Time for Suciu, Meiliou

src/seminar/2018sp.erb

@@ -22,10 +22,10 @@ schedule:
       bio:      |
         Bill Howe is an Associate Professor in the Information School, Adjunct Associate Professor in Computer Science & Engineering, and Associate Director and Senior Data Science Fellow at the UW eScience Institute. He is a co-founder of Urban@UW, and with support from the MacArthur Foundation and Microsoft, leads UW's participation in the MetroLab Network. He created a first MOOC on Data Science through Coursera, and led the creation of the UW Data Science Masters Degree, where he serves as its first Program Director and Faculty Chair. He also serves on the Steering Committee of the Center for Statistics in the Social Sciences.
       headshot: "https://faculty.washington.edu/~billhowe/images/billhowe.png"
-  - when:  May 15; Time TBD
+  - when:  May 15; 3:30-5:00
     what:  Rethinking Query Execution on Big Data
     who:   Dan Suciu (University of Washington)
-    where: Location TBD
+    where: Davis 113A
     details:
       abstract: |
         Database engines today use the same approach to evaluate a
@@ -64,10 +64,10 @@ schedule:
         Suciu's PhD students Gerome Miklau, Christopher Re and Paris Koutris
         received the ACM SIGMOD Best Dissertation Award in 2006, 2010, and
         2016 respectively, and Nilesh Dalvi was a runner up in 2008.
-  - when:  May 17; Time TBD
+  - when:  May 17; 3:30-5:00
     what:  "Diagnoses and Explanations: Creating a Higher-Quality Data World"
     who:   Alexandra Meliou (UMass Amherst)
-    where: Location TBD
+    where: Davis 113A
     details:
       abstract: |
         The correctness and proper function of data-driven systems and applications relies heavily on the correctness of their data.  Low quality data can be costly and disruptive, leading to revenue loss, incorrect conclusions, and misguided policy decisions.  Improving data quality is far more than purging datasets of errors; it is critical to improve the processes that produce the data, to collect good data sources for generating the data, and to address the root causes of problems.<br/>