October 2, 2017
| Time | Session Title |
|---|---|
| 7:30am | Registration |
| 8:45am | Introduction - Melissa Cragin, MBDH Executive Director |
| 9 - 9:10am | Welcome by Dave Hansen (Kiewit Corporation) | Slides |
| 9:10 - 9:30am | Welcome, Bill Gropp, Director of NCSA and MBDH PI | Slides |
| 9:35 - 10:30am | Panel 1: Spoke Projects: Project Reports from Funded Spoke PIs Moderator: Brian Athey (University of Michigan) - UASPSE - Travis Desell (University of North Dakota) | Slides - ACNN - Rich Gonzalez (University of Michigan) | Slides - IMaD - Ian Foster (University of Chicago) |
| 10:30am | Break |
| 10:45 - Noon | Keynote: Dr. Anthony Scriffignano, Senior Vice President and Chief Data Scientist for Dun & Bradstreet Making Decisions that Matter: Data Abounds. Data Confounds. Data is everywhere. In fact, presentations and articles about how data is everywhere are everywhere. The abundance of data itself is becoming difficult to conceptualize, arguably increasing at a rate that can no longer be accurately estimated because of confounding environments such as the Dark Web and Internet of Things. Risk and opportunity abound. Truth be told, nearly all organizations struggle to make sense out of the mounting data already within the four walls of the enterprise. There is great opportunity to do good and to prosper, informed by newly-available data and technology. Quite often though, data and technology are combined in alarmingly inappropriate or incomplete ways, or even nefarious ways. A tremendous amount of “dark” innovation continues in the space of fraud and other bad behavior (e.g. cyber crime, cyber terrorism). There are very real risks to taking a fast-follower strategy in making sense out of the ever-increasing, ever-changing data available. Fascinating inference can be derived if we ask the right questions (and maybe use a bit of different math!). This talk, both relevant and occasionally irreverent, will explore some of the new ways data is being used to expose risk and opportunity and the skills we need to take advantage of a curious world awash in data. |
| Noon - 1:30pm | Lunch & Networking |
| 1:30 - 2pm | Lightning Talks for Posters |
| 2 - 3:15pm | Panel 2: Machine Learning & Data Science: Reproducibility and Gold Standards Across Domains Moderator: Travis Desell - Kate Cooper (University of Nebraska - Omaha) - Chris Brooks (University of Michigan) | Slides - Rayid Ghani (University of Chicago) - Dave Goodsmith (DataScience.com) Abstract As big data science permeates the breadth of social, economical, behavioral, and scientific domains new techniques in the collection, analysis, and protection of this data become cross-disciplinary concerns. In this panel, panelists will consider the issues of "Gold Standards" of Machine Learning and how they differ in the fields of Education, Agriculture, and Biomedicine and more. A central concern is not just the evaluation of machine learning methods and output, but also how replication and reproducibility are tackled in the different fields. |
| 3:15pm | Break |
| 3:30 | Panel 3: Big Data for Midwestern Supply Chain Resilience Co-Chair: Ravi Nath (Creighton University) Co-Chair: Amy Kircher (University of Minnesota) | Slides - Vishal Singh (QuantifiedAg) - Danielle Richardson (ConAgra) | Slides Abstract We have the data, let’s use it! This session will demonstrate how participants leverage existing big data in new ways to support the global production, movement and consumption of food. Panelists will demonstrate methods, technology, and operationalization of big data in the food sector. This session will provide participants with information related to the big data types for the food sector, analysis, and how results are implemented in an operational environment. |
| 4:30 - 5:30pm | Panel 4: Putting the Smarts in Smart Columbus: Data Ecosystems for Smart and Healthy Communities Panel Chair: Raghu Machiraju (Ohio State University) | Slides - Anish Arora (Ohio State University) | Slides - Ayaz Hyder (Ohio State University) | Slides Abstract Data ecosystems for Smart and Healthy Communities: A smart and healthy community requires the successful deployment of data technology for addressing pathological human conditions. The premise is that technology especially mobility and connectivity that can gather and utilize data can significantly help advance solutions for adverse conditions. We describe efforts at SmartColumbus that considers social determinants when devising plans to raise the quality of life indicators for inhabitants in indigent neighborhoods. We will additionally will describe efforts at other participating institutions including OSU in this space. We will structure this "hour" as a mini-symposium; lighting talks followed by Q&A from audience. |
| 6 - 9pm | Poster and Networking Session (Hilton Omaha) |
October 3, 2017
| Time | Session Title |
|---|---|
| 7:30 - 8:30am | Registration and Check-in |
| 8:30 - 9am | State of the MBDH - Melissa Cragin (Executive Director, MBDH) | Slides |
| 9 - 10:15am | Panel 5: Federal Agencies Moderator - Sarah Nusser (ISU) Panelists: - Fen Zhao (NSF) - Chaitan Baru (NSF) | Slides - Meghan Houghton (NSF) | Slides - Rachael Nealer (DOE) | Slides |
| 10:15-10:30am | Break |
| 10:30-11:30am | Panel 6: Education and Workforce Development Moderator - Michael Twidale (University of Illinois - Urbana-Champaign) - Jim Barkley (EKTA) | Slides - David Mongeau (Ohio State University) | Slides - Renata Rawlings-Goss (South Big Data Hub / Georgia Institute of Technology) | Slides Abstract The Regional Big Data Innovation Hubs are invested in developing Data Science and Big Data capacity and expertise across the U.S. There are a variety of data-oriented educational programs in the Midwest aimed at preparing students to join the workforce. With rapid innovation and automation underway across business and government, it is essential to connect Data Science education and training with evolving workforce requirements. This panel will present an overview of DS-related programs in the US and Midwest region, analysis from leading reports, a case study on data analytics program development and industry engagement, and discussion of broad needs for a data-literate workforce. |
| 11:30-Noon | Closing Remarks |
| Noon-1pm | Box Lunch (Stay or Go) |