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As plans for re-opening businesses, communities, and schools emerge, it becomes increasingly important to better understand where and how the new coronavirus is transmitted. In this conversation, Berkeley faculty open different windows onto what we’re discovering about how to track and limit the COVID-19 pandemic.
COVID-19 has impeded many opportunities and has resulted in students feeling isolated and uninspired. To help students interested in data science stay motivated and connected through this difficult time, the UC Berkeley Division of Computing, Data Science, and Society (CDSS) piloted the Discovery Exchange.
UC Berkeley’s third annual National Workshop for Data Science Education was held June 22-26, 2020. The workshop was created to support the increasing demand for data scientists; sharing best practices around teaching Data Science and Berkeley’s open-source curriculum, and creating a community around Data Science Education with instructors in higher education interested in developing Data Science classes and programs at their institutions educating the next generation of data scientists.
Graduate student Tijana Zrnic won an Apple Ph.D. fellowship in Artificial Intelligence/Machine Learning (AI/ML). Scholars from invited institutions are selected for this program based on their "innovative research, record as thought leaders and collaborators in their fields, and unique commitment to take risks and push the envelope in machine learning and AI."
Professor Philip Stark and co-authors authored "Five ways to ensure that models serve society; pandemic politics highlight predictions need to be transparent and humble to invite insight, not blame." The article is timely in particular to the use of statistical models in the context of the COVID-19 pandemic and predictive policing.
As part of the Innovate For Health initiative, UC Berkeley and the University of California, San Francisco are currently soliciting applications (due Aug. 2nd) for outstanding data scientists to join the Data Science Health Innovation Fellowship Program. Selected fellows will receive 2 years of financial support to develop and execute innovative, high-impact, data-driven translational research projects in areas of unmet patient needs.
Incoming 5th Year MIDS student Christina Carr was chosen as the inaugural recipient of the Sharon X. Lin & Andrew R. Bullen Graduate Fellowship in Data Science for the 2020–21 academic year. The award supports students in the Master’s of Information and Data Science program who show a commitment to gender equity within the program.
