Chenyan Xiong

I am an Associate Professor at the Language Technologies Institute (LTI), affiliated with the Machine Learning Department (MLD) in the School of Computer Science at Carnegie Mellon University. I am also a member of the CMU Foundation and Language Model Center (FLAME). From 2018 to 2023, I worked at Microsoft Research Redmond on conversational search, dense retrieval, and large-scale pretraining, contributing both scientific advances and real-world impact across production systems serving billions of users and trillions of web pages. I received my Ph.D. from LTI, CMU, in 2018 under the supervision of Jamie Callan, focusing on integrating knowledge graphs and deep learning into search engines. Prior to that, I completed my undergraduate studies at Wuhan University in 2009, earned a master’s degree at the Institute of Software, Chinese Academy of Sciences in 2012, and spent two years interning at Microsoft Research Asia in Tie-Yan Liu’s group.

My research group welcomes Ph.D. students, postdoctoral researchers, and undergraduate/graduate interns. Recent publications are available at the CX Research Group at CMU. If our research interests align, please feel free to reach out.

• Ph.D. students: I primarily review applicants in LTI’s Ph.D. program. You can list me as a potential advisor in the application system and send me an email to ensure I see your materials.
• Postdocs: Please contact me directly via email.
• Current CMU students: Fill out this form and email me. I particularly enjoy working with students who share my research interests, have well-defined directions, and value long-term impact or real-world applications.

Research Interests

My recent work focuses on foundation and large language models, with particular emphasis on improving the speed–quality trade-offs in pretraining, exploring new scaling frontiers, and enabling new capabilities for next-generation GenAI applications. Current directions in my group include:

Foundation Model Science

• Advancing the Pareto frontier of scaling laws (speed–quality) through data-centric strategies, new architectures, and model–infrastructure co-design.
• Exploring scaling frontiers with synthetic data, innovative training methods, and feedback-driven learning.
• Developing foundation models with new capabilities for emerging applications in multimodality, vision–language–action, and model-as-agents.

GenAI-Native Information Retrieval

• Building agentic search and recommendation systems leveraging the new capabilities of foundation models.
• Exploring the ecosystem of the agentic web, including new organization of the digital world, new economic models, and fair revenue sharing.
• Supporting community research on agentic information systems with retrieval and large-scale training infrastructures.

New GenAI-Enabled Scenarios

• Designing healthcare foundation models to support clinical applications such as disease risk prediction and clinician copilots for improved patient outcomes.
• Developing new context learning paradigms for agent and test-time scaling in various applications.
• Adapting foundation models to verticals such as finance, robotics, and sports.