TextXD 2019

Text Analysis Across Domains


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


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


  • 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


  • 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


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. 

Maryam Vareth

Health & Life Sciences Lead

Maryam Vareth leads BIDS’ data science research in the Health & Life Sciences. She is also a Co-Director of the Innovate For Health initiative, a collaboration among UC Berkeley, UCSF, and the Janssen Pharmaceutical Companies of Johnson & Johnson. As an experienced researcher, engineer, and data scientist, she applies mathematics, statistics and physics to solve unmet needs in healthcare and to enhance patients’ experience during their medical journey. She is an advocate for “data-driven” medicine, and in particular for linking large-scale medical imaging data with medical diagnostics and therapeutics to extract clinically-relevant insights through the use of open source and open research practices.  

Dr. Vareth received her BS and MS training in Electrical Engineering and Computer Science (EECS) from UC Berkeley, where she was awarded the prestigious Regent’s and Chancellor’s Scholarship. She completed her PhD through the joint UC Berkeley-UCSF Bioengineering program as a National Science Foundation Fellow, where she was awarded the Margaret Hart Surbeck Endowed Fellowship for Interdisciplinary Research for her work on developing new techniques and algorithms for the acquisition, reconstruction and quantitative analysis of Magnetic Resonance Spectroscopy Imaging (MRSI), with the goal of improving its speed, sensitivity and specificity to improve the management of patients with brain tumors. She conducted her post-doctoral fellowship at UCSF, combining structural, physiological and metabolic imaging data from large clinical trials to quantitatively characterize heterogeneity within malignant brain tumors.

Ciera Martinez

Postdoctoral Fellow, Molecular and Cell Biology

Ciera received her PhD in Plant Biology from UC Davis, where she studied the evolution of leaf shape. She is currently a computational biologist in Michael Eisen’s lab pursuing her interest in the function and evolution of genomes, especially the mysterious non-coding regions of genomes. Her current project focuses on how enhancer sequences are syntactically defined and evolve in fruit flies. She also loves taking pretty pictures with fancy microscopes.
She has been active in promoting, establishing, and teaching computational reproducibility in the sciences through curriculum development, website resources, and her favorite - conversation.  She loves all things data and programming, and she is currently is an organizer for R-Ladies SF.  Her current projects include Discovery DNA and she also authors Cabinet of Curiosity, a data science blog exploring natural history data.