I am an Assistant Professor in the Department of Statistics at the University of British Columbia (UBC). Prior to joining UBC, I was a Postdoctoral Fellow in Statistics at King Abdullah University of Science and Technology (KAUST), where I was affiliated with the Spatio-Temporal Statistics and Data Science (STSDS) research group led by Prof. Genton. I received my Ph.D. in Statistics from Renmin University of China in 2023. During my doctoral studies, I undertook research visits at Hong Kong Baptist University and KAUST. I earned my B.S. in Statistics from Beijing Institute of Technology, China, in 2018.

My research interests include spatio-temporal statistics, subsampling methods, nonparametric statistics, and Computational Statistics and HPC. My work primarily focuses on the analysis of large- and exa-scale datasets arising from climate and environmental sciences, integrating advanced statistical modeling and high-performance computing.

I’m a co-founder and co-organizer of Spatio-Temporal Statistics and Data Science (STSDS) Online Seminars.

For more details, please refer to my CV.

News

• 2026.08:   I will attend the 2026 Joint Statistical Meetings (Boston) as an invited speaker, presenting work on stochastic generators for high-resolution climate simulations
• 2026.04:   Awarded the NSERC Discovery Grant, along with the Discovery Launch Supplement for Early Career Researchers, to support research on climate data emulation, fusion, and prediction
• 2026.02:   Excited to join the Department of Statistics at the University of British Columbia!
• 2026.02:   Earned the Higher Education Teaching Certificate from Harvard’s Bok Center for Teaching and Learning
• 2025.03:   Our online stochastic generator paper has been selected one of the five finalists for the ADIA Lab Best Paper Award 2024 on Climate Science
• 2024.11:   2024 ACM Gordon Bell Prize for Climate Modelling!!!
• 2024.10:   Online stochastic generators using Slepian bases for regional bivariate wind speed ensembles from ERA5
• 2024.09:   A high-resolution boost for global climate modeling
• 2024.07:   KAUST team selected as ACM Gordon Bell Prize for Climate Modelling finalists

Education

• 2018.09 - 2023.06, Ph.D. in Statistics, Renmin University of China, Beijing, China.
• 2014.09 - 2018.06, B.S. in Statistics, Beijing Institute of Technology, Beijing, China.

Honors and Awards

• 2025.03 One of the five finalists for the ADIA Lab Best Paper Award 2024 on Climate Science
• 2024.11 ACM Gordon Bell Prize for Climate Modelling.
• 2020.12 The second prize of Outstanding Papers, National Forum for Doctoral Students in Statistics.
• 2020.11 Outstanding Poster, RUC Youth Forum on Statistics and Data Science.

Publications

• Yan Song, Wenlin Dai, and Marc G. Genton (2024), “Large-scale low-rank Gaussian process prediction with support points,” Journal of the American Statistical Association, Theory and Methods, published online.
• Sameh Abdulah, Allison H. Baker, George Bosilca, Qinglei Cao, Stefano Castruccio, Marc G. Genton, David E. Keyes, Zubair Khalid, Hatem Ltaief, Yan Song, Georgiy L. Stenchikov, and Ying Sun (2024), “Boosting earth system model outputs and saving petabytes in their storage using exascale climate emulators,” in Proceedings of the International Conference for High Performance Computing, Networking, Storage, and Analysis, IEEE Press, SC ‘24.
• Yan Song, Zubair Khalid, and Marc G. Genton (2024), “Efficient Stochastic Generators with Spherical Harmonic Transformation for High-Resolution Global Climate Simulations from CESM2-LENS2,” Journal of the American Statistical Association, Applications and Case Studies, 119, 2493–2507.
• Maoyu Zhang, Yan Song, and Wenlin Dai (2024), “Fast robust location and scatter estimation: a depth-based method,” Technometrics, 66, 14-27.
• Yan Song and Wenlin Dai (2024), “Deterministic subsampling for logistic regression with massive data,” Computational Statistics, 39, 707-732.
• Xiaoyu Liu, Yan Song, and Kun Zhang (2024), “An exact bootstrap-based bandwidth selection rule for kernel quantile estimators,” Communications in Statistics - Simulation and Computation, 53, 3699–3720.
• Yiping Hong, Yan Song, Sameh Abdulah, Ying Sun, Hatem Ltaief, David E. Keyes, and Marc G. Genton (2023),”The third competition on spatial statistics for large datasets,” Journal of Agricultural, Biological and Environmental Statistics, 28, 618-635.
• Wenlin Dai, Yan Song(co-first), and Dianpeng Wang (2023), “A subsampling method for regression problems based on minimum energy criterion,” Technometrics, 65, 192-205.

