Date(s) - 11/18/2020
12:00 pm - 1:00 pm

Dr. Alex SzalayJoin us on Wednesday, November 18th at noon Pacific for a seminar by Dr. Alexander Szalay, Bloomberg Distinguished Professor of Physics, Astronomy and Computer Science, Institute for Data Intensive Engineering and Science, The Johns Hopkins University​.

Please use this Zoom link to attend.

“From Sky Surveys to Cancer: Spatial Data Everywhere”

The talk describes a 25 year journey leading from the Sloan Digital Sky Survey to a wide range of projects in data science. There are many common threads: the need for extreme interactivity, the need for flexible data aggregation and the commonality of spatial data. The size of data sets have grown almost a million fold, but user expectations for almost instant results has not changed. The talk will describe the gradual evolution of the SciServer, and how new interactive metaphors to interact with hundreds of terabytes of turbulence simulations emerged. We will discuss how machine learning and AI tools are transforming science, from simulations to how large experiments are designed and executed. We will also emphasize that much of these new developments still rely on having unique high value data sets at our fingertips, and how the long term survival of these is entering a critical, endangered phase.

The Data Science Coast to Coast (DS C2C) seminar series is co-hosted by the eScience Institute, along with data science institutes at New York University, Rice University, Stanford University, University of California – Berkeley, University of Michigan and the Academic Data Science Alliance (ADSA).

This fall, the series features important figures in data science, who will provide insight on the transformative use of data science in traditional research disciplines, future breakthroughs in data science research, data science entrepreneurship, and advocacy and national policies for a data-enabled and just society.

Speakers throughout the winter and spring will include faculty members and postdoctoral fellows at the six universities whose research spans the theory and methodology of data science, and their application in arts and humanities, engineering, biomedical, natural, physical  and social sciences.