. For more information, see http://fperez.org.

IPython has evolved from an enhanced interactive shell into a large and fairly complex set of components that include a graphical Qt-based console, a parallel computing framework and a web-based notebook interface. All of these seemingly disparate tools actually serve a unified vision of interactive computing that covers everything from one-off exploratory codes to the production of entire books made from live computational documents. In this talk I will attempt to show how these ideas form a coherent whole and how they are represented in IPython's codebase. I will also discuss the evolution of the project, attempting to draw some lessons from the last decade as we plan for the future of scientific computing and data analysis.

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