Keynote Speakers

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.

Ashi Krishnan

Ashi is a visual poet who has been telling stories with code and words since she was a child—perhaps before. She has worked at seven-person startups, fought fires in the trenches of SRE at Google, and spent the last three years teaching at coding bootcamps. She now works as a senior software engineer at GitHub, where she hopes to dissolve the walls between us and our tools. She is learning to regard every moment, and the creatures within them, with love.

Keynote: Learning from Machines

Why can't you tickle yourself? How do you know where you are? Why do DeepDream images look so trippy? Why does trauma come in waves, washing over us again and again? Computational neuroscience provides insight into these questions and more. In visually lush presentation, I'll take us on a journey through biological and artificial minds, exploring how models of cognition informed by machine learning and computation can help us illuminate and reconfigure our own processes of being.

Maryam Jahanshahi

Maryam runs research at TapRecruit, a NY-based startup that is developing tools to implement evidence-based talent management. TapRecruit's research program integrate recent advances in NLP, data science and decision science to identify robust methods to reduce bias in talent decision-making and attract more qualified and diverse candidate pools. In a past life, Maryam was a cancer scientist where she researched how growing organs 'know' they've reached the right size. She is originally from Melbourne, Australia. Outside of work, she enjoys woodworking, and teaches laser cutting.

Keynote: Using Data Science to Recruit Data Scientists

Although data-driven approaches have become commonplace in many business decisions, hiring remains a (dark) art, subject to many minor snap judgments that are proven to impact the fairness and outcomes of the process. At TapRecruit, we combine natural language processing, data, and decision sciences, to interrogate these 'common sense' judgments and mitigate their impact when hiring for tech and data science roles. This talk will discuss the unique challenges of parsing and interpreting career language data and the results of combining analyses of millions of jobs (and their outcomes) with behavioral experiments. You will leave with concrete advice on how to hire data scientists and data engineers more fairly and efficiently.

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