Renée J. Miller named Canada Excellence Research Chair in Data Intelligence, to join Cheriton School of Computer Science in 2024

Thursday, November 16, 2023

Professor Renée J. Miller has been named the Canada Excellence Research Chair in Data Intelligence. She is currently a University Distinguished Professor at Khoury College of Computer Science at Northeastern University. She will be joining the University of Waterloo in June 2024 as the Cheriton School of Computer Science’s first Canada Excellence Research Chair.

Launched in 2008, the Canada Excellence Research Chairs Program supports Canadian universities in their efforts to build on the nation’s growing reputation as a global leader in research and innovation by supporting world‑renowned researchers and their teams to establish ambitious research programs at Canadian universities.

“Professor Miller is a world-renowned computer scientist working in the field of data systems, in particular on data integration, whose pioneering research is recognized widely across academia and industry,” said Raouf Boutaba, Professor and Director of the Cheriton School of Computer Science. “We congratulate her on being named a Canada Excellence Research Chair and very much look forward to her joining us next year.”

Professor Miller received her PhD in computer science from the University of Wisconsin, Madison and bachelor’s degrees in mathematics and cognitive science from MIT. She is an editor-in-chief of the VLDB Journal and former president of the non-profit Very Large Data Base Foundation. 

Professor Miller’s research combines theoretical elegance with industrial impact for which she has received many prestigious awards, honours and citations. Among them, she is a Fellow of the Association for Computing Machinery, a Fellow of the Royal Society of Canada, and a recipient of a US Presidential Early Career Award for Scientists and Engineers, the highest honour bestowed by the US government to outstanding scientists and engineers beginning their careers. She has also received a National Science Foundation CAREER Award, an Ontario Premier’s Research Excellence Award, and an IBM Faculty Award. In 2018, she received the VLDB Women in Database Research Award, which recognizes a woman researcher who has made significant technical contributions to the field, and the 2020 CS-Can | Info-Can Lifetime Achievement Award in Computer Science. She formerly held the Bell Canada Chair of Information Systems at the University of Toronto. 

Initially, Professor Miller’s research focused on the long-standing open problem of data integration and has achieved the goal of building practical data integration systems adopted by industry. Among the many awards for research that she and her colleagues have received is the 2013 International Conference on Database Theory Test-of-Time Award for Data Exchange: Semantics and Query Answering, a paper praised for “initiating a new line of research, and providing a beautiful conceptual contribution, rich of challenging questions, which has constantly attracted the community in the last ten years, and still attracts many valuable research efforts.” For this research, she and her colleagues also received the 2020 Alonzo Church Award for Outstanding Contributions to Logic and Computation for the paper’s ground-breaking work on laying the logical foundations for data exchange.

Canada Excellence Research Chair in Data Intelligence

Data science is being done over massive data lakes containing datasets from different sources and datasets that have been updated and cleaned repeatedly and often incompletely. This creates a mess of slightly different versions and datasets that may have incomplete or ambiguous semantics. An important challenge in doing trustworthy data science is to enable scientists to find relevant datasets, recover and understand the meaning of different datasets and their versions, and integrate data in a way that fully and faithfully recovers all possible connections between facts. Data intelligence encompasses both the theory and practice of automating and scaling the intelligent, reliable, and trustworthy use of big data for data science and artificial intelligence. 

As the Canada Excellence Research Chair in Data Intelligence, Professor Miller, her students and collaborators will continue to develop the mathematical foundations and methods to understand when data preparation or curation solutions are correct; develop preparation and curation methods that are correct, explainable, and reproducible; and develop frameworks for data curation that help data scientists document, share, and reproduce complex data preparation and curation processes. 

Data intelligence is concerned with the data preparation and data engineering portion of data science. Nonetheless, machine learning, language models, and knowledge bases are important tools in recovering the semantics or meaning of data. Professor Miller will develop the theory and practice of how to employ these and other foundational tools, including network science and data management, to create principled data curation solutions. This research is critical to ensuring the insights we gain from data are valid and unbiased. Data intelligence will help to ensure that we can use data wisely to understand possible inequity in the data rather than blindly building models that repeat past inequities. As a recognized leader in efforts to increase equity, diversity and inclusion, Professor Miller will work to expand the number of data science trainees from marginalized groups by exposing them to the power of telling stories through the careful and wise use of data.

  1. 2023 (65)
    1. December (1)
    2. November (7)
    3. October (7)
    4. September (2)
    5. August (3)
    6. July (7)
    7. June (8)
    8. May (9)
    9. April (6)
    10. March (7)
    11. February (4)
    12. January (4)
  2. 2022 (63)
    1. December (2)
    2. November (7)
    3. October (6)
    4. September (6)
    5. August (1)
    6. July (3)
    7. June (7)
    8. May (8)
    9. April (7)
    10. March (6)
    11. February (6)
    12. January (4)
  3. 2021 (64)
    1. December (2)
    2. November (6)
    3. October (6)
    4. September (4)
    5. August (7)
    6. July (4)
    7. June (8)
    8. May (9)
    9. April (3)
    10. March (8)
    11. February (4)
    12. January (3)
  4. 2020 (73)
  5. 2019 (90)
  6. 2018 (82)
  7. 2017 (50)
  8. 2016 (27)
  9. 2015 (41)
  10. 2014 (32)
  11. 2013 (46)
  12. 2012 (17)
  13. 2011 (20)