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Machine Learning Trends Across Industries
Chicago Data Science

Tuesday, March 29, 2016
6:30 PM to 8:00 PM

What are the machine learning trends across industries?

Metis has invited a panel of data scientists to share their unique perspectives on how their companies and clients are applying machine learning and artificial intelligence to add value to existing products, and in some cases launch new ones.

Panelists from insurance, government, start-up, consultancy and academia will join a moderated discussion in this open event, during which guests will be encouraged to participate in the exchange during a Q&A.

Registration begins at 6:30pm followed by a panel discussion beginning at 7pm. Food and drink will be served.

Confirmed Panelists Include:

Tom Schenk Jr., Chief Data Officer, City of Chicago

Jeremy Watt, Machine Learning Consultant and Author of Machine Learning Refined

Bo Peng, Partner and Data Scientist, Datascope 

Martin Bucheim, Director of Data Science Lab, American Family Insurance

Drew Fustin, Lead Data Scientist, SpotHero

Moderator: Rumman Chowdhury, Sr. Data Scientist, Metis

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Tom Schenk Jr.
Chief Data Officer, City of Chicago

Tom Schenk is a researcher, author, and an expert in a number of fields, including open government, data visualization, business  and research and policy in education. He is currently the Chief Data Officer at the City of Chicago, which includes overseeing Chicago’s open data portal, advanced analytics team, and the City’s data and business intelligence team. He leads the strategic use of data to improve the efficiency of city operations and improve the quality of life for residents. Tom has lead the expansion of Chicago’s leading open data portal, deployed predictive analytics in the City to improve data services, and has streamlined the City’s data operations.

Jeremy Watt
Machine Learning Consultant and Author of Machine Learning Refined

Jeremy Watt holds a PhD in Computer Science and Electrical Engineering from Northwestern University where he conducted research in machine learning and computer vision while actively consulting with partners in finance and insurance, as well as startups in the e-commerce and healthcare space. Jeremy is a seasoned and passionate instructor of data science. In addition to authoring his own textbook on machine learning, titled Machine Learning Refined and published by Cambridge University Press, he has designed and taught several large university courses on machine learning, as well as large tutorial short courses on deep learning at major conferences on artificial intelligence and computer vision.

 

Bo Peng
Partner and Data Scientist, Datascope 

Bo is a Data Scientist at Datascope Analytics, where she has contributed to a variety of in-house apps such as Lunch? and Cheating Commish, in addition to projects for clients such as P&G, Thomson Reuters, and other well-known companies. She is passionate about exploring the root issues behind relevant problems, then using data to find solutions that ultimately improve the way people work and think.When not tweaking on code or thinking of creative ways to convey information, she enjoys strolling around the city, attending book club meetings, and making soupy gloops for dinner. Bo received a BS in mathematics with specialization in economics and a MS in statistics, both from The University of Chicago.

 

Martin Bucheim
Director of Data Science Lab, American Family Insurance

Marty is Director of the Data Science & Analytics Lab (DSAL) at American Family Insurance.   DSAL, which has teams in both Madison, WI and Chicago, is an advanced analytics and technologies team.  It is charged with melding new and existing sources of data with machine learning and other advanced analytical and computational capabilities to discover new, fundamental relationships or insights and further, to leverage these new insights to drive innovation, and value, at American Family.  Along the way Marty also hopes they will prove that insurance isn’t boring.

Marty has an M.S. and PhD in Photogrammetric Engineering and Remote Sensing from the University of Wisconsin-Madison, an MBA in Strategy and Finance from the University of Chicago, and a BA in Biology from Wartburg College.  He previously worked in the strategy consulting practice of IBM’s Global Business Services unit, and as a global research and analytics manager for global consumer products manufacturer Mars, Inc.

Drew Fustin
Lead Data Scientist, SpotHero

Drew is a reformed physicist with a heart for the Chicago tech scene. He currently serves as the Lead Data Scientist at SpotHero, where his responsibilities range from building a marketing attribution model and optimizing ad spend to creating a rate recommendation engine for parking garages to forecasting future company revenues. His prior experience includes a stint with GrubHub as the Insights Analyst, turning food facts into media content for the PR department and transforming data into actionable initiatives within the organization. In the startup space, he was a Data Scientist with Digital H2O, providing water intelligence for the oil/gas industry. He holds a PhD in physics from the University of Chicago, where he studied dark matter by looking for tiny bubbles in a chamber over a mile underground in a Canadian nickel mine. As you do.

Moderator:

Rumman Chowdhury
Sr. Data Scientist, Metis

Rumman comes to data science from a quantitative social science background. Prior to joining Metis, she was a data scientist at Quotient Technology, where she used retailer transaction data to build an award-winning media targeting model. She holds two undergraduate degrees from MIT, a Masters in Quantitative Methods of the Social Sciences from Columbia, and is currently finishing her Political Science PhD from the University of California, San Diego. Her dissertation uses machine learning techniques to determine a causal pathway for political capture by the military-industrial complex. Her passion lies in teaching and learning from teaching. In her spare time, she teaches and practices yoga, reads comic books, and works on her podcast.

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