Introduction to Machine Learning for Ocean Sciences
Workshop, UC Santa Cruz Communicating Research Effectively (CORE) Workshop, Santa Cruz, CA
Talks and presentations
Antarctica InSync International Science Planning Workshop
Workshop, Antarctica InSync International Science Planning Workshop, Frascati, Italy
AI4InSync - Optimizing Southern Ocean Observing Systems using AI.
Oral presentation, Kavli Institute for Theoretical Physics (KITP), The Physics of Changing Polar Climate, Santa Barbara, CA
Probabilistic Forecasts of Interannual Arctic Sea Ice Extent with Data-Driven Statistical Models.
Oral presentation, American Meteorological Society Denver Summit, 18th Conference on Polar Meteorology and Oceanography, Denver, CO
Earth and Climate (ELIC) Seminar Series, Université catholique de Louvain
Seminar, Earth and Climate (ELIC) Seminar Series, Université catholique de Louvain, Louvain-la-Neuve, Belgium
Machine learning is a useful tool to predict and understand sea ice motion in the Arctic.
Oral presentation, Earth Science Information Partners (ESIP) Machine Learning Cluster Meeting, Virtual
Introduction to Machine Learning for Earth and Climate Scientists
Oral presentation, International Global Atmospheric Chemistry (IGAC) Early Career Researchers Online Conference, Virtual
Freshwater Processes in the Upper Ocean.
Oral presentation, Physical Oceanography Dissertation Symposium (PODS), Lihue, HI
Workshop on the Role of Sea Ice and its Variability in the Climate System, International Centre for Theoretical Physics (ICTP)
Poster, Workshop on the Role of Sea Ice and its Variability in the Climate System, International Centre for Theoretical Physics (ICTP), Trieste, Italy
Earth and Climate (ELIC) Seminar Series, Université catholique de Louvain
Seminar, Earth and Climate (ELIC) Seminar Series, Université catholique de Louvain, Louvain-la-Neuve, Belgium
Machine learning is a useful tool to predict and understand sea-ice motion in the Arctic.
Oral presentation, Interagency Arctic Research Policy Committee (IARPC) Modelers' Community of Practice March 2024 Meeting – Combining Modeling and Machine Learning Approaches to Understanding the Arctic Earth System, Virtual
Mentoring Physical Oceanography Women+ to Increase Retention (MPOWIR) Pattullo Conference
, Mentoring Physical Oceanography Women+ to Increase Retention (MPOWIR) Pattullo Conference, Warrenton, VA
54th International Liège Colloquium on Ocean Dynamics: Machine learning and data analysis in oceanography
Poster, 54th International Liège Colloquium on Ocean Dynamics: Machine learning and data analysis in oceanography, Liège, Belgium
SeaSAR 2023
, SeaSAR 2023, Svalbard, Norway
Machine learning is a useful tool to predict and understand sea-ice motion in the Arctic.
Seminar, NCAR Sea Ice Modeling Group Meeting, Boulder, CO
Machine learning is a useful tool to predict and understand sea-ice motion in the Arctic.
Seminar, Atmospheric and Ocean Sciences Forum, University of Colorado Boulder, Boulder, CO
Machine learning is a useful tool to predict and understand sea-ice motion in the Arctic.
Poster, Scientific Machine Learning Symposium, San Diego, CA
Machine learning is a useful surrogate model to parameterize and understand sea-ice motion in the Arctic.
Oral presentation, ECCO Annual Meeting, Pasadena, CA
Machine learning is a useful tool to predict and understand sea-ice motion in the Arctic.
Invited talk, AI for the Study of Environmental Risk (AI4ER) Seminar Series, University of Cambridge, Cambridge, UK
9th Annual FIRO Workshop
Poster, 9th Annual FIRO Workshop, San Diego, CA
Observing, Modeling, and Understanding the Circulation of the Arctic Ocean and Sub-Arctic Seas Workshop
Poster, Observing, Modeling, and Understanding the Circulation of the Arctic Ocean and Sub-Arctic Seas Workshop, Seattle, WA
Center for Western Weather and Water Extremes (CW3E) Annual Meeting
Poster, Center for Western Weather and Water Extremes (CW3E) Annual Meeting, San Diego, CA
Ocean surface salinity response to atmospheric river (AR) events in the California Current System
Oral presentation, Ocean Salinity Conference, New York, NY
Machine learning is a useful tool to predict and understand sea-ice dynamics in the Arctic.
Poster, UCSD Jacobs School of Engineering Research Expo, San Diego, CA
Machine Learning for Evaluating the Drivers of Variability in Arctic Sea-Ice Dynamics to Improve Forecasting
Oral presentation, Ocean Sciences Meeting, Virtual
Machine Learning for Evaluating the Drivers of Variability in Arctic Sea-Ice Dynamics to Improve Forecasting.
Oral presentation, American Meteorological Society Collective Madison Meeting, 17th Conference on Polar Meteorology and Oceanography, Madison, WI
Ocean surface salinity response to atmospheric river (AR) events in the California Current System.
Oral presentation, NOAA Science Seminar Series, Virtual
Ocean surface salinity response to atmospheric river events in the California Current System.
Oral presentation, American Meteorological Society 101st Annual Meeting, Virtual
International Atmospheric Rivers Conference Sponsored Symposium
Conference presentation, International Atmospheric Rivers Conference Sponsored Symposium, Virtual
Ocean Sciences Meeting
Conference presentation, Ocean Sciences Meeting, San Diego, CA
Institute of Electrical and Electronics Engineers Global Humanitarian Technology Conference
Poster, Institute of Electrical and Electronics Engineers Global Humanitarian Technology Conference, Seattle, WA