Toronto, Canada
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.
Deep-learning segmentation pipeline for detecting thin gap-junction structures in electron-microscopy volumes.
Outcome: Example result to update — improved recall on thin structures vs. a baseline U-Net.
Fine-tuned and compared transformer models for classifying sentiment in financial text.
Outcome: Example result to update — best model improved macro-F1 over a baseline.
Team-built attendance system using face recognition, with stranger rejection, reporting, and a dashboard.
Outcome: Example result to update — automated attendance capture with controlled false-positive rate.
SQL and BI workflow analyzing sports data, with data modeling, KPIs, and interactive dashboards.
Outcome: Example result to update — turned raw data into a reusable model and a dashboard answering key questions at a glance.
Toolkit
What I'm looking for
I'm transitioning into data science and applied ML, and I'm targeting roles such as:
Evidence
Writing
Interested in discussing data science, ML engineering, or applied AI roles? I'd be glad to connect.
