My work has charted new directions or bet on emerging directions that I believe would become fundamental for data management. Solving problems in these directions often requires a combination of machine learning, scalable data management, effective human-data interaction, and cloud technologies. Current directions:

• Data cleaning & integration: I build end-to-end data integration systems as parts of the Python ecosystem of open-source data tools. I also leverage these systems to build cloud/crowd data integration services for lay users.
• Data science: This direction is increasingly critical to the data management community, yet no clear agenda exists today. I'm working on an agenda that integrates research, system building, education, and outreach. This agenda currently focuses on data quality and builds on the above work in data cleaning/integration.

• Quick links: DI agenda paper and talk, Magellan homepage and paper, code (py_entitymatching, py_stringsimjoin, py_stringmatching), data sets, data science course, BigGorilla repository of DI tools, DI textbook

Selected Recent Publications (DBLP Entry Google Scholar Entry)

• Magellan: toward building ecosystems of entity matching solutions, AnHai Doan, Pradap Konda, Paul Suganthan G. C., Yash Govind, Derek Paulsen, Kaushik Chandrasekhar, Philip Martinkus, Matthew Christie, Communications of the ACM, 2020.
• Deep Entity Matching with Pre-Trained Language Models, Yuliang Li, Jinfeng Li, Yoshihiko Suhara, AnHai Doan, Wang-Chiew Tan, VLDB-21
• Data Curation with Deep Learning, Saravanan Thirumuruganathan, Nan Tang, Mourad Ouzzani, AnHai Doan, EDBT-20
• Manually Detecting Errors for Data Cleaning Using Adaptive Crowdsourcing Strategies, Haojun Zhang, Chengliang Chai, AnHai Doan, Paris Koutris, Esteban Arcaute, EDBT-2020
• Entity Matching Meets Data Science: A Progress Report from the Magellan Project, Y. Govind, P. Konda, and others. SIGMOD-19. Industrial paper.
• Executing Entity Matching End to End: A Case Study, P. Konda, S. Seshadri, E. Segarra, B. Hueth, A. Doan. EDBT-19. Industrial paper.
• Smurf: Self-Service String Matching Using Random Forests, P. Suganthan G.C., A. Ardalan, A. Doan, A. Akella. VLDB-19.
• CloudMatcher: A Hands-Off Cloud/Crowd Service for Entity Matching, Y. Govind, E. Paulson, P. Nagarajan, P. Suganthan G.C., A. Doan, Y. Park, G. Fung, D. Conanthan, M. Carter, M. Sun. VLDB-18. Demo paper.
• Toward a System Building Agenda for Data Integration (and Data Science), A. Doan, P. Konda, P. Suganthan G.C., A. Ardalan, J. Ballard, S. Das, Y. Govind, H. Li, P. Martinkus, S. Mudgal, E. Paulson, H. Zhang. IEEE Data Engineering Bulletin, Special Issue on Large-Scale Data Integration, 2018. Invited paper.
• BigGorilla: An Open-Source Ecosystem for Data Preparation and Integration, C. Chen, B. Golshan, A. Halevy, W. Tan, A. Doan. IEEE Data Engineering Bulletin, Special Issue on Large-Scale Data Integration, 2018. Invited paper.
• Deep Learning for Entity Matching: A Design Space Exploration, S. Mudgal, H. Li, T. Rekatsinas, A. Doan, Y. Park, G. Krishnan, R. Deep, E. Arcaute, V. Raghavendra. SIGMOD-18. extended version.
• MatchCatcher: A Debugger for Blocking in Entity Matching, H. Li, P. Konda, P. Suganthan G.C., A. Doan, B. Snyder, Y. Park, G. Krishnan, R. Deep, V. Raghavendra. EDBT-18. extended version, slides.
• Human-in-the-Loop Data Analysis: A Personal Perspective, A. Doan. HILDA Workshop @ SIGMOD-18.
• Magellan: Toward Building Entity Matching Management Systems, P. Konda, S. Das, P. Suganthan G.C., P. Martinkus, A. Doan, A. Ardalan, J. R. Ballard, Y. Govind, H. Li, F. Panahi, H. Zhang, J. Naughton, S. Prasad, G. Krishnan, R. Deep, V. Raghavendra. SIGMOD Record, 2018. 8-page version summarizing the progress on the Magellan system by Dec 2017.
  • Magellan: Toward Building Entity Matching Management Systems, P. Konda, S. Das, P. Suganthan G.C., A. Doan, A. Ardalan, J. R. Ballard, H. Li, F. Panahi, H. Zhang, J. Naughton, S. Prasad, G. Krishnan, R. Deep, V. Raghavendra. VLDB-16. extended version, slides. the conference and tech report versions.
  • Magellan: Toward Building Entity Matching Management Systems over Data Science Stacks, P. Konda, S. Das, P. Suganthan G.C., A. Doan, A. Ardalan, J. R. Ballard, H. Li, F. Panahi, H. Zhang, J. Naughton, S. Prasad, G. Krishnan, R. Deep, V. Raghavendra. VLDB-16, demo paper. Jupyter notebook & datasets for demo. the demo proposal.
• CloudMatcher: A Cloud/Crowd Service for Entity Matching, Y. Govind, E. Paulson, M. Ashok, P. Suganthan G.C., A. Hitawala, A. Doan, Y. Park, P. Peissig, E. LaRose, J. Badger. BIGDAS Workshop @ KDD-17. slides
• Human-in-the-Loop Challenges for Entity Matching: A Midterm Report, A. Doan, A. Ardalan, J. Ballard, S. Das, Y. Govind, P. Konda, H. Li, S. Mudgal, E. Paulson, P. Suganthan G.C., H. Zhang. HILDA Workshop @ SIGMOD-17.
• Falcon: Scaling Up Hands-Off Crowdsourced Entity Matching to Build Cloud Services, S. Das, P. Suganthan G.C., A. Doan, J. Naughton, G. Krishnan, R. Deep, E. Arcaute, V. Raghavendra, Y. Park. SIGMOD-17. extended version, slides
• Towards Interactive Debugging of Rule-Based Entity Matching, F. Panahi, W. Wu, A. Doan, J. Naughton, EDBT-17.
• The Beckman Report on Database Research, with many authors. Communications of the ACM, 2016. extended version
• Chimera: Large-Scale Classification using Machine Learning, Rules, and Crowdsourcing, C. Sun, N. Rampalli, F. Yang, A. Doan. VLDB-14, industrial paper. slides
• Corleone: Hands-off Crowdsourcing for Entity Matching, C. Gokhale, S. Das, A. Doan, J. Naughton, N. Rampalli, J. Shavlik, J. Zhu. SIGMOD-14. slides, extended report

