Data Scientist & Applied Machine Learning Developer
Applied AI and data science practitioner with a background in neuroscience and molecular genetics. I build machine learning, computer vision, NLP, and analytics projects that connect technical modeling with clear, real-world interpretation.
I build ML and data systems that turn messy, real-world data into interpretable decisions. My work spans biomedical image segmentation, NLP, analytics, and applied computer vision — and each case study documents the problem, approach, result, and what I would improve next.
Selected work
A few projects that best show how I approach modeling, evaluation, and shipping.
Built a CNN-based pipeline that segments gap junctions in 3D electron-microscopy volumes and converts them into electrical-connectivity measurements.
Outcome: Created a reusable path from raw EM slices to 3D gap-junction predictions, per-neuron measurements, contactomes, and normalized electrical-connectivity matrices.
Built a containerized vision service that streams camera frames from a mobile robot and returns clothing detections or segmentation geometry.
Outcome: Separated capture from inference so lightweight clients can send one JPEG frame at a time and receive compact geometry without hosting model weights or accumulating stale frames.
Built a diffusion-MRI connectomics pipeline comparing DMN/ECN white-matter structure in 40 pediatric brain-tumor survivors and 36 healthy controls.
Outcome: Turned diffusion MRI and cortical parcellations into de-identified, analysis-ready connectivity matrices and documented significant working-memory and processing-speed differences between survivors and controls.
Combined RNN, LSTM, and Conv1D market models with a fine-tuned BERT sentiment pipeline to study daily NVDA price direction.
Outcome: Built reproducible notebooks for price-direction modeling, financial-news classification, daily sentiment aggregation, and side-by-side market interpretation.
Toolkit
What I'm looking for
I'm focused on data science and applied ML roles where I can combine research rigor with production engineering:
Evidence
Writing
Interested in discussing data science, ML engineering, or applied AI roles? I'd be glad to connect.
