2018 TextXD Symposium

Heather A. 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. 

Former BIDS Data Science Fellow Laura K. Nelson is an Assistant Professor of Sociology in the College of Social Sciences and Humanities at Northeastern University. Laura uses computational methods and open source tools - principally automated text analysis - to study social movements, culture, gender, institutions, and organizations. She is particularly interested in developing computational tools that can bolster the way social scientists do inductive and theory-driven research. She received her PhD in sociology from the University of California, Berkeley, and she also holds an MA from UC Berkeley and a BA from the University of Wisconsin, Madison. While at UC Berkeley, she was a postdoctoral fellow with Digital Humanities @ Berkeley, developing a course for undergraduates on computational text analysis in the humanities and social sciences. 

Alexandra is a BIDS data science fellow and a postdoctoral scholar working with Tom Griffiths in the Institute of Cognitive and Brain Sciences. She got her PhD in cognitive and information sciences from the University of California, Merced, in December 2015.

Her work explores human communication in data-rich environments. From capitalizing on large-scale real-world corpora to capturing multimodal experimental data, her research seeks to understand how context changes communication dynamics. Broadly, her work integrates computational and social perspectives to understand interpersonal interaction as a nonlinear dynamical system.

Relatedly, Alexandra also develops research methods to facilitate quantitative research on interaction and encourages others to use data-rich computational methods through teaching and service. As part of that effort, she works with the Center for Data on the Mind to foster the application of big data to questions about cognition and behavior.

Niek Veldhuis is Professor of Assyriology (cuneiform studies) in the Department of Near Eastern Studies. He received his PhD at the Rijksuniversiteit Groningen (The Netherlands) in 1997, and came to Berkeley in 2002. His primary interests are in the intellectual history of ancient Mesopotamia (History of the Mesopotamian Lexical Tradition, 2014) and Sumerian literature (Religion, Literature and Scholarship: The Sumerian Composition Nanše and the Birds, 2004).  He is director of the NEH-supported Digital Corpus of Cuneiform Lexical Texts and is a member of the international Oracc Steering Committee, providing tools and standards for digital publication of cuneiform texts to scholars worldwide. Today, his main research focus is on developing computational text analysis scripts (primarily in Jupyter Notebooks) for cuneiform datasets.

Christopher Hench was a BIDS Data Science Fellow and a PhD Candidate in German Literature and Medieval Studies at UC Berkeley from 2017 to 2018. He studied computational approaches to the formal analysis of lyric and epic poetry, and reading soundscapes. More broadly, with a particular interest in the challenges of domain adaptation for NLP and algorithms for the detection and scoring of text reuse. Christopher was also the Program Development Lead for Digital Humanities at Berkeley and the D-Lab at Berkeley, where he collaborated in several research projects and taught Python and Git workshops. He also coordinated the modules development effort in cooperation with BIDS, D-Lab, and the Data Science Education Program DSEP.

Deborah Sunter is an Assistant Professor of Mechanical Engineering at Tufts University. While at the University of California, Berkeley, she was a BIDS Data Science Fellow and a postdoctoral fellow in the Renewable and Appropriate Energy Laboratory. Her research interests included data science for sustainability, national energy planning, city-integrated renewable energy systems, environmental justice, and clean technology innovation. While working on her BS in Mechan­i­cal and Aero­space Engi­neer­ing at Cor­nell University, she developed a nanosatel­lite mis­sion that was suc­cess­fully launched into orbit. Although fas­ci­nated by aero­space applica­tions, the time-​critical issue of global warm­ing shifted her focus in grad­u­ate school to explore renew­able energy. Spe­cial­iz­ing in com­pu­ta­tional mod­el­ing of thermo-​physics in mul­ti­phase systems, she devel­oped a novel solar absorber tube and received her PhD in Mechan­i­cal Engi­neer­ing at the Uni­ver­sity of Cal­i­for­nia, Berke­ley. The need for better global envi­ron­men­tal solu­tions led her to do research abroad in both Japan and China. After receiving her doctorate, she advanced her understanding of energy policy as an AAAS Science and Tech­nol­ogy Pol­icy Fel­low at the U.S. Depart­ment of Energy. 

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.

Chris Kennedy is an instructor in psychiatry at Harvard Medical School / Massachusetts General Hospital. He has a PhD in biostatistics from UC Berkeley. He is a senior fellow at UC Berkeley’s D-Lab and is affiliated with the Integrative Cancer Research Group and the Division of Research at Kaiser Permanente Northern California. At BIDS, he 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.

As BIDS Chief Research Officer, Jonathan Dugan directed BIDS' efforts to solve research issues, working with the Berkeley research community to empower faculty and researchers to develop missing areas of the data science environment (tools, methods, systems, workflows, etc.), and secure the resources to fund them. He helped to promote successes, fund new work, and find practical solutions that brought together the best faculty, postdocs, students, and staff to solve immediate challenges for our research and education efforts.

Jonathan had focused his career to date on the promotion of science, education and open culture. His work in both nonprofit and for-profit businesses included consulting, business management, entrepreneurship, web services and software development, community engagement, and biomedical informatics systems. He completed his PhD from Stanford in 2002 in biomedical informatics, where he developed nonlinear mathematical simulations for protein structure modeling. His research interests include citations, data sharing, software development, community engagement, identity and reputation systems, and applying machine learning techniques to solve research questions.