Hi, I’m Theresa! I’m a 3rd year Computer Science PhD student at the University of Minnesota working in the Knowledge Computing Lab with Professor Yao-Yi Chiang.

Download my CV here.

Research Interests

• Machine learning for spatial problems and geographic data. Specifically, my background is in computer vision and adapting computer vision models to better address challenges unique to geographic data.

• Using state-of-the-art deep learning models for environmental applications (see ongoing projects), Developing machine learning models for stronger predictive models in applications such as climate modeling, air quality modeling, and wildlife movement patterns.

• Self-supervised learning and multimodal learning in computer vision.

Projects

Ongoing

• CEDAR: Carbon Estimation with Deep Learning (part of the AI-CLIMATE initiative)
• Peatlands Permafrost Mapping (part of the AI-CLIMATE initiative)
• Automatically Georeferencing Geologic Maps

Finished

• Machine Learning for Species Distribution Modeling
  • Developed a transformer-based model to learn geographic embeddings from multimodal, multi-resolution data in order to ultimately predict the distribution of bird species across the United States.
• Using Deep Neural Networks to Generate Representations of Urban Neighborhoods
  • Understanding urban environments by generating indicators, such as walkability or greenness, requires us to understand how the city is split up; i.e. what the neighborhoods are. By utilizing street view images and publicly available data, such as the human settlement layer, we automatically clustered urban areas into likely neighborhoods using self-supervised machine learning methods.