Hello! I’m Linying Yang, a postdoctoral scholar in the Department of Biostatistics at the University of Washington, working with Professor Ting Ye. I received my DPhil in Statistics from University of Oxford, where I was supervised by Professor Robin Evans.
My research focuses on causal inference, with particular interests in distribution shifts, data fusion, data simulation with generative models and sequential modeling. I aim to develop methods with robust statistical guarantees and demonstrate their usefulness in real‑world applications.
I hold an M.S. in Computational & Mathematical Engineering from Stanford University and a B.S. in Statistics from Fudan University. Before beginning my doctorate, I worked as an AI Applied Scientist at Microsoft Research New England.
News
- 2026/11: Started my postdoctoral position in the Department of Biostatistics at the University of Washington, working with Professor Ting Ye.
- 2026/10: New preprint: Causal Robustness from Synthetic Environments
- 2026/08: Passed my PhD viva (thesis defense) with no corrections. Many thanks to my examiners, Professor Frank Windmeijer and Professor Nicolai Meinshausen.
- 2026/07: Invited talk: Data Fusion with Equivalence Testing at PCIC 2026.
- 2026/06: Award: Contribution to the Department Award, Department of Statistics, Oxford.
- 2026/05: Organizer: Invited session on The Use and Generation of Synthetic Data in Causal Inference at ACIC 2026. Also presenting posters on Frengression and Data Fusion with Equivalence Testing.
- 2026/03: New preprint: Data Fusion with Distributional Equivalence Test-then-pool.
- 2026/03: Academic visit: Seminar of Statistics, ETH Zurich, hosted by Professor Peter Bühlmann.
- 2026/01: Co-organizer: EuroCIM 2026 in Oxford.
- 2025/12: Talk: Generative models for causal inference at the first International New Researchers Conference.
- 2025/10: Award: ICSDS Student Travel Award.
- 2025/09: Talk: Frugal, Flexible, Faithful: Causal Data Simulation via Frengression at JICI 2025.
- 2025/08: New preprint: Frugal, Flexible, Faithful: Causal Data Simulation via Frengression. See also Robin’s blog post for a nice overview.
- 2025/07: Invited talk: Frengression at the QMUL Causal Machine Learning Workshop.
- 2025/06: Award: Jesus College Graduate Scholarship.
- 2025/05: Paper accepted: Testing Generalizability in Causal Inference at UAI 2025.
- 2025/05: Talk: Outcome‑Informed Weighting for Robust ATE Estimation at ACIC 2025.
- 2025/04: Best Student Poster Award at EuroCIM 2025 for Outcome‑Informed Weighting for Robust ATE Estimation.
