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Current Role at StanfordMy role at LCLS at SLAC is SRD Deputy Director for Strategic Development - I aid management to develop FEL science sustainably and to increase the impact of FEL science in the broader scientific community.

Coherent X-ray Imaging (CXI) Instrument lead - Lead one of the scientific instruments at LCLS. CXI is a hard X-ray, in-vacuum instrument which specialized in low signal to noise experiments due to a vacuum sample environment and high X-ray power measurements due to a nanofocus beam which can provide X-ray power up to 10^20W/cm^2

LCLS-II-HE CXI upgrade science lead. LCLS-II-HE is an upgrade of the LCLS X-ray Free Electron Laser which will take the repetition rate from 120Hz to 1MHz. The CXI instrument will undergo a complete upgrade to maximally utilize this unprecedented new source.

Contact Info
- 2575 Sand Hill Rd
- Mailstop 0019
- Menlo Park, California 94025
Mail Code: 0210
mliang@SLAC.Stanford.EDU

Mengyu Liang

Postdoctoral Scholar, Earth System Science

Contact Info
Mail Code: 4216
mliang77@stanford.edu

Nic Liang

Ph.D. Student in Materials Science and Engineering, admitted Autumn 2022

Contact Info
Mail Code: 4034

Norah E. Liang

Fellow in Surgery - Pediatric Surgery

Contact Info
Mail Code: 5148
nliang@stanford.edu

Other Names:

Norah Liang

Pauline Liang

Ph.D. Student in Business Administration, admitted Autumn 2020

Contact Info
Mail Code: 5015
pliang20@stanford.edu

Percy Liang

Associate Professor of Computer Science, Senior Fellow at the Stanford Institute for HAI, and Associate Professor, by courtesy, of Statistics

On Partial Leave from 10/01/2024 To 03/31/2025

BioPercy Liang is an Associate Professor of Computer Science at Stanford University (B.S. from MIT, 2004; Ph.D. from UC Berkeley, 2011) and the director of the Center for Research on Foundation Models (CRFM). He is currently focused on making foundation models (in particular, language models) more accessible through open-source and understandable through rigorous benchmarking. In the past, he has worked on many topics centered on machine learning and natural language processing, including robustness, interpretability, human interaction, learning theory, grounding, semantics, and reasoning. He is also a strong proponent of reproducibility through the creation of CodaLab Worksheets. His awards include the Presidential Early Career Award for Scientists and Engineers (2019), IJCAI Computers and Thought Award (2016), an NSF CAREER Award (2016), a Sloan Research Fellowship (2015), a Microsoft Research Faculty Fellowship (2014), and paper awards at ACL, EMNLP, ICML, COLT, ISMIR, CHI, UIST, and RSS.