International Internet2 connectivity and performance in medical imaging applications: Bridging the Americas to Asia. - Brent J. Liu; Zheng Zhou 002; Marco A. Gutierrez 001; Jorge Documet; Lawrence Chan; H. K. Huang (J. High Speed Networks, 2007)

The role of a Data Grid in worldwide imaging-based clinical trials. - Zheng Zhou 002; Marco A. Gutierrez 001; Jorge Documet; Lawrence Chan; H. K. Huang; Brent J. Liu (J. High Speed Networks, 2007)

Development and evaluation of a compartmental picture archiving and communications system model for integration and visualization of multidisciplinary biomedical data to facilitate student learning in an integrative health clinic. - Meyrick Chow; Lawrence Chan (Comput. Educ., 2010)

Clinical Decision Support using Traditional Chinese Medicine (TCM) Ontology and Text Similarity. - Meyrick Chow; Lawrence Chan (Nursing Informatics, 2012)

The Assistive Multi-Armed Bandit. - Lawrence Chan; Dylan Hadfield-Menell; Siddhartha S. Srinivasa; Anca D. Dragan (HRI, 2019)

Accounting for Human Learning when Inferring Human Preferences. - Harry Giles; Lawrence Chan (CoRR, 2020)

A Fast-Processing Pipeline for Three-dimensional Visualization of Acute Ischemic Stroke lesion topography. - Huiling Shao; Lawrence Chan; Fiona Chen; Qilin Ma; Zhiyu Shao; Heng Du (BIBM, 2021)

Optimal Cost Design for Model Predictive Control. - Avik Jain; Lawrence Chan; Daniel S. Brown; Anca D. Dragan (L4DC, 2021)

Human irrationality: both bad and good for reward inference. - Lawrence Chan; Andrew Critch; Anca D. Dragan (CoRR, 2021)

Adversarial training for high-stakes reliability. - Daniel M. Ziegler; Seraphina Nix; Lawrence Chan; Tim Bauman; Peter Schmidt-Nielsen; Tao Lin; Adam Scherlis; Noa Nabeshima; Ben Weinstein-Raun; Daniel de Haas; Buck Shlegeris; Nate Thomas (NeurIPS, 2022)

Progress measures for grokking via mechanistic interpretability. - Neel Nanda; Lawrence Chan; Tom Lieberum; Jess Smith; Jacob Steinhardt (ICLR, 2023)

A Toy Model of Universality: Reverse Engineering how Networks Learn Group Operations. - Bilal Chughtai; Lawrence Chan; Neel Nanda (ICML, 2023)

Evaluating Language-Model Agents on Realistic Autonomous Tasks. - Megan Kinniment; Lucas Jun Koba Sato; Haoxing Du; Brian Goodrich; Max Hasin; Lawrence Chan; Luke Harold Miles; Tao R. Lin; Hjalmar Wijk; Joel Burget; Aaron Ho; Elizabeth Barnes; et al. (CoRR, 2023)

The Alignment Problem from a Deep Learning Perspective. - Richard Ngo; Lawrence Chan; Sören Mindermann (ICLR, 2024)

Compact Proofs of Model Performance via Mechanistic Interpretability. - Jason Gross; Rajashree Agrawal; Thomas Kwa; Euan Ong; Chun Hei Yip; Alex Gibson; Soufiane Noubir; Lawrence Chan (NeurIPS, 2024)

Mathematical Models of Computation in Superposition. - Kaarel Hänni; Jake Mendel; Dmitry Vaintrob; Lawrence Chan (CoRR, 2024)

RE-Bench: Evaluating frontier AI R&D capabilities of language model agents against human experts. - Hjalmar Wijk; Tao Lin; Joel Becker; Sami Jawhar; Neev Parikh; Thomas Broadley; Lawrence Chan; Michael Chen; Josh Clymer; Jai Dhyani; Elena Ericheva; Katharyn Garcia; et al. (CoRR, 2024)

Modular addition without black-boxes: Compressing explanations of MLPs that compute numerical integration. - Chun Hei Yip; Rajashree Agrawal; Lawrence Chan; Jason Gross (CoRR, 2024)

Language Models Are Better Than Humans at Next-token Prediction. - Buck Shlegeris; Fabien Roger; Lawrence Chan; Euan McLean (Trans. Mach. Learn. Res., 2024)

Measuring AI Ability to Complete Long Tasks. - Thomas Kwa; Ben West; Joel Becker; Amy Deng; Katharyn Garcia; Max Hasin; Sami Jawhar; Megan Kinniment; Nate Rush; Sydney von Arx; Ryan Bloom; Thomas Broadley; et al. (CoRR, 2025)

HCAST: Human-Calibrated Autonomy Software Tasks. - David Rein; Joel Becker; Amy Deng; Seraphina Nix; Chris Canal; Daniel O'Connel; Pip Arnott; Ryan Bloom; Thomas Broadley; Katharyn Garcia; Brian Goodrich; Max Hasin; et al. (CoRR, 2025)