TextXD 2019

Text Analysis Across Domains

XD

December 3, 2019 to December 6, 2019
8:00am to 6:00pm
Berkeley Haas School of Business

Register

TextXD brings together researchers from across a wide range of disciplines, who work with text as a primary source of data. We work to identify common principles, algorithms and tools to advance text-intensive research, and break down the boundaries between domains, to foster exchange and new collaborations among like-minded researchers. We encourage scholars and practitioners from a broad disciplinary and geographic range to apply. Talks will range from the theory of text analysis and deep learning to applied analyses or new software packages. In addition to presentations, the event will include a poster session to create dialogue around NLP projects by students and collaborators. 

TextXD 2019
Location: Spieker Forum (6th floor), Chou Hall, Haas School of Business, UC Berkeley
Dates: December 3-6, 2019

CALL FOR ABSTRACTS: Submissions due by September 20, 2019

PROGRAM IN BRIEF

  • Tuesday, Dec. 3rd:       Training workshops - "Intro to Text Analysis"
  • Wednesday, Dec. 4th:  Keynote speakers, research talks, posters
  • Thursday, Dec 5th:       Keynote speakers, research talks, posters
  • Friday, Dec. 6th:            Applied collaboration sessions, research brainstorming, and coding

ORGANIZING COMMITTEE 

  • Chris Kennedy, Biostatistics, BIDS, D-Lab, UC Berkeley
  • Heather Haveman, Sociology & Haas School of Business, UC Berkeley
  • Sameer Srivastava, Haas School of Business, UC Berkeley
  • Caroline Le Pennec, Economics, UC Berkeley
  • Jaren Haber, Sociology, UC Berkeley
  • Oksana Gologorskaya, CTSI & Information Commons, UCSF
  • Violet Yao, Computer Science & Legal Studies, UC Berkeley
  • Eshaan Pathak, Computer Science & Data Science, UC Berkeley

CONTACT:  textxd-bids@lists.berkeley.edu

Follow @Text_XD

Speaker(s)

Chris Kennedy

PhD student, Biostatistics

Chris Kennedy is a BIDS Data Science Fellow and a PhD student in biostatistics, where he works with Alan Hubbard. He is also a D-Lab instructor and consultant, and an NIH biomedical big data trainee. His methodological interests encompass targeted machine learning, randomized trials, causal inference, deep learning, text analysis, signal processing, and computer vision. His applications are primarily in precision medicine, public health, genomics, and election campaigns. His software projects include the SuperLearner ensemble learning system and varImpact for variable importance estimation; he leverages high performance computing on Savio and XSEDE clusters to accelerate his work.

Prior to Berkeley he worked in political analytics in DC, running dozens of randomized trials and integrating machine learning into multi-million dollar programs to improve voter turnout for underrepresented Americans. He has also worked to support climate change action through Al Gore’s Climate Reality Project and the Yale Program on Climate Change Communication. He holds an M.A. in political science from UC Berkeley, an M.P.Aff. from the LBJ School of Public Affairs, and a B.A. in government & economics from The University of Texas at Austin.

Heather Haveman

BIDS Faculty Council Member

Heather A. Haveman is a Professor of Sociology and Business in the Department of Sociology, and a Professor of Sociology and Management at the Haas School of Business, UC Berkeley.  Her current work involves American magazines, American wineries, Chinese listed firms, the emerging marijuana market in several American states, American law professors, and American wineries.