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 featured 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 were 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.
- 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 Haveman is a Professor of Sociology and Business at UC Berkeley. She holds a BA in history and an MBA (from the University of Toronto), and a Ph.D. in organizational behavior and industrial relations (from UC Berkeley). Following positions at Duke University's Fuqua School of Business, Cornell University's Johnson Graduate School of Management, and Columbia University's Graduate School of Business, Professor Haveman joined UC Berkeley in July 2006. Her research interests include how organizations, the fields in which they are embedded, and the careers of their members and employees evolve. Her current work involves American magazines and wineries, Chinese listed firms, and the emerging marijuana market in several US states.
Maryam Vareth leads BIDS’ data science research efforts in the Health & Life Sciences. Dr. Vareth is a Co-Director of the Innovate For Health initiative, a collaboration among UC Berkeley, UCSF, and Janssen Pharmaceutical Companies of Johnson & Johnson. As an experienced engineer, researcher, and data scientist, she applies mathematics, statistics and physics to solve unmet needs in healthcare to enhance patients’ experience during their medical journey. She is an advocate for “data-driven” medicine, and in particular for linking medical imaging data with medical diagnostics and therapeutics to extract clinically-relevant insights through the use of open research and open source 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.
BIDS Biodiversity and Environmental Sciences Lead Ciera Martinez focuses on data intensive research projects that aim to understand how life on this planet evolves in reaction to the environment and climate – especially projects involving large and complex datasets. A long-time open science advocate, Ciera has been involved with and continues to be interested in working on training for open data, education, publishing, and software, including developing community standards for data management practices. As a 2019 Mozilla Open Science Fellow, she connected her love of data and museums and worked on projects aimed at understanding and increasing the usability of biodiversity and natural history museum data. She received her PhD in Plant Biology from UC Davis, researching the genetic mechanisms regulating plant architecture. She then went on to become a NSF Postdoctoral Fellow at UC Berkeley in the Molecular and Cellular Biology Department, studying genome evolution. She was also a BIDS postdoctoral Data Science Fellow for 3 years, working on undergraduate research practices, data science training, community development, and best practices for data science, diversity and inclusion, and computational research.