ÃÛÌÒTV

• Federated Learning
• Adversarial Machine Learning
• Data Privacy
• Distributed Data Synthesis
• Machine Unlearning

My research interests encompass three major domains, each presenting a wide range of advanced possibilities. In the realm of Dependable and Secure Machine Learning, I explore pivotal concepts including Federated Learning, Inference Attacks, Differential Privacy, and the interesting frontier of Machine Unlearning. Additionally, my expertise extends to Distributed Data Synthesis, involving the development of Tabular Data Synthesis Models, the nuanced practice of Distributed Learning using Tabular GANs, and the rigorous evaluation of Synthetic Data. Beyond these pursuits, I'm strongly dedicated to propelling application-oriented and Interdisciplinary Research initiatives associated with privacy and security in AI applications. This dedication is rooted in my extensive involvement in collaborative projects that bring together experts from diverse backgrounds, fostering a comprehensive approach to intricate challenges.

My research agenda encompasses, but is not restricted to, the following areas:

• Machine Unlearning for AI applications based on tabular data.
• Machine Unlearning for Federated Learning systems.
• Privacy attacks in Machine/Federated Learning.
• Federated Learning for Smart Home applications.
• Adversarial attacks against Large Language Models.