• S. Giffard-Roisin, M. Yang, G. Charpiat, B. Kégl, and C. Monteleoni, “Fused Deep Learning for Hurricane Track Forecast From Reanalysis Data.” In Proceedings of the 8th International Workshop on Climate Informatics (CI), 2018. Poster presentation by Giffard-Roisin.

  • S. McQuade and C. Monteleoni, “Spatiotemporal Global Climate Model Tracking.” In Petascale Analytics: Large-Scale Machine Learning in the Earth Sciences, Srivastava, Nemani, Steinhaeuser (Eds.), Chapman & Hall/CRC, August 2017. Invited.

  •  “Spatiotemporal online learning with expert advice, with applications to climate science and finance.” Workshop on Machine Learning for Spatiotemporal Forecasting, Neural Information Processing Systems (NIPS) Conference, Barcelona, Spain, December 2016. Invited.

  •  “Advances in Climate Informatics: Accelerating Discovery in Climate Science with Machine Learning.” A New Look at Climate Diagnosis and Modeling in the Era of Climate Informatics, session at the American Geophysical Union (AGU) Fall Meeting, San Francisco, CA, December 2015. Invited.

  •  C. Tang and C. Monteleoni, “Can Topic Modeling Shed Light on Climate Extremes?” in IEEE Computing in Science and Engineering (CISE) Magazine, Special Issue on Computing & Climate. Vol. 17, no. 6, pp. 43–52, Nov./Dec. 2015.

  •  Scott McQuade, Claire Monteleoni, “Multi-Task Learning from a Single Task: Can Different Forecast Periods be Used to Improve Each Other?” In The Fifth International Workshop on Climate Informatics, The National Center for Atmospheric Research, 2015. Spotlight and poster presentation by McQuade.

  •  Mahesh Mohan, Cheng Tang, Claire Monteleoni, Timothy DelSole, and Benjamin Cash, “Seasonal Prediction Using Unsupervised Feature Learning and Regression.” In The Fifth International Workshop on Climate Informatics, The National Center for Atmospheric Research, 2015. Spotlight and poster presentation by Mohan and Tang.

  •  Timothy DelSole, Claire Monteleoni, Scott McQuade, Michael K. Tippett, Kathleen Pegion, and J. Shukla, “Tracking Seasonal Prediction Models.” In The Fifth International Workshop on Climate Informatics, The National Center for Atmospheric Research, 2015. Spotlight and poster presentation by McQuade.

  •  C. Tang and C. Monteleoni, “Detecting Extreme Events from Climate Time-Series via Topic Modeling,” in Machine Learning and Data Mining Approaches to Climate Science: Proceedings of the 4th International Workshop on Climate Informatics (CI 2014). Lakshmanan, V., Gilleland, E., McGovern, A., Tingley, M. (Eds.), Springer, 2015. Oral and poster presentation by Tang.

  •  A. Banerjee and C. Monteleoni, “Climate Change: Challenges for Machine Learning,” Invited Tutorial at NIPS 2014. Oral presentation by Banerjee and Monteleoni. [Slides]

  •  “Machine Learning Techniques for Combining Multi-Model Climate Projections,” Invited talk, New Approaches for Pattern Recognition and Change Detection, session at American Geophysical Union (AGU) Fall Meeting, 2013. Oral presentation by Monteleoni.

  •  S. McQuade and C. Monteleoni, “MRF-Based Spatial Expert Tracking of the Multi-Model Ensemble,” in New Approaches for Pattern Recognition and Change Detection, session at American Geophysical Union (AGU) Fall Meeting, 2013. Oral presentation by McQuade.

  •  M. Ghafarianzadeh and C. Monteleoni, “Climate Prediction via Matrix Completion,” in Workshop on Machine Learning for Sustainability, NIPS 2013.  Poster presentation by Ghafarianzadeh.

  •  M. Ghafarianzadeh and C. Monteleoni, “Climate Prediction via Matrix Completion,” in Workshop for Women in Machine Learning (WiML), collocated with NIPS 2013.  Poster presentation by Ghafarianzadeh.

  •  “Climate Informatics: Recent Advances and Challenge Problems for Machine Learning in Climate Science,” Invited talk, Discovery Informatics: AI Takes a Science-Centered View on Big Data, AAAI Fall Symposium Series, 2013. Oral presentation by Monteleoni.

