Huancheng Chen
PhD student, University of Texas at Austin
Austin, Texas
huanchengch [AT] utexas.edu
Bio
I am a fourth-year Ph.D. student in the Department of Electrical and Computer Engineering at UT Austin, advised by Prof. Haris Vikalo. I am also a member of the WNCG lab (Wireless Networking and Communications Group). Before joining UT Austin, I obtained my B.Eng degree from the department of Electrical Engineering and Automation, South China University of Technology.
Research Interests
Research Statement in PDF.
My research focuses on Federated Learning and Trustworthy AI.
Specifically, I am interested in:
1) federated learning with data-heterogeneous and resource-heterogeneous clients;
2) model compression (pruning, quantization, distillation) in federated learning;
3) differential privacy and adversarial robustness in federated learning.
Recently, I am interested in deep generative models e.g. GAN, VAE and Diffusion models etc.
News
November, 2023 One paper about mixed-precision quantization preprinted in arXiv.
September, 2023 One paper about client selection preprinted in arXiv.
March, 2023 One paper accepted in CVPR2023 workshop.
Jan, 2023 One paper accepted in ICLR2023.
Publications
Most recent publications on Google Scholar.
‡ indicates equal contribution.
Mixed-Precision Quantization for Federated Learning on Resource-Constrained Heterogeneous Devices
Huancheng Chen, Haris Vikalo
axXiv 2023
Accelerating Non-IID Federated Learning via Heterogeneity-Guided Client Sampling
Huancheng Chen, Haris Vikalo
arXiv 2023
Federated Learning in Non-IID Settings Aided by Differentially Private Synthetic Data
Huancheng Chen, Haris Vikalo
CVPR'23: Conference on Computer Vision and Pattern Recognition FedVision Workshop (oral)
The Best of Both Worlds Accurate Global and Personalized Models through Federated Learning with Data-Free Hyper-Knowledge Distillation
Huancheng Chen, Johnny Wang, Haris Vikalo
ICLR'23: International Conference on Learning Representation (poster)
Skeleton-Graph: Long-Term 3D Motion Prediction From 2D Observations Using Deep Spatio-Temporal Graph CNNs
Abduallah Mohamed ‡, Huancheng Chen‡, Zhangyang Wang, Christian Claudel
ICCV'21: International Conference on Computer Vision Workshop
Mixed-Precision Quantization for Federated Learning on Resource-Constrained Heterogeneous Devices
Huancheng Chen, Haris Vikalo
axXiv 2023
Accelerating Non-IID Federated Learning via Heterogeneity-Guided Client Sampling
Huancheng Chen, Haris Vikalo
arXiv 2023
Federated Learning in Non-IID Settings Aided by Differentially Private Synthetic Data
Huancheng Chen, Haris Vikalo
CVPR'23: Conference on Computer Vision and Pattern Recognition FedVision Workshop (oral)
The Best of Both Worlds Accurate Global and Personalized Models through Federated Learning with Data-Free Hyper-Knowledge Distillation
Huancheng Chen, Johnny Wang, Haris Vikalo
ICLR'23: International Conference on Learning Representation (poster)
Skeleton-Graph: Long-Term 3D Motion Prediction From 2D Observations Using Deep Spatio-Temporal Graph CNNs
Abduallah Mohamed ‡, Huancheng Chen‡, Zhangyang Wang, Christian Claudel
ICCV'21: International Conference on Computer Vision Workshop
Vitæ
Full Resume in PDF.
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University of Texas at Austin 2020 – 2025Ph.D. Student
Electrical and Computer Engineering
Advisor: Haris Vikalo -
Toyota InfoTech Labs, Moutainview, CA Summer 2022Research Internship
AI/ML Infrastructure & Data Lab
Mentor: Johnny Wang -
Nokia Bell Lab, Murray Hill, NJ Spring 2022Research Internship
Mathematics & Algorithms Research Group
Mentor: Iraj Saniee -
South China University of Technology 2014 - 2019B.Sc. Student
Electrical Engineering
Rank 2/304
Teaching
TA for CS395T, 2020 Fall: Foundation of Predictive Machine Learning
TA for EE381K, 2021 Spring: Statistical Machine Learning
TA for EE422C, 2021 Summer: Software Design and Implementation II (Java)
TA for EE380L: 2021 Fall: Data Mining
TA for CS395T, 2022 Spring: Convex Optimization
TA for EE351M, 2022 Fall: Digital Signal Processing
Service
reviewer: ICML2022, NeurIPS2022, ICML2023, NeurIPS2023, ICLR2024
Skills
Programming Languages: Python, Java, C/C++, SQL, LaTeX
Softwares: Pytorch, Tensorflow, Linux, AWS, Google Cloud, Matlab, Git