- High dimensional statistics
- Applied statistics and machine learning
- Fairness and ethics in data science
- Machine learning for climate science
A. Stevens, R. Willett, A. Mamalakis, E. Foufoula-Georgiou, J. Randerson, P. Smyth, S. Wright and A. Tejedor. Graph-guided regularization for improved seasonal forecasting. In Brajard, J., Charantonis, A., Chen, C., & Runge, J. (Eds.). (2019). Proceedings of the 9th International Workshop on Climate Informatics: CI 2019 (No. NCAR/TN- 561+PROC). doi:10.5065/y82j-f154.
Conference Presentations and Posters
Graph-guided regularization for improved forecasting of Southwestern US winter precipitation, AGU , San Francisco, CA, December 2019.
Graph-guided regularization for improved seasonal forecasting, Workshop on Climate Informatics, Paris, France, September 2019. (poster)
Leveraging large ensemble climate simulations and graph-guided regularization for improving seasonal hydroclimatic forecasting,Large Ensembles Workshop, Boulder, CO, July 2019. (poster)
Leveraging large ensemble climate simulations and graph-guided regularization for improving seasonal hydroclimatic forecasting, Midwest Machine Learning Symposium, Madison, WI, June 2019. (poster)
“Graph Total Variation for Seasonal Forecasting,” Computational and Applied Mathematics Student Seminar, Chicago, IL, April 2019.