Our research spans to different aspects of Big Data and Data Science, including data management, analytics, and mining, for which we aim to find efficient, accurate, and scalable algorithmic solutions.
Responsible data science and algorithmic fairness
is our current research focus.
We also work on projects related to ranking and representatives, data management for ML, web databases, social networks, knowledge graphs, computational fact checking, etc
Please refer to our projects
for more info.
Theory v.s. Application:
Our research is in the intersection of theory and applcation.
Some projects are more theory-heavy and some application-oriented.
In all projects, in addition to novelty
, both technical depth
(theory) and motivation
(applcation) are important and should be considered.
To give you an idea, the following sketch shows the distribution of some of our research between theory and application.
I suggest you read my blog post about "Enabling Responsible Data Science in Practice"