My research interests are general topics in machine learning which include but are not limited to: differential privacy, fairness and federated learning. Currently, I am working on building connection between differential privacy and fairness for learning and decision problems. Namely, I focus on the following two directions. First, how privacy constraints affect the individual/group fairness? What are the potential factors and what are the mitigation solutions ? Second, how can we develop effective and efficient private fair models that protect the group information during learning.

Check out my publication page to see my recent papers.