About
I am a software engineer at Meta. My research interest is in language models, federated learning and differential privacy. I am also interested in lower bounds and hardness results in computational complexity. Previously, I worked at Snap Inc. and ByteDance, focusing on privacy-preserving machine learning and large language models. Before that, I received my Ph.D. in the Paul G. Allen School of Computer Science & Engineering at the University of Washington in 2020, advised by Professors Paul Beame and Kevin Jamieson. Even earlier, I received my B.E. from IIIS (Yao Class) at Tsinghua University in 2014.
Publication
(*alphabetic author order)
Number Balancing is as hard as Minkowski′s Theorem and Shortest Vector, Rebecca Hoberg*, Harishchandra Ramadas*, Thomas Rothvoss*, Xin Yang*, IPCO 2017.
Canaries in the Network, Danyang Zhuo, Qiao Zhang, Xin Yang, Vincent Liu, HotNets 2016.
Time-Space Tradeoffs for Learning Finite Functions from Random Evaluations, with Applications to Polynomials, Paul Beame*, Shayan Oveis Gharan*, Xin Yang*, COLT 2018.
On the Bias of Reed-Muller Codes over Odd Prime Fields, Paul Beame*, Shayan Oveis Gharan*, Xin Yang*, SIAM J. Discrete Math 34(2).
Near optimal algorithm for approximating John′s Ellipsoid, Michael B. Cohen*, Ben Cousins*, Yin Tat Lee*, Xin Yang*, COLT 2019.
Total Least Squares Regression in Input Sparsity Time, Huian Diao*, Zhao Song*, David Woodruff*, Xin Yang*, NeurIPS 2019.
Sketching Transformed Matrices with Applications to Natural Language Processing, Yingyu Liang*, Zhao Song*, Mengdi Wang*, Lin Yang*, Xin Yang*, AISTATS 2020.
Label Leakage and Protection in Two-party Split Learning, Oscar Li, Jiankai Sun, Xin Yang, Weihao Gao, Hongyi Zhang, Junyuan Xie, Virginia Smith, Chong Wang, ICLR 2022.
FL-NTK: A Neural Tangent Kernel-based Framework for Federated Learning Convergence Analysis, Baihe Huang*, Xiaoxiao Li*, Zhao Song*, Xin Yang*, ICML 2021.
Differentially Private Multi-Party Data Release for Linear Regression, Ruihan Wu, Xin Yang, Yuanshun Yao, Jiankai Sun, Tianyi Liu, Kilian Q. Weinberger, Chong Wang, UAI 2022.
DPAUC: Differentially private auc computation in federated learning, Jiankai Sun, Xin Yang, Yuanshun Yao, Junyuan Xie, Di Wu, Chong Wang, AAAI 2023.
Sketching meets differential privacy: Fast algorithm for dynamic kronecker projection maintenance, Zhao Song*, Xin Yang*, Yuanyuan Yang*, Lichen Zhang*, ICML 2023.
Faster Algorithms for Structured John Ellipsoid Computation, Yang Cao*, Xiaoyu Li*, Zhao Song*, Xin Yang*, Tianyi Zhou*, NeurIPS 2025.