Shengyuan Hu
I am currently a third-year PhD student in Machine Learning Department at Carnegie Mellon University advised by Virginia Smith. Prior to that, I did my BA at Cornell University double major in Computer Science and Mathematics.
My research interest lies in differential privacy, robustness, algorithmic fairness, and in particular, their applications in building trustworthy federated learning methods. My works focus on studying analytical formulations and efficient methods that provide both theoretical guarantees and strong empirical performance on real world applications to handle these issues in federated learning.
Research
Trustworthy Federated Learning
Federated Learning as Network Effect Game
Dung Daniel Ngo, Shengyuan Hu, Shuran Zheng, Virginia Smith, Steven Wu
Preprint
[PDF]
FedSynth: Gradient Compression via Synthetic Data in Federated Learning
Shengyuan Hu, Jack Goetz, Kshitiz Malik, Hongyuan Zhan, Zhe Liu, Yue Liu
Presented at FL-NeurIPS’22 Workshop
[PDF]
On Privacy and Personalization in Cross-Silo Federated Learning
Ziyu Liu, Shengyuan Hu, Steven Wu, Virginia Smith
NeurIPS 2022
[PDF] [Code]
Fair Federated Learning via Bounded Group Loss
Shengyuan Hu, Steven Wu, Virginia Smith
Preprint
Best Paper Award at ICLR 2022 Workshop on Socially Responsible Machine Learning (SRML)
[PDF]
Private Multi-Task Learning: Formulation and Applications to Federated Learning
Shengyuan Hu, Steven Wu, Virginia Smith
Preprint
[PDF]
Ditto: Fair and Robust Federated Learning Through Personalization
Tian Li, Shengyuan Hu, Ahmad Beirami, Virginia Smith
ICML 2021
Best Paper Award at ICLR 2021 Secure ML Workshop
[PDF] [Code(TensorFlow)] [Code(PyTorch)]
Adversarial Robustness
A New Defense Against Adversarial Images: Turning a Weakness into a Strength
Shengyuan Hu*, Tao Yu*, Chuan Guo, Wei-Lun Chao, Kilian Q. Weinberger
NeurIPS 2020
[PDF] [Code]
Others
Selection pressure on the rhizosphere microbiome can alter nitrogen use efficiency and seed yield in Brassica rapa
Joshua Garcia, Joshua Garcia, LiPing Wei, Liang Cheng, Shengyuan Hu, Jed Sparks, James Giovannoni, Jenny Kao-Kniffin
Nature Communications Biology
[PDF]
Professional Service
- Reviewer for ICML (2021-2023)
- Reviewer for ICLR (2021-2023)
- Reviewer For NeurIPS (2021-2022)
- Reviewer for TMLR
- PC Member for NeurIPS 2022 Workshop on Trustworthy and Socially Responsible Machine Learning (TSRML)
- PC Member for FL4NLP@ACL 2022 Workshop
- Reviewer for IEEE Network Magazine
Industrial Experience
Research Intern (2021)
Meta Platform, Inc.
Pittsburgh PA (Remote)
Research Enigneer Intern (2019)
Uber Technologies, Inc.
Pittsburgh, PA
Software Engineer Intern (2018)
MasterClass
San Francisco, CA
Teaching Experience
I have served as a Teaching Assistant at Cornell and CMU for the following courses:
- 10-714 Deep Learning System (CMU, 2022)
- 10-605 ML with Large Datasets (CMU, 2021)
- CS 4780 Intrdocution To Machine Learning (Cornell, 2019)
- CS 4820 Introduction to Analysis of Algorithms (Cornell, 2017-2018)
- CS 2800 Discrete Structures (Cornell, 2017)