Domain Name

Domain research focuses on understanding and managing variations in data characteristics across different contexts (e.g., tasks, sources, or environments). Current research emphasizes developing robust models and algorithms, such as transformers and diffusion models, that can generalize effectively across domains, often leveraging techniques like domain adaptation, few-shot learning, and contrastive learning. This work is crucial for improving the reliability and applicability of AI systems in real-world scenarios where data is often heterogeneous and limited, impacting fields ranging from natural language processing and computer vision to robotics and scientific computing. The ultimate goal is to build AI systems that are less susceptible to biases and perform consistently across diverse situations.