
About
Christopher Manning is the Thomas M. Siebel Professor in Machine Learning and Professor of Linguistics and Computer Science at Stanford University. He serves as a Founder and Associate Director of the Stanford Institute for Human-Centered Artificial Intelligence (HAI), and former Director of the Stanford Artificial Intelligence Laboratory (SAIL). His research spans Natural Language Processing (NLP), Deep Learning, computational linguistics, and open-source tools like Stanford CoreNLP and Stanza. Manning has pioneered work on sentiment analysis, neural machine translation, and the GloVe model. He holds a B.A. from ANU and a Ph.D. from Stanford, with an honorary doctorate from U. Amsterdam (2023). He previously held faculty roles at Carnegie Mellon University and the University of Sydney.
Education:
- B.A. (Hons), Australian National University, 1989
- Ph.D., Stanford University, 1994
- Honorary Doctorate, University of Amsterdam, 2023
Research Interests: Manning's work focuses on robust NLP techniques, including deep learning for language understanding, dependency parsing, Universal Dependencies, and low-resource language processing. His contributions span theory (e.g., syntactic parsing), applications (e.g., machine translation), and education (e.g., Foundations of Statistical NLP). Recent efforts include studying large language models' emergent properties and addressing ethical AI challenges like hallucination detection.
Awards:
- IEEE John von Neumann Medal (2024)
- ACL Test of Time Award
- ACM/AAAI/AACL Fellowships
- Elected to American Academy of Arts and Sciences (2025)
- National Academy of Engineering (2025)
Advising & Labs: Advisor to numerous PhD graduates (see his dissertations page). Leads the Stanford NLP Group and collaborates with SAIL and HAI on projects like legal AI tools and scalable NLP education.
Notable Projects: Development of Stanford Dependencies, Universal Dependencies, and open-source NLP libraries. Co-created CS224N (Deep Learning for NLP), one of the most-watched online courses in the field.
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