Under Revision

• Yan Song, Zubair Khalid, and Marc G. Genton (2025+), “Online stochastic generators using Slepian bases for regional bivariate wind speed ensembles from ERA5,” acceptable after major revision at Journal of the American Statistical Association, Applications and Case Studies.

Talks

• 2026.08, Joint Statistical Meetings, Boston, Massachusetts, USA - Efficient stochastic generators with spherical harmonic transformation for high-resolution global climate simulations, invited speaker.
• 2026.01, Spatio-Temporal Statistics and Data Science (STSDS) Online Seminars - Online stochastic generators using Slepian bases for regional bivariate wind speed ensembles from ERA5.
• 2025.10, KAUST ASA Student Chapter Research Presentation Session, Thuwal, Makkah, KSA - Online stochastic generators using Slepian bases for regional bivariate wind speed ensembles from ERA5.
• 2025.07, The 3rd Joint Conference on Statistics and Data Science in China, Hangzhou, Zhejiang, China - Online stochastic generators using Slepian bases for regional bivariate wind speed ensembles from ERA5.
• 2025.04, ADIA Lab Climate Science Best Paper Finalists Seminar Series - Online stochastic generators using Slepian bases for regional bivariate wind speed ensembles from ERA5.
• 2025.02, University of British Columbia (UBC) Statistics Webinar - Efficient stochastic generators with spherical harmonic transformation for high-resolution global climate simulations.
• 2025.02, University of Georgia (UGA) Statistics Seminar, Athens, Georgia, United States - Efficient stochastic generators with spherical harmonic transformation for high-resolution global climate simulations.
• 2025.02, New Jersey Institute of Technology (NJIT) Statistics Seminar, Newark, New Jersey, United States - Efficient stochastic generators with spherical harmonic transformation for high-resolution global climate simulations.
• 2025.01, NSF National Center for Atmospheric Research (NCAR) Seminar, Boulder, Colorado, United States - Efficient stochastic generators with spherical harmonic transformation for high-resolution global climate simulations.
• 2024.10, KAUST Graduate Seminar, Thuwal, Makkah, Saudi Arabia - Efficient stochastic generators with spherical harmonic transformation for high-resolution global climate simulations from CESM2-LENS2.
• 2024.08, Joint Statistical Meetings, Portland, Oregon, United States - Efficient stochastic generators with spherical harmonic transformation for high-resolution global climate simulations from CESM2-LENS2.
• 2023.11, KAUST Statistics workshop, Thuwal, Makkah, Saudi Arabia - Efficient stochastic generators with spherical harmonic transformation for high-resolution global climate simulations from CESM2-LENS2.
• 2022.11, KAUST Statistics workshop, Thuwal, Makkah, Saudi Arabia - Large-scale low-rank Gaussian process prediction with support points.
• 2020.12, National Forum for Doctoral Students in Statistics, Guangzhou, Guangdong, China - A model-free subsampling method based on minimum energy criterion.
• 2020.11, RUC Youth Forum on Statistics and Data Science, Beijing, China - A model-free subsampling method based on minimum energy criterion.

Teaching

• 2026Spring Special topics course “STATV 547P: Spatial Statistics” at UBC.
• 2024 Half part of short course “Large-Scale Spatial Data Science” at JSM.
• 2024 A part of short course for the Applied Mathematics and Computational Science and Statistics (AMCS-STAT) school.
• 2024.08 One lesson of course STAT 330: Multivariate Statistics at KAUST.
• 2023.04 Teaching assistant of course Spatial Statistics at RUC.
• 2022Fall Teaching assistant of course Asymptotic Statistics at RUC.
• 2021Fall Teaching assistant of course Asymptotic Statistics at RUC.
• 2021Spring Teaching assistant of course Statistical Learning at RUC.
• 2020Spring Teaching assistant of course Stochastic Process at RUC.

Service

Journal Refereeing

Annals of Applied Statistics (*2), Journal of Computational and Graphical Statistics, IEEE Transactions on Signal Processing

Professional Memberships

American Statistical Association (ASA), Section on Statistics and the Environment (ENVR)

International Statistical Institute (ISI), The International Environmetrics Society (TIES)

Other Professional Service

Co-Founder and Co-Organizer, Spatio-Temporal Statistics and Data Science (STSDS) Online Seminars, 2025 – present