Ibrahim Malik
PhD Candidate in Computer Science | AI Security Researcher
Advancing AI security through research in red/blue-teaming methodologies for generative AI systems. Currently pursuing PhD at Trinity College Dublin in collaboration with IBM Research, specializing in adversarial attacks on large language models, defense mechanisms, and safety evaluation frameworks.
Research Expertise
Specializing in AI security and safety, with focus on adversarial machine learning, generative AI robustness, and privacy-preserving techniques in healthcare applications. Published researcher with expertise in explainable AI, synthetic data generation, and enterprise AI deployment security.
AI Security & Safety
Machine Learning Research
Data Science & Analytics
Technical Infrastructure
Research Projects
Hybrid AI for Leukemia Detection
Master's Thesis Research | Medical AI
Novel architecture combining Generative Adversarial Networks with Vision Transformers for automated leukemia detection, incorporating explainable AI frameworks for clinical interpretability.
- Developed hybrid deep learning system combining GANs with Vision Transformers for medical image classification
- Implemented explainability frameworks (Grad-CAM, SHAP, LIME) for model interpretation and validation
- Discovered architecture-specific optimization: CNNs with conditional GANs vs. Vision Transformers with Wasserstein GANs
- Conducted systematic literature review of 30+ papers on GANs and Vision Transformers in medical AI
- Validated models on 13,000+ images with rigorous metrics for clinical deployment applications
Socioeconomic Analysis of Disability Prevalence
Large-Scale Data Analysis | Public Health Research
Comprehensive analysis of socioeconomic determinants affecting disability prevalence using big data analytics and machine learning on large-scale public health datasets.
- Analyzed 700,000+ public health records using distributed computing
- Identified critical correlations: healthcare access (r = -0.26), income (r = 0.12)
- Gradient Boosting model achieved R² = 0.72 predictive performance
- Feature importance analysis revealing income (58.5%) and healthcare (28.5%) as primary drivers
- Cloud-native data pipeline with Azure, PySpark, and multi-database architecture
Mortality Prediction & Biomedical Text Mining
Epidemiological Modeling | Natural Language Processing
Multi-faceted research combining mortality prediction modeling with large-scale biomedical literature analysis using advanced machine learning and NLP techniques.
- Classified excess mortality risk regions with 92% accuracy
- Five regression models achieving 85%+ R² scores for mortality prediction
- Analyzed 6,000+ biomedical abstracts using K-Means and LDA
- Engineered 10+ features for cross-dataset model optimization
- Reproducible research pipeline with comprehensive evaluation metrics
IMU-Based Gait Analysis System
Biomedical Engineering | Wearable Technology Research
Development of non-invasive gait analysis system using inertial measurement units and machine learning for early pathology detection and movement assessment.
- Integrated ESP32 and IMU sensors for multi-segment gait data collection
- CNN model achieving correlation coefficient >0.9
- 5-fold cross-validation ensuring robust model generalization
- Data preprocessing pipeline for 20+ Excel files with automated cleaning
- Non-invasive approach for early pathology diagnosis applications
Debenhams Ireland Revival Analytics
Business Intelligence | Operations Research
Comprehensive business analytics project including data warehousing, CRM implementation, and Six-Sigma process improvement for retail revival strategy.
- Built MySQL data warehouse with 150,000+ historical records
- Salesforce CRM implementation with 8,000+ customer profiles migrated
- Three interactive Tableau dashboards for real-time business intelligence
- Six-Sigma DMAIC methodology for inventory management optimization
- Generated 20,000+ synthetic records for GDPR-compliant testing
Diabetes Readmission Risk Analysis
Healthcare Analytics | Predictive Modeling
End-to-end healthcare analytics pipeline for predicting diabetes patient readmission risk using machine learning and interactive visualization systems.
- Processed 100,000+ patient records with optimized SQL queries
- Random Forest classifier for readmission prediction across merged datasets
- Interactive Plotly/Dash dashboards visualizing 10+ years of diabetes outcomes
- Complete ETL pipeline with API integration and multi-database storage
- Containerized deployment using Docker for cross-platform compatibility
Professional Experience
Research Experience
Pre-Doctoral Researcher
Sep 2025 - Present
IBM Research & Trinity College Dublin
Conducting AI security research in collaborative partnership between IBM Research and Trinity College Dublin.
