Talks and Presentations

Public Talks

I have given several public-facing talks, mostly about artificial intelligence and tropical meteorology:
  • Tropical Meteorology: Challenges and Opportunities, a United Nations AI for Good Seminar, delivered with Professor Tom Beucler in 2023.
  • AI and Tropical Cyclone Forecasting, a talk on AI and tropical cyclone forecasting, given as part of the 2025 Weather and Climate Livestream.

    • Invited Talks

    I have also given several invited talks at scientific conferences, mostly on the topic of Causality, Interpretability, and Uncertainty:
  • Causality and Uncertainty in Machine Learning for Climate Science". In 2025, I gave a keynote talk at the Gordon Research Conference on Machine Learning for Actionable Climate Science.
  • Causality and Interpretability in Climate Modeling, a talk at the Aspen Global Change Institute's workshop on machine learning for climate modeling.

    • Conference Presentations

    Some of my most recent conference presentations are listed here; a full list can be found in my CV.
  • McGraw, M.C.,, J. Rogers, and H. Hampson: Estimating Flood Risk for an Atlantic Meridional Overturning Circulation Collapse: A Study of Climate Tipping Points in Climate Risk Modeling. Houston Forum on Climate Linked Economics, American Meteorological Society Annual Meeting, Houston, TX. 01/2026.
  • McGraw, M.C., K. Haynes, K.D. Musgrave, I. Ebert-Uphoff, C. Slocum, and J. Knaff: Exploring Tropical Cyclone Structure and Evolution with AI-Based Synthetic Passive Microwave Data. 24th AI Conference, American Meteorological Society Annual Meeting, New Orleans, LA. 01/2025.
  • M. DeMaria, E.A. Barnes, M. Fernandez, R.J. Barnes, M. McGraw, G. Chirokova, L. Lu, P. Santos, and W.A. Hogsett: Applications of a Machine Learning Model for Estimating Tropical Cyclone Track and Intensity Forecast Uncertainty. 36th AMS Conference on Hurricanes and Tropical Meteorology, Long Beach, CA. 05/2024.
  • McGraw, M.C.,, K. Haynes, K.D. Musgrave, I. Ebert-Uphoff, C. Slocum, and J. Knaff: Tropical Cyclone Structure and Evolution in an AI-Based Synthetic Passive Microwave Dataset. 36th AMS Conference on Hurricanes and Tropical Meteorology, Long Beach, CA. 05/2024.
  • Gomez, M.S., M. McGraw, L. Poulain-Auzeau, F. I. H. Tam, S.G. Sudheesh, S.J. Camargo, D.R. Chavas, Y. Cohen, and T. Beucler: TCBench: A Platform for the Data-Driven Prediction of Tropical Cyclones (poster). 36th AMS Conference on Hurricanes and Tropical Meteorology, Long Beach, CA. 05/2024.
  • Beucler, T.G., S. Ganesh Sudheesh, F. I.-H. Tam, M. S. Gomez, M. McGraw, M. DeMaria, K.D. Musgrave, A. Gerhardus, and J. Runge: Causal Feature Selection for Tropical Cyclone Intensity Forecasting. 23rd AI Conference, American Meteorological Society Annual Meeting, Baltimore, MD. 01/2024.
  • McGraw, M.C.,K.D. Musgrave, J.A. Knaff, C.J. Slocum, and I. Ebert-Uphoff: What can machine learning methods tell us about the tropical cyclone intensity forecasting problem? 22nd AI Conference, American Meteorological Society Annual Meeting, Denver, CO. 01/2023.
  • Baldwin, M.R., C.J. Slocum, and M. McGraw: Using AI To Quantify Uncertainty on Tropical Cyclogenesis. Student Conference, American Meteorological Society Annual Meeting, Denver, CO. 01/2023.
  • Haynes, K., R. Lagerquist, M. McGraw, K. Musgrave, and I. Ebert-Uphoff: Creating and Evaluating Uncertainty Estimates with Neural Networks for Environmental-Science Applications. 22nd AI Conference, American Meteorological Society Annual Meeting, Denver, CO. 01/2023.
  • McGovern, A., A. Bostrom, D.J. Gagne II, I. Ebert-Uphoff, K. Musgrave, M. McGraw, and R. Chase: Classifying and Addressing Bias in AI/ML for the Earth Sciences. 22nd AI Conference, American Meteorological Society Annual Meeting, Denver, CO. 01/2023.
  • Sospreda-Alfonso, R., Exenberger, J., Dang, K., and M.C. McGraw: Statistical adjustment of decadal climate predictions using deep learning (spotlight presentation). Tackling Climate Change with Machine Learning Workshop, NeurIPS 2022.