Edge AI

Our research centers on Edge AI, aiming to build personalized, trustworthy, and efficient AI that meets the needs of diverse users in real-world environments. We develop learning and inference algorithms that enable AI services across edge devices and collaborative computing systems, addressing challenges arising from heterogeneous data, evolving user needs, safety requirements, and limited resources. Our research spans large language and multimodal models, image/video understanding and generation, and embodied AI, including vision-language-action models. Our research brings together neural representation interpretability, mathematical foundations, and AI systems. We investigate how neural networks represent and process information, and how their internal representations influence model behavior and generalization. By connecting theoretical analysis with empirical investigation, we use these insights to develop principled learning and inference algorithms for personalized, trustworthy, and efficient AI. AI systems must adapt to diverse users, downstream tasks, and continually changing environments. We study how models can learn collaboratively from distributed and heterogeneous data without requiring users or institutions to share their private data. We also investigate how foundation models can adapt to individual needs and acquire new knowledge while retaining previously learned capabilities. Our current research focuses on collaborative/federated learning, continual learning, and LLM post-training, enabling AI to evolve with users and their environments. AI systems should remain safe and reliable as they are trained, adapted, and deployed. We investigate how to preserve safety during fine-tuning and compression, align predictive confidence with actual correctness, an