  •  C. Monteleoni, G. A. Schmidt, and S. McQuade, “Climate Informatics: Accelerating Discovery in Climate Science with Machine Learning,” IEEE Computing in Science and Engineering (CISE) Magazine, Special Issue on Machine Learning. Sept.–Oct. 2013 (vol. 15 no. 5) pp. 32–40, 2013. Invited.

  •  “Machine Learning Techniques for Combining Multi-Model Climate Projections,” Invited talk, The Third International Workshop on Climate Informatics, The National Center for Atmospheric Research, 2013. Oral presentation by Monteleoni.

  •  S. McQuade and C. Monteleoni, “MRF-Based Spatial Expert Tracking of the Multi-Model Ensemble,“ The Third International Workshop on Climate Informatics, The National Center for Atmospheric Research, 2013. Poster presentation by McQuade.

  •  M. Ghafarianzadeh and C. Monteleoni, “Climate Prediction via Matrix Completion,” The Third International Workshop on Climate Informatics, The National Center for Atmospheric Research, 2013. Poster presentation by Ghafarianzadeh.

  •  M. Ghafarianzadeh and C. Monteleoni, “Climate Prediction via Matrix Completion” to appear in the Late-Breaking Papers Track, at the Twenty-Seventh Conference on Artificial Intelligence (AAAI), lightning oral and poster presentation by Ghafarianzadeh, Bellevue, WA, July 2013.

  •  C. Monteleoni, G.A. Schmidt, F. Alexander, A. Niculescu-Mizil, K. Steinhaeuser, M. Tippett, A. Banerjee, M.B. Blumenthal, A.R. Ganguly, J.E. Smerdon, and M. Tedesco, “Climate Informatics,” to appear in Computational Intelligent Data Analysis for Sustainable Development; Data Mining and Knowledge Discovery Series. Yu, T., Chawla, N., and Simoff, S. (Eds.), CRC Press, Taylor & Francis Group. Chapter 4, pp. 81–126, 2013. Invited.

  •  S. McQuade and C. Monteleoni, “Global Climate Model Tracking using Geospatial Neighborhoods,” In The Second International Workshop on Climate Informatics, The National Center for Atmospheric Research, 2012. Poster and spotlight oral presentation by McQuade.

  •  S. McQuade and C. Monteleoni, “Global Climate Model Tracking using Geospatial Neighborhoods,” in the Computational Sustainability and AI Special Track, at the Twenty-Sixth Conference on Artificial Intelligence (AAAI), oral and poster presentation by McQuade, Toronto, July 2012.

  •  S. McQuade and C. Monteleoni, “Global Climate Model Tracking using Geospatial Neighborhoods,” in poster presentation by McQuade, The Learning Workshop (Snowbird), April 2012.  

  •  “Climate Informatics,” Invited talk, Workshop on Machine Learning for Sustainability, oral presentation by Monteleoni, NIPS 2011.

  •  C. Monteleoni, G. Schmidt, S. Saroha, and E. Asplund, “Tracking Climate Models” in Journal of Statistical Analysis and Data Mining: Special Issue on Best of CIDU 2010, Volume 4, Issue 4, pp. 72–392, August 2011. Invited.

  •  “Tracking Climate Models:  Advances in Climate Informatics,” Invited talk, The Second IEEE ICDMWorkshop on Knowledge Discovery from Climate Data: Prediction, Extremes, and Impacts, The 10th IEEE International Conference on Data Mining (ICDM), oral presentation by Monteleoni, Sydney, Australia , December 2010.

  •  C. Monteleoni, G. Schmidt, and S. Saroha, “Tracking Climate Models”  In NASA Conference on Intelligent Data Understanding (CIDU), oral presentation by Monteleoni, October 2010.  Awarded Best Application Paper.

  •  C. Monteleoni, S. Saroha, and G. Schmidt, “Tracking Climate Models”  In The Learning Workshop, oral presentation by Monteleoni, Snowbird, April 2010.

  •  C. Monteleoni, S. Saroha, and G. Schmidt, “Can machine learning techniques improve forecasts?”  In Intergovernmental Panel on Climate Change (IPCC) Expert Meeting on Assessing and Combining Multi Model Climate Projections, poster presentation by Schmidt, Boulder, January 2010.

  •  C. Monteleoni, S. Saroha, and G. Schmidt, “Tracking Climate Models”  In Temporal Segmentation: Perspectives from Statistics, Machine Learning, and Signal Processing, Workshop at Neural Information Processing Systems, poster presentation by Saroha, Vancouver, December 2009.