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April 11, 2017: Governor’s Data Analytics Summit in Charlottesville: Turning a Commonwealth of Data into Actionable Value
Tuesday, April 11, 2017 from 8:00 a.m. – 5:30 p.m.
The Boar’s Head Inn, Charlottesville, VA
The summit is co-hosted by the University of Virginia’s Data Science Institute
This year’s summit will feature a lineup of distinguished and insightful speakers and panelists who will discuss how to unlock the value of data with the use of analytics to support an agency’s mission, solve challenges, and answer burning questions. Attendees will obtain practical knowledge about identifying and scoping challenges to solve with data analytics, how to get data analytic projects off the ground to completion, and where to get resources.
Attendees will also learn about workforce implications, data sharing options, data ethics and privacy, open data, and the future of data analytics including Internet of Things, big data analytics, machine learning, and predictive analytics.
Registration is only open to Virginia state and local government employees. An email will be sent when the online registration is open in early February. Seating is limited, so register early.
For more information, click here.
DS+DH: THE MACHINE AS HORIZON OF INTERPRETATION: A TRANSDISCIPLINARY CONFERENCE ON DATA SCIENCE AND DIGITAL HUMANITIES
When: Friday, April 7, 2017 – 9:00am-4:00pm
Where: Nau 101 (Morning Session: 9:00am-12:00pm); Alderman 317 (Afternoon Session: 2:00-4:00pm)
Novel Analytics from James Joyce to The Bestseller Code
Matthew L. Jockers
Susan J. Rosowski Associate Professor of English
Department of English
University of Nebraska-Lincoln
Click here for more details about this conference.
The Darden School of Business and the Data Science Institute announced Monday (March 20) that they are joining together to offer a new dual-degree program – MBA/Master of Science in Data Science.
For more information, click here.
Speaker: Nanyun Peng
Date: Monday March 27, 2017
Location: Rice Hall, Room 242
Title: Representation Learning with Joint Models for Information Extraction
There is abundant knowledge out there carried in the form of natural language texts, such as social media posts, scientific research literature, medical records, etc., which grows at an astonishing rate. Yet this knowledge is mostly inaccessible to computers and overwhelming for human experts to absorb. Information extraction (IE) processes raw texts to produce machine understandable structured information, thus dramatically increasing the accessibility of knowledge through search engines, interactive AI agents, and medical research tools. However, traditional IE systems assume abundant human annotations for training high quality machine learning models, which is impractical when trying to deploy IE systems to a broad range of domains, settings and languages. In this talk, I will present how to use deep representation learning to leverage the distributional statistics of characters and words, the annotations for other tasks and other domains, and the linguistics and problem structures, to combat the problem of inadequate supervision, and conduct information extraction with scarce human annotations.
Nanyun Peng is a PhD candidate in the Department of Computer Science at Johns Hopkins University, affiliated with the Center for Language and Speech Processing and advised by Dr. Mark Dredze. She is broadly interested in Natural Language Processing, Machine Learning, and Information Extraction. Her research focuses on using deep learning for information extraction with scarce human annotations. Nanyun is the recipient of the Johns Hopkins University 2016 Fred Jelinek Fellowship. She has completed two research internships at IBM T.J. Watson Research Center, and Microsoft Research Redmond. She holds a master’s degree in Computer Science and BAs in Computational Linguistics and Economics, all from Peking University.