Efficient workspace generation for binary manipulators with many actuators. - Imme Ebert-Uphoff; Gregory S. Chirikjian (J. Field Robotics, 1995)

Inverse kinematics of discretely actuated hyper-redundant manipulators using workspace densities. - Imme Ebert-Uphoff; Gregory S. Chirikjian (ICRA, 1996)

Evaluating efficiency of self-reconfiguration in a class of modular robots. - Gregory S. Chirikjian; Amit Pamecha; Imme Ebert-Uphoff (J. Field Robotics, 1996)

Useful metrics for modular robot motion planning. - Amit Pamecha; Imme Ebert-Uphoff; Gregory S. Chirikjian (IEEE Trans. Robotics Autom., 1997)

Discretely Actuated Manipulator Workspace Generation using Numerical Convolution on the Euclidean Group. - Gregory S. Chirikjian; Imme Ebert-Uphoff (ICRA, 1998)

Numerical convolution on the Euclidean group with applications to workspace generation. - Gregory S. Chirikjian; Imme Ebert-Uphoff (IEEE Trans. Robotics Autom., 1998)

Corrections To "Numerical Convolution On The Euclidean Group With Applications To Workspace Generation". - Gregory S. Chirikjian; Imme Ebert-Uphoff (IEEE Trans. Robotics Autom., 1998)

Dynamic Modeling of a Class of Spatial Statically-Balanced Parallel Platform Mechanisms. - Imme Ebert-Uphoff; Clément Gosselin (ICRA, 1999)

Investigation of the Deficiencies of Parallel Manipulators in Singular Configurations Through the Jacobian Nullspace. - Vincent K. Chan; Imme Ebert-Uphoff (ICRA, 2001)

Motion Planning for Active Acceleration Compensation. - Michael W. Decker; Anh X. Dang; Imme Ebert-Uphoff (ICRA, 2001)

Two Viewpoints on the Unconstrained Motion of Parallel Manipulators at or Near Singular Configurations. - Philip A. Voglewede; Imme Ebert-Uphoff (ICRA, 2002)

A Novel Mechanism for Implementing Multiple Collocated Spherical Joints. - Paul Bosscher; Imme Ebert-Uphoff (ICRA, 2003)

Digital clay: architecture designs for shape-generating mechanisms. - Paul Bosscher; Imme Ebert-Uphoff (ICRA, 2003)

Finger Sculpting with Digital Clay: 3D Shape Input and Output through a Computer-Controlled Real Surfac. - Jarek Rossignac; Mark Allen; Wayne J. Book; Ari Glezer; Imme Ebert-Uphoff; Christopher D. Shaw; David Rosen 001; Stephen Askins; Jing Bai; Paul Bosscher; Joshua Gargus; ByungMoon Kim; et al. (Shape Modeling International, 2003)

Introducing parallel manipulators through laboratory experiments. - Imme Ebert-Uphoff (IEEE Robotics Autom. Mag., 2003)

A Stability Measure for Underconstrained Cable-driven Robots. - Paul Bosscher; Imme Ebert-Uphoff (ICRA, 2004)

Wrench-based Analysis of Cable-driven Robots. - Paul Bosscher; Imme Ebert-Uphoff (ICRA, 2004)

On the Connections between Cable-driven Robots, Parallel Manipulators and Grasping. - Imme Ebert-Uphoff; Philip A. Voglewede (ICRA, 2004)

Force-feasible Workspace Analysis for Underconstrained, Point-mass Cable Robots. - Andrew T. Riechel; Imme Ebert-Uphoff (ICRA, 2004)

Measuring "Closeness" to Singularities for Parallel Manipulators. - Philip A. Voglewede; Imme Ebert-Uphoff (ICRA, 2004)

Active acceleration compensation for transport vehicles carrying delicate objects. - Anh X. Dang; Imme Ebert-Uphoff (IEEE Trans. Robotics, 2004)

