Data Science Coast to Coast - Alex Szalay
Date: Wednesday, November 18, 2020
Time: 12:00 PM Pacific / 3:00 PM Eastern
Location: Join via Zoom
Alex Szalay is a Distinguished Professor in the Bloomberg Center for Physics and Astronomy at Johns Hopkins University, where he is also a professor in the department of Computer Science, and the director of the Institute of Data Intensive Engineering and Science (IDIES). He also leads the Open Storage Network (OSN), funded by the NSF and the Schmidt Futures Foundation in 2018, to provide cyberinfrastructure services that address specific data storage, transfer, sharing, and access challenges. The OSN is linked to the Big Data Innovation Hubs and other data science initiatives involved in local, regional, and national-scale research and education. Szalay is a cosmologist, working on the statistical measures of the spatial distribution of galaxies and galaxy formation. He is a Corresponding Member of the Hungarian Academy of Sciences, and a Fellow of the American Academy of Arts and Sciences. In 2004, he received an Alexander Von Humboldt Award in Physical Sciences, and in 2007, the Microsoft Jim Gray Award. In 2008 he became Doctor Honoris Causa of the Eotvos University, Budapest. He is also a musician, film sound designer and computer animator.
The Data Science Coast to Coast (DSC2C) seminars present leaders in data science whose research spans the theory and methodology of data science, and their application in arts and humanities, engineering, biomedical, natural, physical and social sciences. The series was launched in fall 2020, hosted jointly by BIDS, NYU’s Center for Data Science, Rice University’s Ken Kennedy Institute, Stanford Data Science, the University of Michigan’s Michigan Institute for Data Science (MIDAS), and the University of Washington’s eScience Institute. All events in the series are free to attend, and all who are interested are welcome and encouraged to attend. Event Contact: Questions may be directed to Jing Liu (email@example.com), Managing Director of MIDAS.