- Specializing in adversarial attacks on large language models and defense mechanisms development
- Contributing to benchmarking datasets for AI safety evaluation and model robustness assessment
- Collaborating on agent-based systems and model context protocols
- Research focus on enterprise AI deployment security
- Advancing methodologies for red/blue-teaming in generative AI
Artificial Intelligence Research Assistant
Jan 2025 - Aug 2025
School of Computing, National College of Ireland
Supervisor: Dr. Bharat Agarwal
- Developed hybrid deep learning system combining GANs with Vision Transformers for medical image classification
- Implemented explainability frameworks (Grad-CAM, SHAP, LIME) for model interpretation and validation
- Discovered architecture-specific optimization: CNNs with conditional GANs vs. Vision Transformers with Wasserstein GANs
- Conducted systematic literature review of 30+ papers on GANs and Vision Transformers in medical AI
- Validated models on 13,000+ images with rigorous metrics for clinical deployment applications
Machine Learning Research Assistant
Feb 2023 - Sep 2023
Department of Electrical and Computer Engineering, Curtin University Malaysia
Supervisor: Dr. Saaveethya Sivakumar
- Conducted machine learning research in biomechanical gait analysis for medical diagnostics
- Performed literature review of 30+ papers on LSTM architectures for time-series analysis
- Engineered LSTM model achieving 0.86 correlation coefficient for predictive healthcare applications
Industry Experience
IT Intern - Data Reporting
May 2025 - July 2025
ELI SFI Coding Club, National College of Ireland
Developed comprehensive data reporting solutions to monitor and analyze student performance metrics.
- Built Power BI dashboards improving reporting speed by 30%
- Managed data cleaning and ETL processes using Excel and internal templates
- Collaborated on documentation and version control using Notion and GitHub
- Streamlined data visualization workflows for educational analytics
Data Science and Business Analytics Intern
Jul 2024 - Aug 2024
The Sparks Foundation (Remote)
Applied machine learning and data visualization techniques to solve real-world business and educational challenges.
- Leveraged supervised ML to predict student scores with 95% accuracy
- Implemented Tableau dashboard tracking COVID-19 spread across Europe
- Developed interactive Python visualizations for educational and business data
- Presented analytical findings to stakeholders and management
R&D Engineer Intern
Nov 2021 - Mar 2023
NCIG (M) Sdn Bhd., Selangor, Malaysia
Contributed to product development and quality assurance processes through data-driven analysis and systematic testing.
- Tracked and managed 200+ product test cases using internal BI tools
- Reduced information retrieval time by 30% through systematic data entry
- Coordinated with cross-functional teams for structured defect analysis
- Built internal data reports supporting engineering KPIs and quality metrics
Academic Leadership & Engagement
ACDSA Conference 2025
Session Chair: Deep Learning - II
Led academic session on deep learning applications, facilitating scholarly discourse and peer review processes at the International Conference on Artificial Intelligence, Computer, Data Sciences and Applications.
Citi upStart 2025
Team-Based Entrepreneurship Program
Developed AI-driven startup proposal addressing Sustainable Development Goals in healthcare innovation. Collaborated with postgraduate team members on business strategy and technical implementation.
IDEATHON 2025
3rd Place Winner - Celtify Project
Created AI-powered Celtic language recognition and translation tool with VR simulation for immersive language learning and cultural preservation initiatives.
IEEE Society Member
Curtin Malaysia Chapter (2023)
Conducted poster presentations and educational talks to high school students on mathematics and engineering concepts, promoting STEM education and academic engagement.
Publications & Research Output
Authors: Malik, I., Diang'a, L.J., Stynes, P., Pathak, P., & Sahni, A.
DOI: 10.1109/ACDSA65407.2025.11166046 | Role: Session Chair: Deep Learning - II
Abstract: Novel hybrid architecture combining GANs and Vision Transformers for medical image classification, achieving significant improvements in leukemia detection sensitivity through architecture-specific optimization strategies.
Authors: Groenewald, E., Khan, M., Raza, S., Malik, I., Tehseen, R et al.
DOI: 10.33282/rr.vx9i2.37 | Pages: 631-651
Abstract: Comprehensive analysis of the relationship between information technology capabilities, organizational learning capacity, and dynamic capabilities in driving competitive advantage and performance outcomes.
Authors: J. Lee, I. Malik, A. C. L. Thien and S. Sivakumar
DOI: 10.1109/ICDATE58146.2023.10248823 | Location: Miri, Sarawak, Malaysia
Abstract: Development of neural network-based approach for lower limb gait estimation using foot motion data, contributing to non-invasive movement analysis and pathology detection methodologies.
Research Interests
AI Security & Privacy
Security and privacy implications of Large Language Models in enterprise environments
Adversarial AI
Adversarial attacks and defenses for generative AI systems
Healthcare AI
Privacy-preserving machine learning techniques in healthcare applications
Explainable AI
Explainable AI and Fairness for enhanced security in critical systems
Contact & Collaboration
Location
Dublin, Ireland
Available for local collaborations
"Interested in collaborative research opportunities in AI security, red/blue-teaming methodologies, and privacy-preserving machine learning. Open to industry partnerships, academic collaborations, and innovative projects that advance the responsible development of secure AI systems."