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Imme Ebert-Uphoff

Has grant

Machine Learning Lead

Colorado State University

United States

Research Interests

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Recent Grants

Grant: Close

Collaborative Research: Understanding Climate Processes with Causal Discovery and Graphs of Information Flow in the Coupled Atmosphere-Land-Ocean System

Open Date: 2015-01-15

Close Date: 2017-12-31

Grant: Close

Workshop on Fundamental Issues and Future Research Directions for Parallel Mechanisms and Manipulators; October 3-4, 2002; Quebec City, Quebec, Canada

Open Date: 2002-04-01

Close Date: 2004-03-31

Grant: Close

ITR/PE+SY Digital Clay for Shape Input and Display

Open Date: 2001-09-01

Close Date: 2007-08-31

Grant: Close

CAREER: New Research Directions for Parallel Manipulators -- Investigation of Redundant Actuation, Redundant Sensing and Static Balancing

Open Date: 2000-07-15

Close Date: 2004-06-30

Articles (16)

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2025

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Christina Kumler (Bonfanti)

University of Colorado Boulder

UNITED STATES

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Imme Ebert-Uphoff

Research Interests

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Recent Grants

Articles (16)

(Re)Conceptualizing trustworthy AI: A foundation for change

Emulating Daytime ABI Cloud Optical Properties at Night With Machine Learning

The influence of correlated features on neural network attribution methods in geoscience

Trust and trustworthy artificial intelligence: A research agenda for AI in the environmental sciences

AI2ES: The NSF AI Institute for Research on Trustworthy AI for Weather, Climate, and Coastal Oceanography

Super-Resolution of GOES-16 ABI Bands to a Common High Resolution with a Convolutional Neural Network

Identifying and Categorizing Bias in AI/ML for Earth Sciences

Exploring the Use of Machine Learning to Improve Vertical Profiles of Temperature and Moisture

Trustworthy Artificial Intelligence for Environmental Sciences: An Innovative Approach for Summer School

Carefully Choose the Baseline: Lessons Learned from Applying XAI Attribution Methods for Regression Tasks in Geoscience

The outlook for AI weather prediction

Leveraging spatiotemporal information in meteorological image sequences: From feature engineering to neural networks

A Primer on Topological Data Analysis to Support Image Analysis Tasks in Environmental Science

Assessing decadal variability of subseasonal forecasts of opportunity using explainable AI

Estimating Full Longwave and Shortwave Radiative Transfer with Neural Networks of Varying Complexity

Creating and Evaluating Uncertainty Estimates with Neural Networks for Environmental-Science Applications

Collaborators (10)

Christopher Wirz

Elizabeth A. Barnes

Min-Seop Ahn

Yuhan Rao

Emily King

Ann Bostrom

Amy McGovern

Marybeth Arcodia

Kyle Hilburn

Christina Kumler (Bonfanti)

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