BIDS' Cathryn Carson, Dani Ushizima, and Valeri Vasquez will be featured presenters at the 2020 ADSA Annual Meeting, which is being hosted by the Academic Data Science Alliance on October 14-16, 2020. BIDS alumnae Orianna Demasi and Sara Stoudt will also present. This year's conference is free to attend, and all sessions will take place virtually.
The 2020 ADSA Annual Meeting will bring together data science methodologists and domain researchers from all disciplines and career stages to share breakthroughs and new approaches in data science research and education, with a strong emphasis on responsible data science. We are encouraging new, untested ideas to promote brainstorming for innovation and promote collaborative feedback and engaging discussions.
This year's meeting is being sponsored by the Alfred P. Sloan Foundation, the Gordon and Betty Moore Foundation, the Berkeley Institute for Data Science, the University of Washington eScience Institute, the NYU Center for Data Science, and the National Science Foundation.
Cathryn Carson holds the Thomas M. Siebel Presidential Chair in the History of Science at the University of California, Berkeley. Before receiving her Ph.D. in History of Science from Harvard University, she was trained in condensed matter physics. Her research deals with the intellectual, political, and institutional history of contemporary science, including theoretical physics and data science. She has served as Associate Dean of Social Sciences, founding Director of the Social Sciences Data Laboratory (D-Lab), founding Senior Fellow of the Berkeley Institute for Data Science, Faculty Lead of the Data Science Education Program, and Chair of the Faculty Advisory Board for Berkeley's Data Science Planning Initiative, which developed the blueprint for Berkeley’s organizational realignment around data science. In 2019-20, she served as Associate Dean for Strategy and Planning for the Division of Computing, Data Science, and Society.
BIDS Faculty Affiliate Dani Ushizima is a Staff Scientist in the Machine Learning and Analytics Group in the Computational Research Division at Berkeley Lab, where she leads the Image Processing/Machine Vision team at CAMERA, and an Affiliate Faculty of the Bakar Computational Health Sciences Institute (BCHSI) at the University of California, San Francisco. She also leads the Center for Recognition and Inspection of Cells (CRIC), where her research focuses on imaging cancer cells for early-stage disease diagnosis. With 20 years of research and development experience in Computer Vision, Dani has focused primarily on quantitative microscopy and microstructure classification, from materials science to biomedical imaging.
Váleri N. Vásquez is a PhD student with the Energy and Resources Group, a Moore/Sloan Fellow at the Berkeley Institute for Data Science, and a former research scholar in the Renewable and Appropriate Energy Laboratory. Váleri’s research interests include the use of computational models to examine the environmental drivers and economic impacts of infectious diseases. She is currently studying questions relevant to the use of gene drive systems for malarial control. Prior to graduate school, Váleri focused on international and domestic climate change issues at the U.S. Department of State, the Center for American Progress, and the White House Council on Environmental Quality. She holds an MS from the University of California, Berkeley and a BA from the College of William and Mary.
Orianna is a former BIDS Data Science Fellow and a PhD student in the Computer Science Division, where she worked on applications of machine learning for mental health. Her research focused on the development of quantitative monitoring tools as necessary preliminary steps in understanding disorders and evaluating treatments.
Former BIDS Data Science Fellow Sara Stoudt is a lecturer in the Statistical & Data Sciences Program at Smith College. At UC Berkeley, she was a PhD student in Statistics advised by Professors Will Fithian and Perry de Valpine. Her research interests included ecological applications of statistics and assessing the identifiability and robustness of inference under model misspecification in species distribution models. She was also involved in teaching writing for statistics with Professor Deborah Nolan. Prior to being a BIDS Fellow, Sara was supported by a National Physical Sciences Consortium Fellowship with the National Institute of Standards and Technology and was part of the Data Science for the 21st Century: Environment and Society Training Program. Sara graduated with a BA in Mathematics and Statistics from Smith College.