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Minghong Fang

AI Safety AI Security Adversarial Machine Learning Robustness In AI Systems
University of Louisville J.B.SPEED SCHOOL OF ENGINEERING

About

I am a tenure-track Assistant Professor in the Department of Computer Science and Engineering at University of Louisville. From 2022 to 2024, I was a Postdoctoral Associate in the Department of Electrical and Computer Engineering at Duke University. I received my Ph.D. in Electrical and Computer Engineering from The Ohio State University in August 2022. My research broadly focuses on various aspects of AI safety and security.

Research Performance Summary

55
Total Papers
5050
Total Citations
18
H-Index
2014-2026
Active Research Span

First Recorded Paper

Prioritizing disease-causing genes based on network diffusion and rank concordance

Year: 2014

Citations: 10

Venue: 2014 IEEE International Conference on Bioinformatics and Biomedicine (BIBM

Latest Recorded Paper

When the Server Steps In: Calibrated Updates for Fair Federated Learning

Year: 2026

Citations: 0

Venue: arXiv preprint arXiv:2601.05352

Last 10 Years Publication Activity

This timeline shows the professor's yearly publication activity.

2026
2
2025
20
2024
14
2023
2
2022
5
2021
3
2020
5
2019
1
2018
1
2014
2

Publication Venues and Collaboration

Journal, Conference, and Book Publication Breakdown

Conference 37
Journal 3
Book 2

Top Coauthors

Fang Liu Zhang

Research Impact by Period

2025-2026

Period Stats
Papers 22
Citations 110
Avg. Citations / Paper 5
H-Index 6

2020-2024

Period Stats
Papers 29
Citations 4560
Avg. Citations / Paper 157.2
H-Index 16

2015-2019

Period Stats
Papers 2
Citations 368
Avg. Citations / Paper 184
H-Index 2

2010-2014

Period Stats
Papers 2
Citations 12
Avg. Citations / Paper 6
H-Index 2

Contact and Professional Links

Contact Information

m0fang04@louisville.edu

Detected Research Keywords

Model Poisoning Attacks Robust Federated Learning Poisoning Attacks Federated Attacks Federated Learning Retrieval Augmented Generation Byzantine Robust Federated Decentralized Federated Learning Poisoning Attacks Defenses Against Model Poisoning Data Poisoning Attacks