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Hongchang Gao

Deep Learning Machine Learning Neural Network Optimization Representation Learning Graph Neural Networks

About

Hongchang Gao joined the CIS department at Temple University as a tenure-track Assistant Professor in 2020. Prior to this, he received his Ph.D. degree in Electrical and Computer Engineering from University of Pittsburgh in 2020, under the supervision of Dr. Heng Huang. He also earned an M.S. degree in Computer Science from Beihang University in 2014 and a B.S. degree in Mathematics and Applied Mathematics from Ocean University of China in 2011. He is a recipient of the NSF CAREER award (2024), the AAAI New Faculty Highlights (2023), and the Cisco Faculty Research Award (2023). He regularly serves as an Area Chair for ICML, NeurIPS, and ICLR

Research Performance Summary

62
Total Papers
2530
Total Citations
21
H-Index
2015-2026
Active Research Span

First Recorded Paper

Multi-view subspace clustering

Year: 2015

Citations: 747

Venue: Proceedings of the IEEE international conference on computer vision

Latest Recorded Paper

Swapping and Purification Scheme Optimization for Entanglement Distribution in Quantum Networks

Year: 2026

Citations: 0

Venue: IEEE Transactions on Networking

Last 10 Years Publication Activity

This timeline shows the professor's yearly publication activity.

2026
1
2025
6
2024
9
2023
12
2022
8
2021
7
2020
3
2019
3
2018
6
2017
1
2016
2

Publication Venues and Collaboration

Journal, Conference, and Book Publication Breakdown

Conference 38
Journal 3
Book 1

Top Coauthors

Gao Huang Zhang

Research Impact by Period

2025-2026

Period Stats
Papers 7
Citations 18
Avg. Citations / Paper 2.6
H-Index 2

2020-2024

Period Stats
Papers 39
Citations 844
Avg. Citations / Paper 21.6
H-Index 15

2015-2019

Period Stats
Papers 16
Citations 1668
Avg. Citations / Paper 104.2
H-Index 12

Contact and Professional Links

Contact Information

hongchang.gao@temple.edu

Detected Research Keywords

Stochastic Gradient Descent Stochastic Compositional Gradient Compositional Gradient Descent Gradient Descent Ascent Generative Adversarial Networks Gradient Descent Momentum Decentralized Stochastic Gradient Federated Bilevel Optimization Group Hierarchical Federated Hierarchical Federated Learning