معرفی
Fanny Yang is an Assistant Professor in the Computer Science Department at ETH Zurich. She previously held postdoctoral positions at Stanford University and a Junior Fellowship at the Institute for Theoretical Studies at ETH Zurich, advised by Nicolai Meinshausen. Her PhD was completed at the EECS Department of UC Berkeley, supervised by Martin Wainwright.
Research Interests:
- Theoretical foundations of machine learning and statistics, particularly focusing on overparameterized models and high-dimensional data.
- Developing trustworthy ML models with emphasis on distributional robustness, domain generalization, and interpretability.
- Applications in medical diagnostics and treatment effect analysis, aiming to address reliability issues in real-world domains.
Recent Work Trends:
- 2025 Articles: Theoretical analysis of semi-supervised multi-objective learning, test-time scaling with verifiers, and foundation models for efficient randomized experiments.
- 2024 Articles: Studies on robust mixture learning, privacy-preserving data synthesis via optimal transport, confounding quantification in causal inference, and semi-private learning frameworks.
- 2023 Articles: Investigations into active vs. passive learning in high dimensions, inductive bias in noisy interpolation, and semi-supervised novelty detection using model ensembles.
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