Medical AI Researcher | Deep Learning for Precision Medicine | Open Science Advocate
From my early days in Computer Engineering, I’ve been fascinated by the power of algorithms to illuminate hidden patterns within complex biomedical data. My journey has spanned from independent exploratory projects—like breast cancer detection from mammography, lung cancer prediction from CT scans, retinal disease diagnosis, and cardiovascular risk prediction—to formal research contributions, including the release of globally balanced leukemia gene-expression datasets and the authorship of multiple peer-reviewed preprints and technical books.
I specialize in developing innovative deep learning solutions for medical imaging, with a focus on making AI interpretable and clinically relevant. My current doctoral research centers on brain tumor detection and segmentation using advanced architectures such as U-Net and Vision Transformers. I’m deeply committed to collaborative, open, and reproducible science that can directly support clinical decision-making and improve patient outcomes.
- Deep learning for medical imaging (MRI, fMRI, PET, EEG)
- Genomics and precision medicine
- Explainable and interpretable AI models for healthcare
- Multi-modal data integration for diagnosis and prognosis
- Decision-support systems for clinicians
- Genomic-Classification-of-Acute-Lymphoblastic-Leukemia-Using-AI-Towards-Personalized-Medicine
- Stacked-Deep-Learning-for-Subtype-Classification-in-Acute-Myeloid-Leukemia
- Cardiac-Diagnosis
- Feature-Based-Machine-Learning-for-Brain-Metastasis-Detection-Using-Clinical-MRI
- A-Hybrid-Deep-Learning-Ensemble-for-Accurate-Skin-Cancer-Classification
Programming: Python (Advanced), Java (Intermediate)
Machine/Deep Learning: TensorFlow, Keras, Scikit-learn
Image Processing: OpenCV, Scikit-image
Data Science: Pandas, NumPy, Matplotlib, Seaborn
Medical Imaging: DICOM, NIfTI
Tools & Platforms: Git, Linux
- 5+ peer-reviewed preprints
- 4 technical books (including the world’s first volumes on Vision Transformers and 3D Vision Transformers in medical imaging)
- Publications available on Google Scholar
- Passionate about the intersection of medicine, neuroscience, and AI—especially brain research
- Enjoy writing technical books/tutorials to mentor students and young researchers
- Interested in how human cognition and psychology can inspire next-gen AI
- Love hiking in nature and playing chess for relaxation and inspiration
- Strong believer in open science and reproducibility
- Committed to structured project planning with milestones—balanced by creative innovation
- Dedicated to building AI tools that clinicians can trust and use in real-world healthcare
“Let’s build transparent, impactful AI for medicine—together.”