معرفی
Dana Yang is an Assistant Professor in the Department of Statistics and Data Science at Cornell University. She joined Cornell in Spring 2022 after completing a Simons-Berkeley fellowship focusing on computational complexity of statistical inference at UC Berkeley. Previously, she was a postdoctoral associate at Duke University’s Fuqua School of Business. Her education includes a B.S. in Mathematics from Tsinghua University and M.A./Ph.D. in Statistics from Yale University.
Yang’s research interests span statistical inference, machine learning, and computational complexity. She focuses on problems involving planted structures (e.g., matching recovery), privacy-preserving algorithms, high-dimensional statistics, and graph theory. Her work bridges theoretical foundations with practical applications in data science.
Her recent articles explore phase transitions in statistical recovery, private convex optimization, and algorithmic fairness. Notable contributions include studies on planted matching problems, community detection efficiency, and secure sequential learning protocols.
No scientific awards were explicitly mentioned in the provided materials. Her advising history and grant involvement remain unspecified in the current data. Dana Yang is affiliated with Cornell’s Comstock Hall facility, though specific lab or team affiliations were not detailed.
