Published in The 14th International Conference on Learning Representations (ICLR), 2026
Fernando Martinez, Tao Li, Yingdong Lu, Juntao Chen, “Stackelberg Coupling of Online Representation Learning and Reinforcement Learning”
Published in Preprint, 2025
Fernando Martinez, Tao Li, Yingdong Lu, Juntao Chen, “In-Context Reinforcement Learning via Communicative World Models”
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”
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”
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”
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”
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”
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?”
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”
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.
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.
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.
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
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”