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I'm a Data Scientist at Marine Biologics currently working on developing predictive models to make food and beverage ingredient discovery and development more standardized and driven by a variety of data sources.

I have a background in computational and theoretical physical chemistry, particularly in statistical mechanics and biophysics. I've worked in the biopharma space for a few years at the intersection of cheminformatics and machine learning, with emphasis on pose/binding mode prediction, (equivariant) graph neural networks, and model explainability. My scientific publications are available on Google Scholar.

• Marine Biologics, Director of AI/ML
• Independent, Data Science Consultant
• Numerion Labs (fka Atomwise), Cheminformatics Scientist II
• Enel X, Lead Software Engineer in Distributed Energy Resources

• University of California-Berkeley, Ph.D. in Chemistry (emphasis: Physical)
• Hope College, B.S. in Chemistry and Mathematics

In the very near future I hope to dive into

• Streamlit applications
• Productionizing ML models from scratch
• Forecasting

I welcome any suggestions for where to get started on any of these topics, be they tutorials, open-source projects, or something else!

• Cheminformatics
• Handling messy data
• Developing data projects based on ambiguous requirements
• Working with highly interdisciplinary teams
• How to improve written communication
• PhD -> industry
• Applying to PhD programs