SCS Recruiting Seminar: Yongjoo Park

Event Details
  • Date/Time:
    • Thursday March 14, 2019
      11:00 am - 12:00 pm
  • Location: KACB 1116W
  • Phone:
  • URL:
  • Email:
  • Fee(s):
    N/A
  • Extras:
Contact

Tess Malone, Communications Officer

tess.malone@cc.gatech.edu

Summaries

Summary Sentence: Bringing Statistical Trade-offs to Data Systems

Full Summary: No summary paragraph submitted.

Media
  • Yongjoo Park Yongjoo Park
    (image/jpeg)

TITLE: Bringing Statistical Trade-offs to Data Systems

ABSTRACT:

Despite advances in computing power, the cost of large-scale analytics and machine learning remains daunting to small and large enterprises alike. This has created a pressing demand for reducing infrastructure costs and query latencies. To meet these goals, data analysts and applications are in many cases willing to tolerate a slight — but controlled — degradation of accuracy in exchange for substantial gains in cost and performance, which we refer to as statistical trade-offs. This is particularly true in the early stages of data exploration and is in stark contrast to traditional trade-offs where the infrastructure costs must increase for higher performance.

My research builds large-scale data systems that can make these statistical trade-offs in a principled manner. In this talk, I will focus on two specific directions. First, I will present VerdictDB, a system that enables quality-guaranteed, statistical trade-offs without any changes to backend infrastructure; thus, it offers a universal solution for off-the-shelf query engines. Second, I will introduce Database Learning, a new query execution paradigm that allows existing query engines to constantly learn from their past executions and become “smarter” over time without any user intervention. I will conclude by briefly discussing other promising directions with emerging workloads beyond SQL, including visualization and machine learning.

BIO:

 Yongjoo Park is a research fellow in computer science and engineering at the University of Michigan, Ann Arbor. His research interest is software systems for fast data analytics and machine learning. He received a Ph.D. from the University of Michigan, advised by Michael Cafarella and Barzan Mozafari. He is a recipient of 2018 ACM SIGMOD Jim Gray Dissertation Award Runner-up, Kwanjeong Ph.D. Fellowship, and Jeongsong Graduate Study Fellowship.

 

 

 

Additional Information

In Campus Calendar
No
Groups

School of Computer Science, College of Computing

Invited Audience
Faculty/Staff, Postdoc, Public, Graduate students, Undergraduate students
Categories
Seminar/Lecture/Colloquium
Keywords
No keywords were submitted.
Status
  • Created By: Tess Malone
  • Workflow Status: Published
  • Created On: Feb 18, 2019 - 3:58pm
  • Last Updated: Mar 6, 2019 - 12:03pm