Research Interests:
• Distributed Machine Learning Algorithms:
Designing scalable and efficient machine learning algorithms for distributed computing environments,
focusing on real-world applications in data science.
• Federated Learning and Privacy-Preserving Techniques:
Exploring secure and collaborative learning frameworks, such as vertical federated learning, to protect data privacy in distributed settings.
• Signal Processing for Machine Learning:
Integrating advanced signal processing methods to enhance feature extraction, data preprocessing, and model performance in machine learning.
• Machine Unlearning:
Investigating methods to remove learned data from models efficiently, enhancing data privacy and compliance with regulations like GDPR.
• Recommender Systems:
Developing personalized recommender systems that leverage advanced machine learning techniques to improve user experience and engagement.
• Explainable Artificial Intelligence:
Developing methods and tools to make AI systems transparent, interpretable, and trustworthy, enabling users to understand and trust their decisions.
• Data Science Solutions for Industry:
Applying data science principles to solve complex, practical problems, emphasizing innovation in predictive modeling, data-driven decision-making,
and business insights.
• Statistical Learning and Model Interpretability:
Developing interpretable machine learning models and advancing statistical learning theories for more transparent and reliable outcomes.