Application of the Antipodal Grasp Theorem to Cable-Driven Robots. - Philip A. Voglewede; Imme Ebert-Uphoff (IEEE Trans. Robotics, 2005)

Overarching framework for measuring closeness to singularities of parallel manipulators. - Philip A. Voglewede; Imme Ebert-Uphoff (IEEE Trans. Robotics, 2005)

Disturbance Robustness Measures for Underconstrained Cable-driven Robots. - Paul Bosscher; Imme Ebert-Uphoff (ICRA, 2006)

Wrench-feasible workspace generation for cable-driven robots. - Paul Bosscher; Andrew T. Riechel; Imme Ebert-Uphoff (IEEE Trans. Robotics, 2006)

Causal Discovery from Spatio-Temporal Data with Applications to Climate Science. - Imme Ebert-Uphoff; Yi Deng 006 (ICMLA, 2014)

Using Causal Discovery to Track Information Flow in Spatio-Temporal Data - A Testbed and Experimental Results Using Advection-Diffusion Simulations. - Imme Ebert-Uphoff; Yi Deng 006 (CoRR, 2015)

Identifying Physical Interactions from Climate Data: Challenges and Opportunities. - Imme Ebert-Uphoff; Yi Deng 006 (Comput. Sci. Eng., 2015)

High-Dimensional Dependency Structure Learning for Physical Processes. - Jamal Golmohammadi; Imme Ebert-Uphoff; Sijie He; Yi Deng 006; Arindam Banerjee 001 (ICDM, 2017)

Causal discovery in the geosciences - Using synthetic data to learn how to interpret results. - Imme Ebert-Uphoff; Yi Deng 006 (Comput. Geosci., 2017)

Intelligent systems for geosciences: an essential research agenda. - Yolanda Gil; Suzanne A. Pierce; Hassan A. Babaie; Arindam Banerjee 001; Kirk D. Borne; Gary S. Bust; Michelle Cheatham; Imme Ebert-Uphoff; Carla P. Gomes; Mary C. Hill; John D. Horel; Leslie Hsu; et al. (Commun. ACM, 2019)

Physically Interpretable Neural Networks for the Geosciences: Applications to Earth System Variability. - Benjamin A. Toms; Elizabeth A. Barnes; Imme Ebert-Uphoff (CoRR, 2019)

Machine Learning for the Geosciences: Challenges and Opportunities. - Anuj Karpatne; Imme Ebert-Uphoff; Sai Ravela; Hassan Ali Babaie; Vipin Kumar 001 (IEEE Trans. Knowl. Data Eng., 2019)

Explainable Artificial Intelligence in Meteorology and Climate Science: Model Fine-Tuning, Calibrating Trust and Learning New Science. - Antonios Mamalakis; Imme Ebert-Uphoff; Elizabeth A. Barnes (xxAI@ICML, 2020)

Development and Interpretation of a Neural Network-Based Synthetic Radar Reflectivity Estimator Using GOES-R Satellite Observations. - Kyle A. Hilburn; Imme Ebert-Uphoff; Steven D. Miller (CoRR, 2020)

Evaluation, Tuning and Interpretation of Neural Networks for Meteorological Applications. - Imme Ebert-Uphoff; Kyle A. Hilburn (CoRR, 2020)

Neural Network Attribution Methods for Problems in Geoscience: A Novel Synthetic Benchmark Dataset. - Antonios Mamalakis; Imme Ebert-Uphoff; Elizabeth A. Barnes (CoRR, 2021)

CIRA Guide to Custom Loss Functions for Neural Networks in Environmental Sciences - Version 1. - Imme Ebert-Uphoff; Ryan Lagerquist; Kyle Hilburn; Yoonjin Lee; Katherine Haynes; Jason Stock; Christina Kumler; Jebb Q. Stewart (CoRR, 2021)

