Ohio State chosen as a Beyond Traffic Innovation Center by the USDOT

U.S. Transportation Secretary Anthony Foxx announced this week that The Ohio State University has been named one of 18 institutions across the country to lead research on transportation challenges outlined in the Department of Transportation’s Beyond Traffic 2045 report.

As a Beyond Traffic Innovation Center, Ohio State is recognized as a forward-thinking and influential institution capable of driving solutions to these challenges by convening decision-makers in the Great Lakes megaregion and coordinating related research, curriculum, outreach and other activities. Due to its location in the center of the country, the Great Lakes megaregion sits at the heart of the U.S. transportation network.

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Collaborative Analytics – public lecture by Dr. Kristin Tolle, April 19th

The Center for Urban and Regional Analysis (CURA), in cooperation with Translational Data Analytics @ Ohio State, Ohio State ADVANCE, The Women’s Place and the STEAM Factory is proud to present a public lecture by Dr. Kristin Tolle:

kristintolle_headshot_fall2010 Dr. Kristin Tolle, Director, Data Science Initiative, Microsoft Research Outreach

Collaborative analytics:

Meeting global challenges through shared research and development

Tuesday, April 19th, 4pm-5pm

Wexner Center Theater, The Ohio State University

This talk will focus on research collaborations that cut across disciplinary, organizational and geographical boundaries to generate research and development breakthroughs on global challenges such as healthcare and climate change.  Pushing the state of the art can be facilitated through these partnerships—public/private and academic/industry—when each group bring their unique strengths and perspectives to bear on scientific problems.

Seats are free but limited!  More information and RSVP

More about Dr. Kristin Tolle:  Kristin M. Tolle is the Director of the Data Science Initiative in Microsoft Research Outreach, where she has worked since 2000 for several product teams including the Natural Language Group, Visual Studio, and the Microsoft Office Excel Team. Since joining Microsoft Research’s outreach program in 2006, she has run several major initiatives from Biomedical computing and environmental science to more traditional computer and information science programs around natural user interactions and data curation. She also directed the development of the Microsoft Translator Hub and the Environmental Science Services Toolkit. Dr. Tolle is an editor, along with Tony Hey and Stewart Tansley, of one of the earliest books on data science, The Fourth Paradigm: Data Intensive Scientific Discovery. Her current focus is develop an outreach program to engage with academics on data science in general and more specifically around using data to create meaningful and useful user experiences across devices platforms.


Should we always play dumb in science?

A recent article by Naomi Oreskes (co-author of the brilliant but depressing The Collapse of Western Civilization: A View From the Future) questions why we always play dumb in climate science [Playing Dumb on Climate Change].

Prof. Oreskes argues the well-accepted (read: rarely questioned) 95% confidence limit in statistical tests is a severe standard: it reflects a greater fear of Type I errors (false positives) over Type II errors (false negative).  It essentially asks scientists to “play dumb”: pretend they know nothing about the phenomenon and reject causality unless there is only a 1 in 20 odds that the observed relationship occurred by chance.

But, the 95% confidence standard is a convention: it has no basis in nature.  What if we’re not so dumb – instead of a blank slate, what if we have good theory to guide our empirical investigation?  Or, what if the consequences of a false negative are much greater than a false positive?  Should accept lower odds of a Type I error (and higher odds of a Type II error) by lowering the required confidence level?  What is that level?  Should it vary?

Solid theory and high consequences from false negatives is certainly the case for climate science.  But, this is a much broader issue across all sciences.  Why 95%?  During the birth of statistics in the 18th and 19th centuries, there were good reasons to play dumb.  There are good reasons to be smarter now.