AI & Data Science
Applying machine learning, generative AI, and NLP to hard problems across domains — building tools that turn complex, real-world data into insight and action.
AI for Biodiversity & Ecology
Decades of fieldwork and specimen-level data collection give me a rare, ground-truth understanding of what ecological data actually looks like. I build AI tools that bridge this domain knowledge — from automated trait extraction and species identification to predictive models linking environmental shifts to biodiversity outcomes — turning nature's complexity into computable, actionable science.
AI for Biomedical & Clinical
Rigorous training in statistical modeling, experimental design, and high-dimensional biological data translates directly into biomedical AI. I develop machine learning and NLP solutions for drug discovery, clinical data analysis, and biomedical knowledge extraction — bringing a scientist's intuition for signal versus noise to every model.
AI as a Service
Beyond research, I'm interested in delivering AI capability where it's needed most. Whether that's building custom LLM-powered workflows, designing data pipelines for organizations sitting on untapped data, or prototyping intelligent tools for niche domains — the same principled, end-to-end approach applies.
Skills & Tools
From raw data to deployed models.
Machine Learning
Generative AI & LLMs
NLP & Text Intelligence
Data Engineering
Programming
Scientific Computing
Application Domains
Where AI meets scientific impact.
Clinical & Biomedical AI
Building machine learning solutions for drug discovery, clinical trial optimization, and biomedical data analysis — translating complex, high-dimensional datasets into actionable insights for healthcare.
Biomedical NLP & Knowledge Graphs
Designing NLP pipelines and knowledge graph systems for biomedical literature mining, entity extraction, and large-scale question answering across clinical and scientific corpora.
AI Tools for Biodiversity & Ecology
Developing end-to-end AI tools for ecological research — from automated species identification and trait extraction to predictive models linking environmental change to biodiversity outcomes.
Nature Digitization Pipelines
Building robust computational pipelines in Python and R to process multidimensional biological data — from multispectral reflectance to environmental sensor arrays — and extract macroecological signals at scale.