Research & Engineering

Projects

A selection of my research and applied engineering work at the intersection of AI, clinical decision-making, and neuroscience.

Neuroengineering: Visual Stimulus Decoding

Deep learning pipeline to decode visual stimulus categories from human brain fMRI data. Leverages convolutional and recurrent architectures to map BOLD signals to semantic categories, advancing brain-computer interface research.

PyTorch fMRI Deep Learning Neuroscience

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Multi-class WBC Classification

Deep convolutional neural network for automated classification of high-resolution white blood cell images into multiple morphological subtypes. Designed for clinical lab automation with high accuracy on imbalanced medical imaging datasets.

CNN Medical Imaging PyTorch Clinical AI

View on GitHub

Agentic AI Evaluation Framework

Multi-tier evaluation framework to validate autonomous AI agents for NSCLC (Non-Small Cell Lung Cancer) treatment planning. Combines LLM-as-judge, deterministic rule checks, and clinician feedback loops to ensure safety, accuracy, and alignment with clinical guidelines.

Agentic AI LLM Clinical AI NSCLC Evaluation

View on GitHub