Research
Research interests & current projects
AI-Driven Biomarker Discovery
Precision medicine through machine learning models for biomarker identification.
Swarm Intelligence & Metaheuristics
Optimization for feature selection and model tuning (GWO, PSO, and hybrids).
Causal Inference & Subgroup Identification
Treatment effect estimation for precision oncology.
Computer Vision
Deep learning for industrial and medical imaging applications.
LLMs & RAG
Retrieval-augmented generation for intelligent decision support systems.
Survival Analysis
Prognostic modeling in oncology using deep survival networks.
Current research projects
AI-Powered CT Verification System
Computer VisionDual-camera computer vision system for automated Current Transformer connection verification in manufacturing QA, using OCR, Hough-circle detection, and SVM/KNN classifiers — achieving 100% leave-one-out accuracy.
Enterprise RAG & LLM Decision Support
LLMs / GenAIRetrieval-augmented generation architectures for natural language interfaces to structured databases, document retrieval, and conversational AI for industrial maintenance diagnostics.
Causal AI for Cancer Immunotherapy
Computational MedicineCausal AI-based clinical and radiomic analysis for optimizing patient selection in combined immunotherapy and SABR in early-stage NSCLC (Journal for Immunotherapy of Cancer, 2025).
Swarm Intelligence for Deep Survival Networks
Swarm IntelligenceSwarm-based hyperparameter optimization and feature selection for deep survival networks applied to prognostic radiomics across multiple solid cancers.
Industrial IoT Analytics
Applied MLML pipelines for industrial equipment monitoring using Gaussian Mixture Model-based automatic threshold computation for regime detection and utilization analytics.
Open-source software
SwarmDeepSurv
Python package for swarm intelligence-enhanced deep survival networks for prognostic radiomics signatures in cancer research.
Hybrid-Binary-GWO-FS
MATLAB package for binary optimization using hybrid Grey Wolf Optimization for feature selection.
BMOGWO-S
MATLAB package for binary multi-objective Grey Wolf Optimizer for feature selection in classification.