Who Am I? (as a professional)

I am a climate data scientist seeking to increase society’s resilience to climate variability and anthropogenic climate change by improving quantitative climate forecasting and climate modeling. I received my M.A. in Climate & Society at Columbia University, and my B.S. in Computer Science at the College of William & Mary, specializing in AI/ML and data science methods. My work falls at the intersection of the two fields.

Currently, I am developing the next generation of Python Climate Forecasting tools, including XCast and PyCPT. Through this work, I am trying bridge the gap between traditional climate forecasting tools, like IRI’s Climate Predictability Tool, and the Python climate data science ecosystem. I want to make it easier to use state-of-the-art machine learning methods for statistical bias correction and climate forecasting. I believe that higher-quality, easier-to-produce climate forecasts will help stakeholders adapt to a changing climate.

While my main focus has been on climate forecasting tool development, I also work on designing and implementing tailored operational climate forecast systems and knowledge transfer tools (websites, reports, maprooms). I have worked with partners on operational Forecast-based Finance (FBF) forecasts and a number of other projects using domain-specific climate knowledge and a variety to technology stacks. Climate information is not useful if it never makes it into the hands of those who need it.

Who Am I? (as a person)

I’m currently located in Baltimore, MD, where you’ll find me in the park playing soccer, running, hammocking, or generally enjoying being outside and spending time with friends. I love to hike/camp/backpack when I get the chance. I am a lifelong learner and love to study anything from statistics, to religion, to sociology, to science and back. If you want to hear my rant, ask me about the terrible fantasy novel I’m probably reading, or about my latest ideas about machine learning/rain forecasting!

Research Interests:

• Geospatial Data Science
• Artificial Intelligence
• Deep Learning
• High Performance Computing
• Climate Forecasting
• Statistical Forecasting Methods