Keynote Speakers

Jessica Stauth

Dr. Jessica Stauth is the managing director of portfolio management and research at Quantopian, a crowd-sourced quantitative investment firm, that inspires talented people from around the world to write investment algorithms. Jess and her team are in charge of selecting the algorithms from the Quantopian community, for the Quantopian portfolio.

Quantopian offers license agreements for algorithms that fit certain investment criteria, and licensing authors are paid based on their strategy's individual performance. Previously, she has worked as an equity quant analyst at the StarMine Corporation and as a Director of Quant Product Strategy for Thomson Reuters prior to joining Quantopian in August of 2013. Jess holds a PhD from UC Berkeley in Biophysics.

Keynote: A beginner’s guide to being open (source) in the traditionally secretive field of quantitative finance

The field of quantitative finance is intensely competitive and maniacally secretive as a rule. The tendency toward secrecy is perhaps unsurprising given that the smallest of competitive advantages can translate to substantial profits. Indeed, over the past decade a growing list of legal prosecutions for alleged code theft or misuse have underscored how high the stakes can be for developers looking to leverage and contribute to open source projects. Notable exceptions to this approach include work from Wes McKinney and Travis Oliphant, whose work on open source projects like pandas and numpy, which have gained widespread adoption. In this talk we will review some of the costs and benefits of engaging with open source as a “two way street” and frame the modern quant workflow as a mosaic of open sourced, third party, and proprietary components.

Travis E. Oliphant

Travis E. Oliphant is a Founder and CEO/CTO of Quansight, a company that grows talent, builds technology, and discovers new products while sustaining open-source communities by helping companies gain actionable, quantitative insights from their data. Travis previously co-founded Anaconda Inc. (was Continuum Analytics) and served as its CEO. He continues as a Director of Anaconda, Inc. Since 1997, he worked with Python for numerical and scientific programming, notably as the primary developer of the NumPy package and as a founding contributor of the SciPy package. He is also the author of the definitive "Guide to NumPy".

Mr. Oliphant was an assistant professor of Electrical and Computer Engineering at BYU from 2001-2007, where he taught courses in probability theory, electro-magnetic, inverse problems and signal processing. He also served as Director of the Biomedical Imaging Lab, where he researched satellite remote sensing, MRI, ultrasound, elastography and scanning impedance imaging. From 2007-2011, Travis was the President at Enthought, Inc. Travis has worked with Fortune 50 companies in finance, oil-and-gas and consumer-products. He has been involved in all aspects of the contractual relationship, including consulting, training, code-architecture and development. He engages customers, develops business strategy and guides technical direction of projects. He actively contributes to software development and engages with the wider open source community in the Python ecosystem. Travis has a Ph.D. from the Mayo Clinic and B.S. and M.S. degrees in Mathematics and Electrical Engineering from Brigham Young University.

Keynote: The Business of Open Source

Open Source is eating the world especially in Data Science and Machine Learning. In this talk, I review some of the business models around open source based on my direct and indirect experience. I discuss some of the opportunities that connecting business and open source can bring to the world as well as the challenges introduced both to businesses and to open source communities. Finally, ideas for how to organize business activities around open source software in ways that are supportive rather than exploitative are provided in an effort to ensure that open source communities continue to thrive and grow.

Katharine Hyatt

Keynote: Why I Use Julia For Quantum Physics

Working on the "many electron problem" often requires writing code which is both readable and fast. Although Julia isn't the only language which can provide both these features, I use it often to research a variety of problems in condensed matter physics. In this talk, I'll discuss the sort of systems we would like to understand better, and why I've found Julia a useful tool to approach them. I'll also discuss what it is about the Julia community at large I feel is great for scientists (and non-scientists!), and how programmers of any skill level can get involved.

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