AnHai Doan

About

AnHai Doan is the Vilas Distinguished Achievement Professor and Gurindar S. Sohi Professor of Computer Science at the University of Wisconsin–Madison. His research goal is to make messy data usable at scale. He works on data integration, data science, and machine learning, building end-to-end systems that are deployed in real-world settings. He has received the ACM Doctoral Dissertation Award, NSF CAREER Award, and Sloan Fellowship, and co-authored Principles of Data Integration, a widely used textbook. AnHai has worked extensively at the intersection of academia and industry, serving on the advisory board of Transformic (acquired by Google), as Chief Scientist at Kosmix (acquired by Walmart), and co-founding GreenBay Technologies (acquired by Informatica). He has also served on the SIGMOD Advisory and Executive Committees and was Co-Chair of SIGMOD 2020. His background includes growing up in Vietnam, studying in Hungary, and earning his Ph.D. from the University of Washington in 2002. His career spans roles as a graduate student, professor, startup employee, and big-company employee, with interests outside of work in architecture, history, art, interior design, traveling, and long-distance hiking.

Research topics

• Computer Science
• Data Mining
• Artificial Intelligence
• World Wide Web
• Data science
• Database
• Theoretical computer science
• Algorithm
• Mathematics

Selected publications

• Deep Learning for Entity Matching

  2018-05-25 · 519 citations

  article

  Entity matching (EM) finds data instances that refer to the same real-world entity. In this paper we examine applying deep learning (DL) to EM, to understand DL's benefits and limitations. We review many DL solutions that have been developed for related matching tasks in text processing (e.g., entity linking, textual entailment, etc.). We categorize these solutions and define a space of DL solutions for EM, as embodied by four solutions with varying representational power: SIF, RNN, Attention, a…

  PublisherDOI

• Deep learning for blocking in entity matching

  Proceedings of the VLDB Endowment · 2021 · 77 citations

  Senior authorCorresponding

  Entity matching (EM) finds data instances that refer to the same real-world entity. Most EM solutions perform blocking then matching. Many works have applied deep learning (DL) to matching, but far fewer works have applied DL to blocking. These blocking works are also limited in that they consider only a simple form of DL and some of them require labeled training data. In this paper, we develop the DeepBlocker framework that significantly advances the state of the art in applying DL to blocking…

  PublisherDOI

• The Seattle Report on Database Research

  ACM SIGMOD Record · 2020 · 68 citations

  Approximately every five years, a group of database researchers meet to do a self-assessment of our community, including reflections on our impact on the industry as well as challenges facing our research community. This report summarizes the discussion and conclusions of the 9th such meeting, held during October 9-10, 2018 in Seattle.

  PublisherDOI

• The Seattle report on database research

  Communications of the ACM · 2022 · 43 citations

  Every five years, a group of the leading database researchers meet to reflect on their community's impact on the computing industry as well as examine current research challenges.

  PublisherOA PDFDOI

• BigGorilla: An Open-Source Ecosystem for Data Preparation and Integration.

  IEEE Data(base) Engineering Bulletin · 2018-01-01 · 43 citations

  articleSenior author
  Publisher

Recent grants

• III: Medium: Enabling Technologies for 21st Century Entity Matching Applications

  NSF · $1.1M · 2016–2021

• CAREER: Evolving and Self-Managing Data Integration Systems

  NSF · $238k · 2006–2010

Frequent coauthors

• Alon Halevy

  49 shared

• Zachary G. Ives

  University of Pennsylvania

  22 shared

• Raghu Ramakrishnan

  20 shared

• Jeffrey F. Naughton

  19 shared

• Peter Haddawy

  Mahidol University

  17 shared

• Warren Shen

  13 shared

• Sanjib Das

  Jadavpur University

  13 shared

• Pedro Domingos

  Instituto de Tecnología Química

  13 shared

Labs

• UW-Madison Database GroupPI

Awards & honors

• Gurindar S. Sohi Professorship, 2020
• Vilas Distinguished Achievement Professorship, 2018
• Alfred Sloan Research Fellowship, 2007
• NSF CAREER Award, 2004
• ACM Doctoral Dissertation Award, 2003

Similar researchers at University of Wisconsin-Madison

• Yong Jae Leeh-90
• Somesh Jhah-69
• Michael Gleicherh-62