To Prospective Applicants (Ph.D., Postdoctoral Researcher, and Master)

I'm interested in doing research in natural language processing (NLP), where I design machine learning algorithms and computational models for tasks on summarization, generation, reasoning, narrative understanding, argument mining, and AI alignment. I'm also interested in applying NLP and machine learning techniques for interdisciplinary subjects, e.g., computational social science and educational technology. Specifically, our group has the following ongoing research projects:

• Abstractive Summarization: We tackle the challenge of extracting key information from large amounts of and long documents. We aim to generate concise, informative, and factual summaries for different types of texts.
• Factual and Controllable Generation: Though large language models have obtained superior performance in text generation, they sometimes fall short of producing factual content. We are interested in building inference-time and fast techniques to improve generation models' factuality, calibration, and controllability.
• AI Alignment: We aim to make large language models more helpful and honest. We are also interested in measuring the potential bias content generated in real-world applications where large language models are employed as well as their influence on users.
• Media Bias Analysis: News media play a vast role not just in supplying information, but in selecting, crafting, and biasing that information to achieve both nonpartisan and partisan goals. We aim to automate media bias detection from news articles, and quantify and further highlight biased content in order to promote the transparency of news production as well as enhance readers’ awareness of media bias. Discourse analysis and opinion mining algorithms are designed to identify and categorize different types of bias, which in turn facilitate the understanding of the prevalence of bias in content produced by media with different ideological leanings. Potentially applications include misinformation detection and characterization, multi-perspective news summarization, etc.
• Narrative Understanding: Narratives are arguably the most effective way to communicate cultures, values, and knowledge. We are interested in media narratives, which play a critical role in echoing, influencing, and reinforcing public opinion. We aim to design a computational framework with a unified narrative representation, grounded in social psychological theories, to facilitate narrative extraction at scale. We will also model how narratives emerge, spread, and gain impacts among target audience.
• Argument Mining: Arguments play an important role for decision-making processes and persuasion. We are interested in understanding how people argue with and influence others, as well as form their own opinions on topics of interest. Especially, we want to discover linguistic patterns that reflect these processes, and use them for social interaction analysis and prediction.

If any of these sounds interesting to you, please check out more about my research and papers at this website. If you find your research agenda might be aligned with mine and are interested in working with me, please fill in this external contact form and (for Ph.D. applicants) apply to Computer Science and Engineering at University of Michigan and mention my name in your application. Once you've submitted the form, feel free to send me an email.