Stackelberg Coupling of Online Representation Learning and Reinforcement Learning
Published in Preprint, 2025
Fernando Martinez, Tao Li, Yingdong Lu, Juntao Chen, “Stackelberg Coupling of Online Representation Learning and Reinforcement Learning”
Publications
In-Context Reinforcement Learning via Communicative World Models
Published in Preprint, 2025
Fernando Martinez, Tao Li, Yingdong Lu, Juntao Chen, “In-Context Reinforcement Learning via Communicative World Models”
Multi-Stage Enhanced Zero Trust Intrusion Detection System for Unknown Attack Detection in Internet of Things and Traditional Networks
Published in ACM Transactions on Privacy and Security, 2025
Malek Al-Zewairi, Sufyan Almajali, Moussa Ayyash, Mohamed Rahouti, Fernando Martinez, Nordine Quadar, “Multi-Stage Enhanced Zero Trust Intrusion Detection System for Unknown Attack Detection in Internet of Things and Traditional Networks”
Decoding Fatphobia: Examining Anti-Fat and Pro-Thin Bias in AI-Generated Images
Published in NAACL 2025, 2025
J. Warren, G. M. Weiss, F. Martinez, A. Guo, Y. Zhao, “Decoding Fatphobia: Examining Anti-Fat and Pro-Thin Bias in AI-Generated Images”
Benchmarking Interpretability in Healthcare Using Pattern Discovery and Disentanglement
Published in Bioengineering - MDPI, 2025
Peiyuan Zhou, Amane Takeuchi, Fernando Martinez-Lopez, Malikeh Ehghaghi, Andrew K. C. Wong, En-Shiun Annie Lee, “Benchmarking Interpretability in Healthcare using Pattern Discovery and Disentanglement”
SPRIG: Stackelberg Perception-Reinforcement Learning with Internal Game Dynamics
Published in AAAI 2025 Workshop on Planning and Reinforcement Learning (PRL), 2025
F. Martinez-Lopez, J. Chen, Y. Lu, “SPRIG: Stackelberg Perception-Reinforcement Learning with Internal Game Dynamics”
Learning in Multiple Spaces: Few-Shot Network Attack Detection with Metric-Fused Prototypical Networks
Published in The AAAI-25 Workshop on Artificial Intelligence for Cyber Security (AICS), 2025
F. Martinez-Lopez, L. Santana, M. Rahouti, “Learning in Multiple Spaces: Few-Shot Network Attack Detection with Metric-Fused Prototypical Networks”
GPT vs. Llama2: Which Comes Closer to Human Writing in Text Generation?
Published in EDM 2024, 2024
F. Martinez, G.M. Weiss, M. Palma, H. Xue, A. Borelli, Y. Zhao, “GPT vs. Llama2: Which Comes Closer to Human Writing in Text Generation?”
Admissions in the Age of AI: Detecting AI-Generated Application Materials in Higher Education
Published in 2nd Annual Symposium on Data Science and AI: Empowering Society for the Greater Good, Fordham University, 2024
Presented Y. Zhao, A. Borelli, F. Martinez, H. Xue, G.M. Weiss “Admissions in the Age of AI: Detecting AI-Generated Application Materials in Higher Education”
Redefining Cyber Resilience with Dual-Space Prototypical Networks for DDoS Attack Detection
Published in ICCCN, 2024
Our work introduces the Dual-space Prototypical network, a neural network that leverages a unique dual-space loss function to enhance the detection accuracy of distributed denial-of-service (DDoS) attacks, both known and novel patterns, through geometric and angular similarity measures in the latent space, demonstrating outstanding performance, especially on reduced training sets, which is a challenge for standard deep learning architectures.
Integrating Multiple Visual Attention Mechanisms in Deep Neural Networks
Published in COMPSAC, 2023
We introduce PVAN, a novel and efficient convolutional neural network approach that employs parallelized hybrid visual attention. This method not only outperforms existing state-of-the-art techniques but also maintains efficiency.
Recommended citation: F. Martinez and Y. Zhao, "Integrating Multiple Visual Attention Mechanisms in Deep Neural Networks," 2023 IEEE 47th Annual Computers, Software, and Applications Conference (COMPSAC), Torino, Italy, 2023, pp. 1191-1196, doi: 10.1109/COMPSAC57700.2023.00180.
Rediscovering Fraud Detection in Bitcoin Transactions Using Machine Learning Models
Published in WF-IoT, 2023
Performance study of ML techniques such as conventional ML models, regular MLP architectures, and graph convolutional networks to accurately detect fraudulent transactions in the Bitcoin Blockchain.
Recommended citation: F. Martinez, M. Rahouti, A. Chehri, R. Amin and N. Ghani, "Rediscovering Fraud Detection in Bitcoin Transactions Using Machine Learning Models," 2023 IEEE 9th World Forum on Internet of Things (WF-IoT), Aveiro, Portugal, 2023, pp. 1-6, doi: 10.1109/WF-IoT58464.2023.10539490.
Rediscovering The Particle in a Box: Machine learning regression analysis for hypothesis generation in physical chemistry lab
Published in Journal of Chemical Education, 2023
We an interactive ML activity that utilizes regression analysis to predict the wavelength of maximum absorption for cyanine dyes. The activity employs a dataset of 13 molecular features to train regression models of increasing complexity. This approach effectively guides students towards the particle-in-a-box (PIB) model, fostering a deeper understanding of the underlying molecular properties that govern cyanine dye absorption.
Recommended citation: Thrall E, Martinez Lopez F, Egg T, Lee SE, Schrier J, Zhao Y. Rediscovering the particle in a box: Machine learning regression analysis for hypothesis generation in physical chemistry lab. Journal of Chemical Education; 2023, doi: 10.1021/acs.jchemed.3c00765
A Machine Learning Approach to Examine Personality, Adjustment, and Engineering Identity Among College Engineering Students
Published in [PENDING], 2023
Submitted Y. Ding, Q. Wang , F. Martinez Lopez, Y. Wu, Y. Zhao, “A Machine Learning Approach to Examine Personality, Adjustment, and Engineering Identity Among College Engineering Students”