About

I am a PhD student at Cornell University in the Department of Computer Science, where I am advised by Prof. Chris De Sa. I also work closely with Prof. Vitaly Shmatikov at Cornell Tech. I received my bachelor degree in Mathematics from Shanghai Jiao Tong University in July 2019, where I am fourtunated to work with Prof. John E. Hopcroft and Huan Long.

I'm interested in general machine learning topics, particularly my work spans in the following areas:
(1) Hyperbolic deep learning, adopting hyperbolic geometry for more expressive and accurate models;
(2) Private and secure ML, such as (personalized) federated learning, defending adversarial examples, developing privacy-preserving algorithms and mitigating the tradeoff between accurcy, robustness & privacy.

We host the Machine Learning Security, Privacy & Fairness group weekly in Ithaca campus [Past Schedule].

[Curriculum Vitae] [Google Scholar] [Github] [LinkedIn]

Education & Experience

Research Intern June 2020 - Aug. 2022

It's my fortune to intern at Apple MLPT Privacy team working with experienced reserachers and engineers including Ulfar Erlingsson, Vojta Jina, Martin Pelikan, Omid Javidbakht and etc., to look into some topics on federated learning and ml privacy.

Research Intern July 2018 - Dec. 2018

Happy to get the research intern opportunity in Cornell CS from Prof. Kilian Q. Weinberger, to work on defenses against adversarial examples and simplifying GCN for NLP tasks. I also work closely with Prof. Chris De Sa on developing numerically robust and accurate models for hyperbolic embeddings of graphs.

B.S. in Mathematics Sep. 2015 - July 2019

It's my great honor to major in Mathematics and Applied Mathematics (ZhiYuan honours programme) at Shanghai Jiao Tong University, where I am so lucky to work with Prof. John E. Hopcroft and Huan Long, we analyzed both theoretically and experimentally of the intrinsic dimension of the manifolds embedded in neural networks.

Recent Projects

Publications

Tao Yu, Christopher De Sa. "HyLa: Hyperbolic Laplacian Features For Graph Learning". (Preprint)

Tao Yu*, Yichi Zhang*, Zhiru Zhang, Christopher De Sa. "Understanding Hyperdimensional Computing for Parallel Single-Pass Learning". (Preprint)

Tao Yu, Christopher De Sa. "Representing Hyperbolic Space Accurately using Multi-Component Floats". In 35th Conference on Neural Information Processing Systems (NeurIPS 2021)

Tao Yu, Eugene Bagdasaryan, Vitaly Shmatikov. "Salvaging Federated Learning by Local Adaptation", [Code], (Preprint)

Tao Yu, Christopher De Sa. "Numerically Accurate Hyperbolic Embeddings Using Tiling-Based Models", Spotlight, [Compression Code, Learning Code, Poster]. In 33rd Conference on Neural Information Processing Systems (NeurIPS 2019)

Tao Yu*, Shengyuan Hu*, Chuan Guo, Wei-Lun Chao, Kilian Q. Weinberger. "A New Defense Against Adversarial Images: Turning a Weakness into a Strength", [Code, Poster]. In 33rd Conference on Neural Information Processing Systems (NeurIPS 2019)

Felix Wu*, Tianyi Zhang*, Amauri Holanda de Souza Jr.*, Christopher Fifty, Tao Yu, Kilian Q. Weinberger. "Simplifying Graph Convolutional Networks", [Code]. In 36th International Conference on Machine Learning (ICML 2019).

Tao Yu, Huan long, John Hopcroft. "Curvature-based Comparison of Two Neural Networks". In 24th International Conference on Pattern Recognition (ICPR 2018).

Tao Yu, Yu Qiao, Huan Long. "Knowledge-based Fully Convolutional Network and Its Application in Segmentation of Lung CT Images". (Technical Report)



Professional Activities

Reviewer

ICML, NeurIPS, ICLR, AISTATS.

Talks

NeurIPS 2019, "Numerically Accurate Hyperbolic Embeddings Using Tiling-Based Models", Slides.