Postdoctoral researcher

Huanle Zhang

Postdoctoral Researcher

About Huanle

Huanle is a Postdoctoral Researcher at the University of California, Davis, working on applied machine learning, mobile systems and applications, the Internet of Things, and wireless networking.

1 projects

Project involvement

Year 1

AI Prediction of Fruit Bruising, Serum Leakage, and Spoilage During Transportation of Processing Tomatoes to Processing Facilities

#Core AI Technologies#Food Processing and Distribution

5 publications

AIFS publications

Journal Article · Journal of Food Protection · 2024
Detection of Escherichia Coli Using Bacteriophage T7 and Analysis of Excitation‑Emission Matrix Fluorescence Spectroscopy
Wisuthiphaet, Nicharee, Huanle Zhang, Xin Liu, and Nitin Nitin
Paper page for Detection of Escherichia coli Using Bacteriophage T7 and Analysis of Excitation‑Emission Matrix Fluorescence Spectroscopy PDF of Detection of Escherichia coli Using Bacteriophage T7 and Analysis of Excitation‑Emission Matrix Fluorescence Spectroscopy DOI 10.1016/j.jfp.2024.100396
Journal Article · IEEE Internet of Things Journal · 2023
Client Selection in Federated Learning: Principles, Challenges, and Opportunities
Fu, Lei, Huanle Zhang, Ge Gao, Mi Zhang, and Xin Liu
Paper page for Client Selection in Federated Learning: Principles, Challenges, and Opportunities PDF of Client Selection in Federated Learning: Principles, Challenges, and Opportunities DOI 10.1109/jiot.2023.3299573
Journal Article · IEEE Internet of Things Journal · 2023
Federated Learning Hyperparameter Tuning From a System Perspective
Zhang, Huanle, Lei Fu, Mi Zhang, Pengfei Hu, Xiuzhen Cheng, Prasant Mohapatra, and Xin Liu
Paper page for Federated Learning Hyperparameter Tuning From a System Perspective PDF of Federated Learning Hyperparameter Tuning From a System Perspective DOI 10.1109/jiot.2023.3253813
Conference Article · Military Communications Conference · 2022
FedTune: Automatic Tuning of Federated Learning Hyper-Parameters From System Perspective
Zhang, Huanle, Mi Zhang, Xin Liu, Prasant Mohapatra, and Michael DeLucia
Paper page for FedTune: Automatic Tuning of Federated Learning Hyper-Parameters from System Perspective DOI 10.1109/MILCOM55135.2022.10017717
Journal Article · Frontiers in Artificial Intelligence · 2022
Spectroscopy Approaches for Food Safety Applications: Improving Data Efficiency Using Active Learning and Semi-supervised Learning
Zhang, Huanle, Nicharee Wisuthiphaet, Hemiao Cui, Nitin Nitin, Xin Liu, and Qing Zhao
Paper page for Spectroscopy Approaches for Food Safety Applications: Improving Data Efficiency Using Active Learning and Semi-supervised Learning DOI 10.3389/frai.2022.863261

Back to the team

Huanle Zhang

Project involvement

AI Prediction of Fruit Bruising, Serum Leakage, and Spoilage During Transportation of Processing Tomatoes to Processing Facilities

AIFS publications

Detection of Escherichia Coli Using Bacteriophage T7 and Analysis of Excitation‑Emission Matrix Fluorescence Spectroscopy

Client Selection in Federated Learning: Principles, Challenges, and Opportunities

Federated Learning Hyperparameter Tuning From a System Perspective

FedTune: Automatic Tuning of Federated Learning Hyper-Parameters From System Perspective

Spectroscopy Approaches for Food Safety Applications: Improving Data Efficiency Using Active Learning and Semi-supervised Learning