TextXD brings together researchers and text processing experts from across a wide range of disciplines in academia and industry, who work with text as a primary source of data. TextXD works to break down the boundaries between domains by identifying common principles, algorithms, and software tools to advance text-intensive research; and by encouraging the exchange of new ideas and collaborations among like-minded researchers.
This year's conference will feature over 20 lecture presentations with additional lightning talks, introductory workshops, poster sessions, and a hackathon. Scholars, researchers, and practitioners from diverse disciplinary backgrounds and geographic areas are encouraged to register.
Location: Spieker Forum (6th floor), Chou Hall, Haas School of Business, UC Berkeley
Dates: December 3-6, 2019
Meet Kathleen Carley (CMU), Justin Grimmer (Stanford), Yunyao Li (IBM), Christopher Potts (Stanford), Brandon Stewart (Princeton), and more.
Join the TextXD Mailing list for program updates.
- 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
- Suzanne Tamang, Stanford
- Ciera Martinez, BIDS
- Maryam Vareth, BIDS, UCSF
CONTACT: Questions about this conference may be directed to https://www.textxd.org/#contact.
Chris Kennedy was a BIDS - Biomedical Big Data Training (BBDT) Data Science Fellow and a PhD student in biostatistics at UC Berkeley, where he worked with Alan Hubbard. He was also a D-Lab instructor and consultant, and an NIH biomedical big data trainee. His methodological interests encompassed targeted machine learning, randomized trials, causal inference, deep learning, text analysis, signal processing, and computer vision. His applications were primarily in precision medicine, public health, genomics, and election campaigns. His software projects included 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 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 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 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.