I am a Research Fellow in Computer Science and Engineering at the University of Michigan, Ann Arbor. My research is focused on building extremely scalable analytics systems by exploiting statistical tradeoffs. I have applied this concept to various types of workloads, such as SQL-based analytics, visualization, and machine learning.

I am leading VerdictDB, a system that enables approximate query processing on top of any SQL engines. VerdictDB is open sourced under the Apache License and is used by several major companies. I am a recipient of 2018 ACM SIGMOD Jim Gray Dissertation Award Runner Up, 2013 Kwanjeong Ph.D. Fellowship, and 2011 Jeongsong Graduate Study Fellowship.

I obtained a Ph.D. in Computer Science and Engineering from the University of Michigan, Ann Arbor, advised by Michael Cafarella and Barzan Mozafari. I received a B.S. from Seoul National University. I am best reached via email: pyongjoo@umich.edu.

I am on the academic job market to start in the fall of 2019.
(Research Statement)     (Curriculum Vitae)


Awards & Honors

  1. ACM SIGMOD Jim Gray Dissertation Award Runner Up, June 2018

  2. ACM SIGMOD Student Travel Award, May 2017

  3. Rackham Travel Grant, January 2017

  4. Kwanjeong Ph.D. Fellowship, 2013

  5. Jeongsong Graduate Study Fellowship, 2011

  6. Korean National Science Scholarship, 2004

Peer-Reviewed Publications (click here to see other publications/talks)

  1. Yongjoo Park, Jingyi Qing, Xiaoyang Shen, Barzan Mozafari
    BlinkML: Efficient Maximum Likelihood Estimation with Probabilistic Guarantees
    SIGMOD 2019 (research)
    (pdf)

  2. Yongjoo Park, Barzan Mozafari, Joseph Sorenson, Junhao Wang
    VerdictDB: Universalizing Approximate Query Processing
    SIGMOD 2018 (research)
    (pdf), (technical report), (slides)

  3. Wen He, Yongjoo Park, Idris Hanafi, Jacob Yatvitskiy, Barzan Mozafari
    Demonstration of VerdictDB, the Platform-Independent AQP System
    SIGMOD 2018 (demo)
    (pdf)

  4. Yongjoo Park, Amhad Shahab Tajik, Michael Cafarella, Barzan Mozafari
    Database Learning: Toward a Database that Becomes Smarter Every Time
    SIGMOD 2017 (research)
    (pdf), (technical report), (slides)

  5. Yongjoo Park
    Active Database Learning
    CIDR 2017 (abstract)
    (pdf)

  6. Yongjoo Park, Michael Cafarella, Barzan Mozafari
    Visualization-Aware Sampling for Very Large Databases
    ICDE 2016 (research)
    (pdf), (technical report), (slides), (demo)

  7. Yongjoo Park, Michael Cafarella, Barzan Mozafari
    Neighbor-Sensitive Hashing
    PVLDB 2015 (research)
    (pdf), (supplementary document), (slides), (code)

  8. Michael Anderson, Dolan Antenucci, Victor Bittorf, Matthew Burgess, Michael Cafarella, Arun Kumar, Feng Niu, Yongjoo Park, Christopher Ré, Ce Zhang
    Brainwash: A Data System for Feature Engineering
    CIDR 2013 (vision)
    (pdf)

Professional Service

  1. Research Track Program Committee, VLDB 2020

  2. Program Committee, SIGMOD 2020

  3. Program Committee, SoCC 2019

  4. Reviewer, TKDE 2018

  5. Program Committee, aiDM workshop 2018

  6. Publicity Chair, ACAIA workshop 2017

  7. Reviewer, SIGMOD 2018

  8. Reviewer, VLDB Journal 2017

  9. External Reviewer,
    CIDR 2017,
    VLDB Journal 2016, VLDB 2016, SIGMOD 2016,
    VLDB 2015, ICDE 2015, CIDR 2015