Tamer ABUHMED

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Dr. Tamer ABUHMED is an Associate Professor in the College of Computing and Informatics at Sungkyunkwan University (SKKU), where he has served since September 2019. He is the founder and director of the SKKU Information Research Laboratory (InfoLab). Before joining SKKU, he was an Assistant Professor in the Department of Computer Engineering at Inha University. He also held visiting scholar positions at Loyola University Chicago, the University of Notre Dame, and the University of Chicago, contributing to research on secure interpretable AI, software security, and medical diagnostic applications.

Research Interests

Cybersecurity and Systems Security: AI/ML security and robustness, adversarial machine learning, malware analysis and detection, software security analysis, network security, digital forensics, threat intelligence, cyber threat hunting, continuous authentication, secure mobile and IoT systems, binary analysis, and vulnerability detection.
Privacy and Trustworthy AI: federated learning security, privacy-preserving machine learning, secure multi-party computation, differential privacy, trustworthy AI systems, interpretable security models, and interpretable deep learning.
AI for Social Good and Healthcare: expert systems, clinical decision support systems, deep learning for medical diagnosis, medical time-series analysis, multimodal data fusion, risk assessment, and probabilistic modeling.

Education

INHA University, Republic of Korea
M.S. and Ph.D. in Computer Engineering, August 2012
Dissertation: Trustworthy Wireless Sensor Networks Through Software-Based Attestation

Selected Publications

Security, Trustworthy AI, and Privacy

A Deep Dive into Function Inlining and its Security Implications for ML-based Binary Analysis. Network and Distributed System Security Symposium (NDSS), 2026.
Large-scale and Language-Oblivious Code Authorship Identification. ACM SIGSAC Conference on Computer and Communications Security (CCS), 2018.
Multi-χ: Identifying Multiple Authors from Source Code Files. Proceedings on Privacy Enhancing Technologies (PETS), 2020.
Infodeslib: Python Library for Dynamic Ensemble Learning using Late Fusion of Multimodal Data. ACM KDD Workshop on Knowledge-infused Learning (KiL’24), 2024.
AdvChar: Attacking Interpretable NLP Systems. IEEE Transactions on Information Forensics and Security, 2025.
SingleADV: Single-Class Target-Specific Attack against Interpretable Deep Learning Systems. IEEE Transactions on Information Forensics and Security, 2024.
Hardening Interpretable Deep Learning Systems: Investigating Adversarial Threats and Defenses. IEEE Transactions on Dependable and Secure Computing, 2023.
Stealthy Query-Efficient Opaque Attack Against Interpretable Deep Learning. IEEE Transactions on Reliability, 2025.
Explainable Artificial Intelligence (XAI): What we know and what is left to attain Trustworthy Artificial Intelligence. Information Fusion, 2023.
Large-scale and robust code authorship identification with deep feature learning. ACM Transactions on Privacy and Security (TOPS), 2021.

AI for Healthcare and Biomedical Applications

MM-DES: Enhancing Multimodal Clinical Prediction with Joint Contrastive Embeddings and Dynamic Ensembles. International Conference on Pattern Recognition (ICPR), 2026.
Trustworthy Alzheimer’s diagnosis: Integrating robustness, fairness, and explainability in neuroimaging-based deep ensemble framework. Engineering Applications of Artificial Intelligence, 2026.
Multi-plane multi-slice longitudinal MRI for deep ensemble progression detection based on enhanced residual multi-head self-attention. Knowledge-Based Systems, 2026.
4DfCF: 4D fMRI CrossFormer Vision Transformer. IEEE Journal of Biomedical and Health Informatics, 2025.
Information fusion-based Bayesian optimized heterogeneous deep ensemble model based on longitudinal neuroimaging data. Applied Soft Computing, 2024.
Alzheimer’s disease diagnosis in the metaverse. Computer Methods and Programs in Biomedicine, 2024.
Prediction of Alzheimer’s progression based on multimodal deep-learning-based fusion and visual explainability of time-series data. Information Fusion, 2023.
Explainable machine learning models based on multimodal time-series data for the early detection of Parkinson’s disease. Computer Methods and Programs in Biomedicine, 2023.
Multitask Deep Learning for Cost-Effective Prediction of Patient’s Length of Stay and Readmission State Using Multimodal Physical Activity Sensory Data. IEEE Journal of Biomedical and Health Informatics, 2022.
Automatic detection of Alzheimer’s disease progression: An efficient information fusion approach with heterogeneous ensemble classifiers. Neurocomputing, 2022.

