Founder of DeepNeuro.dev, an AI company specializing in production-ready machine learning models for healthcare applications, computer vision systems, and NLP solutions. I develop end-to-end AI solutions that bridge the gap between cutting-edge research and real-world applications, with expertise in deep learning, computer vision, natural language processing, and generative AI.
My work spans from building emotion classification systems using transformer models to developing brain tumor detection algorithms with convolutional neural networks. I'm passionate about making AI accessible and practical, sharing insights through technical writing on Medium where I explore topics ranging from fine-tuning large language models to deploying containerized ML applications on cloud platforms. With a background in neurobiology and extensive experience in healthcare AI, I bring a unique perspective to solving complex problems at the intersection of technology and life sciences.
Showcasing impactful AI/ML solutions in healthcare and beyond
Deep learning model for automated cancer detection in MRI scans with 89% accuracy. Implemented data augmentation and feature visualization for medical interpretability.
End-to-end ML pipeline for diabetes risk prediction. Implemented SMOTE for class balancing and achieved 85% precision with XGBoost.
Protein sequence-based classifier with 91% accuracy and 0.96 ROC-AUC. Utilized Random Forest and XGBoost for robust predictions.
TensorFlow, PyTorch, Keras, Neural Networks, CNNs, RNNs
Medical Imaging, DICOM, Bioinformatics, Clinical Data Analysis
Image Classification, Object Detection, Feature Engineering
Transformers, LangChain, RAG, Hugging Face, OpenAI API
Google Cloud, Vertex AI, Model Deployment, CI/CD
pandas, NumPy, scikit-learn, Statistical Analysis, Visualization
I'm currently open to opportunities in AI/ML engineering, healthcare AI, and research positions.