• Gurindar S. Sohi Professorship, 2020
• Vilas Distinguished Achievement Professorship, 2018
• SIGMOD Research Highlight Award, 2017
• Vilas Associate, UW-Madison, 2016
• Alfred P. Sloan Research Fellowship, 2007
• IBM Faculty Award, 2007, 2008
• NSF CAREER Award, 2004
• ACM Doctoral Dissertation Award, 2003
• William Chan Memorial Dissertation Award, Univ. of Washington, 2003

Recent classes include data science at the undergrad and grad levels, and CS 564 (Introduction to RDBMSs).

• Selected recent service for the data management community:
  • member, SIGMOD Advisory Board,
  • member, ICDE 10-Year Most Influential Paper Award Committee,
  • associate editor, VLDB-16,
  • co-chair, industrial program, VLDB-15,
  • co-chair, Beckman meeting (with Mike Carey), 2013.
  • chair, industrial program, SIGMOD-12
  • data integration textbook (with Alon Halevy and Zack Ives), 2012.
• I spent 3 years (2011-2014) setting up a professional MS program and a certificate program in CS at UW-Madison (with help from Karu Sankaralingam, Jeff Naughton, and Suman Banerjee). These programs have been highly successful, enrolling hundreds of students.
• I've participated in or led several important strategic initiatives for CS at UW-Madison. These include growth to 50 (tenure-track faculty), a re-organization of the department's governance structure, and data science initiatives. I serve as an associate chair since 2017. I've also worked on several strategic initiatives for UW-Madison, including serving on the steering committees of several data science centers and initiatives, and serving on the 2017-2018 campus task force for growth strategies for computing at UW-Madison.

• For advice on research, communication, job hunt, and more, see the mother of all advice collections by Tao Xie at Illinois.
• My academic job application package (in 2002), academic job talk slides (in 2003).