Research Areas: My research focuses on optimization, federated learning, and their interplays.

Research Interest: Design and performance analysis of large-scale principle learning algorithm including both theoretical aspects and practical implementations by considering trustworthy machine learning (robustness, fairness, and transparency). A compilation of topics that I am actively thinking about/working on is as follows:

• - Designing and analyzing an optimal federated learning algorithm that addresses challenges related to communication efficiency, real-world statistical data heterogeneity, and computational heterogeneity while ensuring compatibility with the system architecture.
• - Continuing research in optimization to develop novel techniques for accelerating convergence, reducing training time, and minimizing resource requirements in machine learning and deep learning models.