He, Siyu

Can large language models provide useful feedback on research papers? A large-scale empirical analysis

Liang, W., Zhang, Y., Cao, H., Wang, B., Ding, D., Yang, X., Vodrahalli, K., He, S., Smith, D., Yin, Y., et al. (2023) · arXiv

Academic Writing and PublishingTopic Modeling

Cited by 243

CosmoFlow: Using Deep Learning to Learn the Universe at Scale

Mathuriya, A., Bard, D., Mendygral, P., Meadows, L., Arnemann, J., Shao, L., He, S., Karna, T., Moise, D., Pennycook, S. J., et al. (2018) · SC18: International Conference for High Performance Computing, Networking, Storage and Analysis

Parallel Computing and Optimization TechniquesCosmology and Gravitation Theories

Cited by 132

Quantifying large language model usage in scientific papers

Liang, W., Zhang, Y., Wu, Z., Lepp, H., Ji, W., Zhao, X., Cao, H., Liu, S., He, S., Huang, Z., et al. (2025) · Nature Human Behaviour

scientometrics and bibliometrics researchAcademic Writing and Publishing

Cited by 80

Constraining gravity at the largest scales through CMB lensing and galaxy velocities

Pullen, A. R., Alam, S., He, S., & Ho, S. (2016) · Monthly Notices of the Royal Astronomical Society

Cosmology and Gravitation TheoriesGalaxies: Formation, Evolution, Phenomena

Cited by 75

Mapping the Increasing Use of LLMs in Scientific Papers

Liang, W., Zhang, Y., Wu, Z., Lepp, H., Ji, W., Zhao, X., Cao, H., Liu, S., He, S., Huang, Z., et al. (2024) · arXiv

scientometrics and bibliometrics researchAcademic Writing and Publishing

Cited by 55

Squidiff: predicting cellular development and responses to perturbations using a diffusion model

He, S., Zhu, Y., Tavakol, D. N., Ye, H., Lao, Y.-H., Zhu, Z., Xu, C., Chauhan, S., Garty, G., Tomer, R., et al. (2026) · Nature Methods

Single-cell and spatial transcriptomicsGene Regulatory Network Analysis

Cited by 38