Eugene Wu

Bio

Eugene Wu is broadly interested in technologies that help users play with their data. His goal is for users at all technical levels to effectively and quickly make sense of their information. He is interested in solutions that ultimately improve the interface between users and data, and uses techniques borrowed from fields such as data management, systems, crowd sourcing, visualization, and HCI. Eugene Wu recieved his Ph.D. from MIT, B.S. from Cal, and was a postdoc in the AMPLab. A profile, informal bio, obit.

See the WuLab website

We are recruiting interns, PhD candidates, and others for 2018! See Below.


Selected Publications (Show All)

  1. Mining Precision Interfaces From Query Logs
    Haoci Zhang, Thibault Sellam, Eugene Wu
    Tech Report
  2. BoostClean: Automated Error Detection and Repair for Machine Learning
    Sanjay Krishnan, Michael J. Franklin, Ken Goldberg, Eugene Wu
    Tech Report
  3. Load-n-Go: Fast Approximate Join Visualizations That Improve Over Time
    Marianne Procopio, Carlos Scheidegger, Eugene Wu, Remco Chang
    DSIA 2017
  4. Approximate Entropy as a Measure of Line Chart Complexity
    Gabriel Ryan, Abigail Mosca, Eugene Wu, Remco Chang
    InfoVIS Poster 2017
  5. PreCog: Improving Crowdsourced Data Quality Before Acquisition
    Hamed Nilforoshan, Jiannan Wang, Eugene Wu
    Arxiv 2017
  6. Precision Interfaces
    Haoci Zhang, Thibault Sellam, Eugene Wu
    HILDA 2017
  7. PALM: Machine Learning Explanations For Iterative Debugging
    Sanjay Krishnan, Eugene Wu
    HILDA 2017
  8. Combining Design and Performance in a Data Visualization Management System
    Eugene Wu, Fotis Psallidas, Zhengjie Miao, Haoci Zhang,Laura Rettig, Yifan Wu, Thibault Sellam
    CIDR 2017
  9. QFix: Diagnosing errors through query histories
    Xiaolan Wang, Alexandra Meliou, Eugene Wu
    SIGMOD 2017
  10. A DeVIL-ish Approach to Inconsistency in Interactive Visualizations
    Yifan Wu, Joe Hellerstein, Eugene Wu
    Hilda 2016
  11. PFunk-H: Approximate Query Processing using Perceptual Models
    Daniel Alabi, Eugene Wu
    Hilda 2016
  12. Towards Reliable Interactive Data Cleaning: A User Survey and Recommendations
    Sanjay Krishnan, Daniel Haas, Michael J. Franklin, Eugene Wu
    Hilda 2016
  13. ActiveClean: Interactive Data Cleaning While Learning Convex Loss Models
    Sanjay Krishnan, Jiannan Wang, Eugene Wu, Michael J. Franklin, Ken Goldberg
    Arxiv 2016
  14. Towards Perception-aware Interactive Data Visualization Systems
    Eugene Wu, Arnab Nandi
    DSIA 2015 Slides
  15. Explaining Data in Visual Analytic Systems
    Eugene Wu
    Doctoral Thesis
  16. The Case for Data Visualization Management Systems
    Eugene Wu, Leilani Battle, Samuel Madden
    VLDB 2014
  17. Scorpion: Explaining Away Outliers in Aggregate Queries
    Eugene Wu, Samuel Madden
    VLDB 2013 (Selected as one of the best papers of the conference!) Slides
  18. SubZero: a Fine-Grained Lineage System for Scientific Databases
    Eugene Wu, Samuel Madden, Michael Stonebraker
    ICDE 2013 (Best of conference)
  19. Human-powered Sorts and Joins
    Adam Marcus, Eugene Wu, David Karger, Samuel Madden, Robert Miller
    VLDB 2012
  20. No Bits Left Behind
    Eugene Wu, Carlo Curino, Sam Madden
    CIDR 2011
  21. Crowdsourced Databases: Query Processing with People
    Adam Marcus, Eugene Wu, Sam Madden, Robert Miller
    CIDR 2011
  22. Relational Cloud: A Database-as-a-Service for the Cloud
    Carlo Curino, Evan Jones, Raluca Popa, Nirmesh Malviya, Eugene Wu, Sam Madden, Hari Balakrishnan, Nickolai Zeldovich
    CIDR 2011
  23. High-performance complex event processing over streams
    Eugene Wu, Yanlei Diao, Shariq Rizvi
    SIGMOD 2006