Eugenie Lai


Email: eugenie.y.lai [at]
Twitter: @EugenieLai
GitHub: eugenieshine
CV, resume

I am a senior undergraduate student in the Combined Major of Business and Computer Science (BUCS) program at the University of British Columbia (UBC) Sauder School of Business. I am currently a full-time research assistant at the UBC Data Management and Mining Lab, supervised by Dr. Rachel Pottinger and postdoc researcher Dr. Mostafa Milani. Last summer, I was supervised by Dr. Raymond Ng in the UBC Data Science for Social Good (DSSG) program.

My current research focuses on databases while applying concepts of visualization, machine learning, and HCI to help users interact with and make sense of data. Today, database systems provide a vital infrastructure for users to access high volumes of data in a variety of applications. However, both field-specific and database-related expertise are required for a user to interact with such database applications. This need of the users with an incomplete skillset sparks my urge to centre my work around the theme of facilitating user interaction with databases, especially in knowledge exploration.

My pronouns are she/her/hers.

NEWS: Check out my post on how SIGMOD 2020 changed my view on my research interests and grad studies.

Research Projects


QueryTeller is a system that suggests customized queries to users by predicting user intent. Database-related expertise is required for users to interact with database applications. However, it takes years of experience for non-experts to learn how to formulate effective queries and understand the schema of a particular database application. QueryTeller seeks to make databases more accessible for users with a database-related incomplete skillset. Our system leverages SQL query workloads, as a collective knowledge exploration history of all past users, to learn query representations and recommend the next queries that users may be interested in. [work-in-progress]


Pastwatch is a system that helps users understand query answers by summarizing, explaining, and visualizing query provenance. Data provenance is any information about the origin of data and the process that leads to its creation. The provenance of a query over a database is a collection of the data in the database that contributed to the query answer. While comprehensive, query provenance remains large and overwhelming. Hence the burden is on users to query and explore query results via different data manipulation languages. Our system helps users explore and interact with their database by providing novel insights into their query results using query provenance.


Summarizing Provenance of Aggregation Query Results in Relational Databases [link]
Omar AlOmeir, Eugenie Y. Lai, Mostafa Milani, and Rachel Pottinger
Submitted to IEEE International Conference on Data Engineering, 2021 (ICDE ‘21)

Pastwatch: On the Usability of Provenance Data in Relational Databases [short paper] [link]
Omar AlOmeir, Eugenie Y. Lai, Mostafa Milani, and Rachel Pottinger
To Appear in IEEE International Conference on Data Engineering, 2020 (ICDE ‘20)


I write about things I did on the way to discover my research interests.


This is my favourate way to destress.