Join in for the Business Summit’s roundtable discussion with participation from IBM, Capital One, the DoD, AWS, Oracle, and others. Speakers will discuss important issues in our current environment—everything from compliance and GDPR to ML models.
You’ll have a chance to submit your questions in advance to the moderator.
Note: The Q&A for this roundtable is intended as dialogue among practitioners. We ask that members of the press hold their questions for other opportunities outside of the Business Summit.
David Schaaf is a director of data engineering at Capital One, where he leads data product development within the Financial Services Division. As part of his role, he guides agile teams to build data products for analyst and data communities with a primary focus on enabling self-service analytics, exploration, and insight discovery. David’s teams typically design data products using microservices, Angular, and Python and leverage core CI/CD practices for continuous delivery. David has more than 15 years of experience in software engineering and data analytics. He also has a wide breadth of knowledge across the financial services domain and in the retail industry. As a developer and analyst, David’s greatest interest is solving unique, complex problems and developing others as software and data engineers.
Julia Lane is a professor at the NYU Wagner Graduate School of Public Service and the NYU Center for Urban Science and Progress as well as a NYU provostial fellow for innovation analytics. Previously, Julia was a senior managing economist and institute fellow at American Institutes for Research, where she cofounded the Institute for Research on Innovation and Science (IRIS) at the University of Michigan. Over her career, Julia has held positions at the National Science Foundation, the Urban Institute, the World Bank, American University, and NORC at the University at Chicago.
Dan Romuald Mbanga is a global lead business development manager at AWS, where he leads business and technical initiatives involving Amazon AI platforms such as Amazon SageMaker, designed to provide end-to-end machine learning environments for AWS’s customers. He helps AWS customers in all GEOs, as well as internal AWS stakeholders across data science, product development, marketing, sales, and technical support achieve success with AWS’s machine and deep learning technologies. Previously, Dan was a big data and DevOps engineering manager at AWS, where he built and led two teams of specialized engineers on the Hadoop ecosystem and in CI/CD technologies. Dan holds BS degrees in physics and computer science from the University of Buea. In his spare time, he enjoys traveling, hacking hardware electronics, and learning new languages.
Dave Stuart is a senior product manager at the US Department of Defense, where he is leading a large-scale effort to transform the workflows of thousands of enterprise business analysts through Jupyter and Python adoption, making tradecraft more efficient, sharable, and repeatable. Previously, Dave led multiple grass-roots technology adoption efforts, developing innovative training methods that tangibly increased the technical proficiency of a large noncoding enterprise workforce.
Tianhui Michael Li is the founder and president of the Data Incubator, a data science training and placement firm. Michael bootstrapped the company and navigated it to a successful sale to the Pragmatic Institute. Previously, he headed monetization data science at Foursquare and has worked at Google, Andreessen Horowitz, J.P. Morgan, and D.E. Shaw. He’s a regular contributor to the Wall Street Journal, Tech Crunch, Wired, Fast Company, Harvard Business Review, MIT Sloan Management Review, Entrepreneur, Venture Beat, Tech Target, and O’Reilly. Michael was a postdoc at Cornell Tech, a PhD at Princeton, and a Marshall Scholar in Cambridge.
Pramit Choudhary is a Lead data scientist/ML scientist at h2o.ai, where he focuses on optimizing and applying classical machine learning and Bayesian design strategy to solve large scale real-world problems.
Currently, he is leading initiatives on figuring out better ways to generate a predictive model’s learned decision policies as meaningful insights(Supervised/Unsupervised problems)
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