Yang Chen
University of Michigan, Harvard University, University of Science and Technology of China
Research areas: Physics and Astronomy · Astronomy and Astrophysics · Solar and Space Plasma Dynamics · Ionosphere and magnetosphere dynamics · Solar Radiation and Photovoltaics
0000-0002-9516-8134
About
Yang Chen is an Associate Professor in the Department of Statistics at the University of Michigan and a Research Associate Professor at MIDAS, where her work focuses on statistical methodology and computational algorithms for scientific applications, including Bayesian modeling and computation, hidden Markov and finite mixture models, uncertainty quantification, spatial-temporal data analysis, and machine learning for astronomy and space science. She earned her Ph.D. in Statistics from Harvard University in 2017 and his B.Sc. in Mathematics from the University of Science and Technology of China in 2011.
Employment
Education
- 2011 - 2017
- 2007 - 2011
Projects & Funding
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CLEAR: Center for All-Clear SEP Forecast National Aeronautics and Space Administration2024 - 2028
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Collaborative Research: Evolution of the Global Total Electron Contents (TEC) during Solar Flares Directorate for Geosciences2024 - 2027
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Collaborative Research: DMREF: AI-enabled Automated design of ultrastrong and ultraelastic metallic alloys Directorate for Mathematical & Physical Sciences2023 - 2027
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Space Weather Operational Readiness Development (SWORD) Center National Aeronautics and Space Administration2023 - 2028
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DMS-EPSRC Collaborative Research: Advancing Statistical Foundations and Frontiers for and from Emerging Astronomical Data Challenges Directorate for Mathematical & Physical Sciences2021 - 2024
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Solar Storms and Terrestrial Impacts Center (SOLSTICE) NASA2020 - 2022
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SWQU: NextGen Space Weather Modeling Framework Using Data, Physics and Uncertainty Quantification Directorate for Mathematical & Physical Sciences2020 - 2023
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Collaborative Research: Highly Principled Data Science for Multi-Domain Astronomical Measurements and Analysis Directorate for Mathematical & Physical Sciences2018 - 2021
Publications (109)
- Mouse bio-behavioral phenotyping using a digital homecage framework for long-timescale, high-resolution, and multi-factor data collection and analytics Save
- Real-Time 24-hour Ahead Forecasting of Solar Energetic Particle Events with SEPNET-PRISM-R Save
- Realtime forecasting of solar energetic particle event and proton flux using multi-source solar observations and multi-task deep learning Save
- Improving Geomagnetic Perturbation Forecasts in the Southern Hemisphere via Kernel‐Based Encoding of Mirror Symmetry Save
- ONLINE TENSOR LEARNING: COMPUTATIONAL AND STATISTICAL TRADE-OFFS, ADAPTIVITY AND OPTIMAL REGRET Save
- Calibration and Fusion of GOLD‐Inferred TEC Data With Ground‐Based TEC Save
- Solar Energetic Particle Forecasting With Multi‐Task Deep Learning: SEPNET Save
- Conformalized Tensor Completion with Riemannian Optimization Save
- Operational Implementation of Real-Time SEP Forecasting: R2O2R Activities from the CLEAR Space Weather Center of Excellence Save
- SEPNET: a multi-task deep learning framework for SEP forecasting Save
- On the Optimal Prediction of Extreme Events in Heavy-Tailed Time Series With Applications to Solar Flare Forecasting Save
- Defects and Inconsistencies in Solar Flare Data Sources: Implications for Machine Learning Forecasting Save
- Warming makes 20 boreal species grow earlier but not always taller Save
- Improving Geomagnetic Perturbation Forecasts in the Southern Hemisphere via Kernel-Based Encoding of Mirror Symmetry Save
- Making high-order asymptotics practical: correcting goodness-of-fit test for astronomical count data Save
- GeoDGP: One‐Hour Ahead Global Probabilistic Geomagnetic Perturbation Forecasting Using Deep Gaussian Process Save
- Maximum-likelihood Regression with Systematic Errors for Astronomy and the Physical Sciences. I. Methodology and Goodness-of-fit Statistic of Poisson Data Save
- Maximum-likelihood Regression with Systematic Errors for Astronomy and the Physical Sciences. II. Hypothesis Testing of Nested Model Components for Poisson Data Save
- Discussion on “Poisson-FOCuS: An Efficient Online Method for Detecting Count Bursts with Application to Gamma Ray Burst Detection” by Ward, Dilillo, Eckley and Fearnhead Save
- Determine the Number of States in Hidden Markov Models via Marginal Likelihood Save