My research is at the intersection of machine learning, statistics, and signal processing. I like to formulate and address novel research problems that are often inspired by foundational thoughts in statistics, information theory, signal processing, and optimization. I address those research problems often by establishing new mathematical models, using asymptotic statistics and probability theory, and performing real-world data studies.
As AI rapidly transitions from research labs to a broad spectrum of disciplines and industries, my research focuses on the following interconnected directions:
- AI Foundations to unravel fundamental principles to augment the interpretability and trustworthiness of data-driven decisions;
- Efficient Training and Deployment of Large Models to make AI more scalable and accessible to the general public;
- Decentralized and Collaborative AI to transcend the limitations of single-machine capabilities by catalyzing machine-to-machine interactions across networks;
- AI Safety to address emerging societal concerns related to privacy, security, and watermarking in the training and deployment of AI models.
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