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Text Information Management and Analysis Group |
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Our research projects are mostly related to the general goal of building intelligent systems to leverage big data, especially big text data, to augment human intelligence and support complex user tasks (e.g., complext decisions). We are interested in developing general models, theoretically sound algorithms, and systems for analyzing large amounts of data (especially natural language text data) to discover latent knowledge and derive new insights from the data, and applying such general techniques to build innovative applications in specific domains such as healthcare, education, and scientific discovery, to support all kinds of user tasks.
We emphasize optimization of human-AI collaboration and maximization of the combined intelligence of humans and AI systems. To this end, we study how to mathematically model and simulate users, which is required for both evaluating and training interactive intelligent systems. Leveraging user modeling and simulation, we study how to optimize sequential decisions for an intelligent agent to interact with users to support their tasks in a personalized manner while minimizing a user's overall effort. To enable explainable AI (XAI) and trustworthy AI, we are also interested in studying human-like natural language processing techniques, especially neuro-symbolic models. We apply general models and algorithms to develop specific intelligent task agents in multiple application domains such as healthcare, education, and scientific discovery. See the list of TIMAN projects for details about the projects that we have completed and our current projects. |