Lindner Center for Business Analytics Data Science Symposium 2017

TThe Data Science Symposium 2017, hosted by the Lindner College of Business’ Center for Business Analytics, will showcase presentations from three thought leaders in data science, with each presenter covering technical use cases. A panel discussion will take place from noon to 1 p.m. The event will also include opportunities for networking with other leading analytics professionals and graduate students in the Business Analytics and Information Systems programs.

Attendees will receive continental breakfast, a box lunch and free parking.

There will be a panel discussion featuring the three speakers from noon-1 p.m. For more information on the speakers, scroll down.

Tuesday, Oct. 10
8 a.m. - 1:30 p.m.

Carl H. Lindner College of Business
2925 Campus Green Dr.
University of Cincinnati Main Campus - Room 112
Cincinnati, OH 45221



RSVP Deadline: Oct. 4


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Lindner Center for Business Analytics Data Science
2017 Symposium
Tuesday, October 10, 2017 at 8:00 AM
Guest: 1 $150.00 ea.

I wish to give an additional contribution of:  $

Speakers include:

Joseph Blue, Data Scientist and Director of Global Data Science MapR Technologies

Title: "Are you Using All of Your Data?"

Abstract: Every piece of information that your business generates has potential to add value. This session is composed of real-world examples that demonstrate how that value may manifest, meant to provoke a review of your own data to identify new opportunities. Vivid accounts from the front line will be presented that illustrate how data limitations (silos, scaling issues, unstructured data) have been overcome to create an orchestration layer which serves multiple use cases. Examples are drawn from media consumption, advertising, financial transactions and healthcare.

Bio: In his role Director of Data Science at MapR, Joe assists businesses in solving their big data problems, making efficient use of the MapR platform to generate tangible results. Recent projects include debit card fraud & breach detection, lead generation from social data, forecasting television audiences, analyzing live patient data and lookalike modeling using browser history.

Prior to MapR, Joe was the Chief Scientist for Optum (a division of UnitedHealth) and the principal innovator in analytics for healthcare. As a Sr. Fellow with OptumLabs, he applied machine learning concepts to healthcare issues such as disease prediction from co-morbidities, estimation of PMPY (member cost), physician scoring and treatment pathways. As a leader in the Payment Integrity business, he built the predictive scoring machine responsible for saving more than $1B in claim overpayments.

Mark Wolff, Ph.D. Chief Health Analytics Strategist Advisory Industry Consultant Chief Health Analytics Strategist Health & Life Sciences Global Practice SAS Institute

Title: "Rise of the Intelligent Internet of Things: Opportunities and Challenges of Connected People, Environments and Machines"

Abstract: Rapid advances in the development of the Internet of Things, the Industrial Internet, and Event Stream Processing are creating opportunities and challenges in efficiently capturing, processing and analyzing high volume and high velocity data in support of real time decision making. Pharmaceutical, Biotechnology, Diagnostic and Medical Device companies are increasing their use of sensor data and IoT connectivity to improve and optimize processes in clinical research, development, sales, marketing and manufacturing. In Healthcare, connected sensors and IoT enabled devices are being used to develop next generation, intelligent clinical decision support systems as well as improving patient experience, health outcomes, safety and managing the cost of care. In public health, data from sensor networks are being used to address challenges such a food/crop safety, disease/pandemic outbreaks and natural disasters. The rapid adoption of this technology presents challenges and risks related to the quality and integrity of streaming sensor data. That risk becomes even greater when considering the development and adoption of “autonomous” IoT devices in decision support applications. As we adopt these technologies for decision making, it will be necessary to develop powerful and predictive analytic methods to monitor, alert and maintain the integrity and veracity of data streams and proactively alert decision makes as to potential errors, malfunctions and security threats.

Bio: Dr. Wolff has over 20 years of experience in the health and life science industries as a scientist and analyst working in the U.S. and Europe. Mark joined SAS in 2005 and is an Advisory Industry Consultant within in the Health and Life Sciences Global Practice. Mark’s areas of expertise at SAS include the development and application of advanced and predictive analytics in the life sciences and healthcare with a particular interest in patient safety and outcomes. Current work focuses on methods and application of unstructured data and text analytics in support of safety/outcomes research, visualization and development of intelligent, decision support systems for healthcare. Prior to joining SAS Mark held a variety of research and leadership positions in academia, government and industry. He holds a Bachelor of Science degree from Loyola College in Maryland, a Master of Science and a Doctorate in Toxicology from North Carolina State University.



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