The Need for Ethical, Responsible, and Trustworthy Artificial Intelligence for Environmental Sciences. - Amy McGovern; Imme Ebert-Uphoff; David John Gagne II; Ann Bostrom (CoRR, 2021)

Investigating the fidelity of explainable artificial intelligence methods for applications of convolutional neural networks in geoscience. - Antonios Mamalakis; Elizabeth A. Barnes; Imme Ebert-Uphoff (CoRR, 2022)

Can we integrate spatial verification methods into neural-network loss functions for atmospheric science? - Ryan Lagerquist; Imme Ebert-Uphoff (CoRR, 2022)

A Primer on Topological Data Analysis to Support Image Analysis Tasks in Environmental Science. - Lander Ver Hoef; Henry Adams; Emily J. King; Imme Ebert-Uphoff (CoRR, 2022)

Carefully choose the baseline: Lessons learned from applying XAI attribution methods for regression tasks in geoscience. - Antonios Mamalakis; Elizabeth A. Barnes; Imme Ebert-Uphoff (CoRR, 2022)

Tools for Extracting Spatio-Temporal Patterns in Meteorological Image Sequences: From Feature Engineering to Attention-Based Neural Networks. - Akansha Singh Bansal; Yoonjin Lee; Kyle Hilburn; Imme Ebert-Uphoff (CoRR, 2022)

Building Unified Global 3D Cloud Data From Multiple Satellites For Advancing Weather And Climate Research. - Yoo-Jeong Noh; John M. Haynes; Steven D. Miller; Charles White; Brandon Daub; Imme Ebert-Uphoff; Hungjui Yu; Jason Apke; Randy J. Chase; Katherine Haynes; Jeremy Solbrig; Evan Rose; et al. (IGARSS, 2024)

AI2ES: The NSF AI Institute for Research on Trustworthy AI for Weather, Climate, and Coastal Oceanography. - Amy McGovern; Imme Ebert-Uphoff; Elizabeth A. Barnes; Ann Bostrom; Mariana G. Cains; Phillip Davis; Julie L. Demuth; Dimitrios I. Diochnos; Andrew H. Fagg; Philippe Tissot; John K. Williams; Christopher D. Wirz (AI Mag., 2024)

SRViT: Vision Transformers for Estimating Radar Reflectivity from Satellite Observations at Scale. - Jason Stock; Kyle Hilburn; Imme Ebert-Uphoff; Charles W. Anderson (CoRR, 2024)

Evaluation of Tropical Cyclone Track and Intensity Forecasts from Artificial Intelligence Weather Prediction (AIWP) Models. - Mark DeMaria; James L. Franklin; Galina Chirokova; Jacob Radford; Roberto Demaria; Kate D. Musgrave; Imme Ebert-Uphoff (CoRR, 2024)

Center-fixing of tropical cyclones using uncertainty-aware deep learning applied to high-temporal-resolution geostationary satellite imagery. - Ryan Lagerquist; Galina Chirokova; Robert DeMaria; Mark DeMaria; Imme Ebert-Uphoff (CoRR, 2024)

(Re)Conceptualizing trustworthy AI: A foundation for change. - Christopher D. Wirz; Julie L. Demuth; Ann Bostrom; Mariana G. Cains; Imme Ebert-Uphoff; David John Gagne; Andrea Schumacher; Amy McGovern; Deianna Madlambayan (Artif. Intell., 2025)

Score-based diffusion nowcasting of GOES imagery. - Randy J. Chase; Katherine Haynes; Lander Ver Hoef; Imme Ebert-Uphoff (CoRR, 2025)

Transparent Machine Learning: Training and Refining an Explainable Boosting Machine to Identify Overshooting Tops in Satellite Imagery. - Nathan Mitchell; Lander Ver Hoef; Imme Ebert-Uphoff; Kristina Moen; Kyle Hilburn; Yoonjin Lee; Emily J. King (CoRR, 2025)