Tristan B

- Research Program Mentor

Bio

Project ideas

AI for Climate Change

Recent years have seen an explosion of advancements in AI, and the climate and environmental sciences have only begun to adopt these latest methods. Several avenues for projects exist based on student interest in learning certain AI methods (computer vision, natural language processing (NLP), time series methods with transformers) and the climate domain of interest (wildfire, heatwaves, ecosystem impacts, climate justice). Some potential examples include: 1) Using computer vision models to classify wildfire smoke plumes from satellite imagery 2) Using computer vision 'super-resolution' models to enhance the spatial resolution of climate forecasts 3) Time series forecasting of variables like temperature or water levels 4) Natural language processing (NLP) models like BERT applied to classifying climate change misinformation in text data Learning outcomes would include 1) Experience doing an end-to-end data science project on real-world data 2) In-depth knowledge of a particular AI model/algorithm, 2) Python coding experience implementing, training, and fine-tuning the model. A variety of project outcomes are possible, with the most common being a research paper. Prerequisites: Some experience with Python programming. Prior AI coursework experience not expected.

Modeling local climate change impacts

This project explores expected climate change impacts on the student's hometown or any other location of particular interest. The learning objectives include an understanding of the fundamentals of climate science and the tools used to predict future impacts. I am happy to guide the student in framing an engaging topic and to support them in obtaining relevant data, such as historical weather data and predictions out to 2100 from climate models (e.g. from NASA). Specific research directions can be catered to the student's interest, for example placing greater emphasis on statistical analysis or instead diving deeper into policy frameworks (e.g. Paris Climate Accord) and public health dimensions. Students are welcome to think outside the box when considering climate change impacts, which can have a range of secondary effects. For example, droughts have been linked to major conflicts and international migration, sea level rise can exacerbate income inequality, and warmer temperatures can increase the spread of mosquito and tick-borne disease. Potential project outcomes could range from research papers to data analyses to interactive data visualizations.

How has climate change affected the likelihood of extreme events?

When a natural disaster hits, it is now common to ask whether climate change played a role. From Hurricane Sandy in 2012, to recent heatwaves across Russia, to the massive Great Barrier Reef coral bleaching event, understanding and quantifying the influence of climate change, if any, is increasingly relevant to scientists, local communities, and policymakers. Scientists approach tackling these types of questions through a framework known as detection and attribution, a framework that is currently at the cutting edge of climate science. In short, for a given extreme event, one compares the likelihood of that event occurring in two worlds: the first world being as close to reality as we know, and the second world representing a fictional planet where the industrial revolution essentially never occurred. By comparing the odds of the extreme event in these two worlds, you can begin to quantify the influence of climate change. Fortunately, a degree in climate modeling or computer science isn't necessary to explore these questions. Relevant datasets from climate models can be analyzed in software like Python, R, or Excel. I can help provide this data, or if the student is interested in gaining additional Python experience I can provide example code. The primary learning objectives are 1) Research the various drivers of a particular extreme event, 2) Explore how climate change may influence these drivers, 3) Understand the detection and attribution framework, as well as its controversies, and 4) Gain experience analyzing data and telling a compelling story through data science and data visualizations.

Coding skills

Credentials

Reviews

"Working with Tristan was an absolutely wonderful experience. Tristan helped me learn coding in R and data science (from scratch), then apply it to analyse data for my project, and finally complete my research paper, all within 3 months (8-9 sessions). Working with Tristan has been, by far, one of the best academic experience that I have ever had. He made understanding complex topics simple, the approach to writing long research papers natural and easy (as compared to strenuous and difficult), and learning -- FUN. With his insightful feedback, curated assignments, and extremely thorough knowledge eventually outdo my expectations for my Polygence project, and also gain more confidence with Data Science. He was very flexible with scheduling, very understanding when it came to my other commitments (like test prep, college app), and super easy to communicate with through and between my sessions. Tristan also inspired me to spark personal growth and find more clarity with my career goals. He made sure that I achieved all my goals for my Polygence Project, and always gave me extra time post our general discussions to make sure that my Polygence learning experience could great as possible. I feel fortunate to have worked with Tristan."

Arihant from Kolkata, India