A curated set of peer-reviewed work spanning deep learning, evolutionary machine learning, and applied AI across cybersecurity and computational biology. Full list and live citation counts on Google Scholar.
2025
Genetic programming for enhanced detection of Advanced Persistent Threats through feature construction
Computers & Security, vol. 149, 104185 · Elsevier · Q1, IF ~5.6
Uses genetic programming to automatically construct features that catch advanced persistent threats — outperforming hand-engineered baselines on benchmark intrusion datasets.
2024
Detection of Advanced Persistent Threat: A genetic programming approach
Applied Soft Computing, vol. 167, 112447 · Elsevier · Q1, IF ~8.7
Evolutionary ML for cybersecurity — proposes a GP framework for APT detection that generalizes across attack families with interpretable detection rules.
2021
Multi-run concrete autoencoder to identify prognostic lncRNAs for 12 cancers
International Journal of Molecular Sciences, 22(21), 11919 · MDPI · Q1
Deep-learning approach (concrete autoencoder) for biomarker discovery across 12 cancer types — finds prognostic long non-coding RNAs that survive across independent runs, giving clinicians a stable feature set.
2020
Deep learning to discover genomic signatures for racial disparity in lung cancer
IEEE International Conference on Bioinformatics and Biomedicine (BIBM 2020)
Deep learning surfaces genomic signals that differ between racial groups in lung cancer — a societal-impact application of ML to a healthcare-equity problem.
2019
Long non-coding RNA based cancer classification using deep neural networks
Proceedings of the 10th ACM International Conference on Bioinformatics, Computational Biology and Health Informatics (ACM-BCB 2019)
End-to-end deep neural network for multi-cancer classification using lncRNA expression — outperforms classical ML baselines on the TCGA pan-cancer dataset.
For the complete list, conference proceedings, and live citation counts, see Google Scholar.