Academic Services

Ongoing Department and College Service

Member, Department of Computer Science and Engineering
Member, Department of Media and Communication
Member, Convergence Security Department
Member, Department of DMC Engineering
Member, Forensic Science Department / Digital Forensics
Member, Department of Artificial Intelligence Systems
Host and Organizer, Computer Science Seminar
Member, Super Sapiens Research Institute

Reviewer and Guest Editor Activities

IEEE Transactions on Neural Networks and Learning Systems
IEEE Transactions on Dependable and Secure Computing
IEEE Transactions on Image Processing
IEEE Transactions on Cognitive and Developmental Systems
IEEE Internet of Things Journal
IEEE Transactions on Mobile Computing
ACM Transactions on Privacy and Security
Expert Systems with Applications
Knowledge-Based Systems
Information Fusion
Human-centric Computing and Information Sciences

Conference Program Committee / Reviewer

ICML 2026
NeurIPS 2026
IJCAI 2025
ASONAM 2025–2026
ECAI 2022–2023
CSoNet 2020–2021
CCS 2019–2020
PETS 2020–2021

Professional Memberships

IEEE Senior Member and IEEE Computer Society
Association for Computing Machinery (ACM)
Korea Information and Communications Society (KICS)
Korean Institute of Information Security & Cryptology (KIISC)

Teaching

Dr. ABUHMED has more than 10 years of teaching experience. His recent teaching includes graduate courses such as Advanced Topics in Machine Learning, Machine Learning Security and Robustness, and Trustworthy Machine Learning, as well as undergraduate courses such as Fundamentals of Machine Learning, Introduction to Deep Neural Networks, Capstone Design, and Theory of Programming Languages.

Publications

2026

Trustworthy Alzheimer’s diagnosis: Integrating robustness, fairness, and explainability in neuroimaging based deep ensemble framework

Maria Bashir, Nasir Rahim, Shaker El-Sappagh, Omar Amin El-Serafy, Tamer Abuhmed

Engineering Applications of Artificial Intelligence · 01 May 2026 · doi:10.1016/j.engappai.2026.114291

GraphShield: Advanced dynamic graph-based malware detection using graph neural networks

Eslam Amer, Shaker El-Sappagh, Tamer Abuhamad, Bander Ali Saleh Al-Rimy, Alaa Mohasseb

Expert Systems with Applications · 01 Mar 2026 · doi:10.1016/j.eswa.2025.129812

2025

Mortality prediction for ICU patients with mental disorders using large language models ensemble and unstructured medical notes

Waleed Nazih, Tamer Abuhmed, Meshal Alharbi, Shaker El-Sappagh

PLOS One · 19 Sep 2025 · doi:10.1371/journal.pone.0332134 · PMID: 40971951 · PMCID: PMC12448993

Stealthy Query-Efficient OpaqueAttack Against Interpretable Deep Learning

Eldor Abdukhamidov, Mohammed Abuhamad, Simon S. Woo, Eric Chan-Tin, Tamer Abuhmed

IEEE Transactions on Reliability · 01 Sep 2025 · doi:10.1109/TR.2025.3551717

Research Interests

Education

Selected Publications

Security, Trustworthy AI, and Privacy

AI for Healthcare and Biomedical Applications

Academic Services

Ongoing Department and College Service

Reviewer and Guest Editor Activities

Conference Program Committee / Reviewer

Professional Memberships

Teaching

Publications

2026

2025

2024

2023

2022

2021

2020

2019

2018

2017

2015

2009