Research projects

My current research is centered around four main areas: (1) Privacy-preserving Data Analysis, (2) Federated Learning(3) Trustworthy AI; and (4) AI security and privacy.

These research themes also guide the master’s and Ph.D. projects I supervise. For more detailed information on my ongoing research projects, please explore the following sections. If any of these research topics pique your interest, please don’t hesitate to get in touch with me. Your input and collaboration are always welcome!

research projects

Federated learning is a decentralized approach to machine learning where multiple devices or servers collaboratively train a shared model without centralizing data.

Unbiased Private AI

Unbiased private AI refers to the development and deployment of artificial intelligence systems that prioritize fairness, privacy, and accuracy. These systems are designed to minimize biases in both the data they are trained on and the outcomes they produce.

Location privacy

Indoor location privacy is the protection of individuals’ location data when they are within enclosed spaces, such as buildings or facilities. It addresses concerns related to tracking, surveillance, and data privacy within indoor environments.

Game Theory in Distributed Learning

Game theory in distributed learning involves strategic interactions among multiple participants, each aiming to optimize their individual learning objectives while contributing to the collective improvement of a shared model. 

Federated Learning

Federated learning is a decentralized approach to machine learning where multiple devices or servers collaboratively train a shared model without centralizing data.