Imme Ebert-Uphoff
Imme Ebert-Uphoff

Machine Learning Lead (CIRA) / Research Professor (ECE)

iebert@colostate.edu


Cooperative Institute for Research in the Atmosphere
Colorado State University
1375 Campus Delivery
Fort Collins, CO 80523-1375

CIRA building – Room 41

Please visit our CIRA ML website to learn about CIRA’s ML/AI activities.

 I have three highly connected roles here at Colorado State University.

  1. Research Professor in Electrical and Computer Engineering (ECE). I joined ECE in 2011.
  2. Machine learning lead at the Cooperative Institute for Research in the Atmosphere (CIRA). I joined CIRA in July 2019 to support its use of machine learning (ML) techniques.
  3. CSU lead for the NSF AI Institute for Research on Trustworthy AI in Weather, Climate and Coastal Oceanography (AI2ES). AI2ES is a 5-year $20M AI institute funded by the NSF, led by Amy McGovern at OU. It just got funded in Fall 2020.  CSU is one of the primary partners of the institute.

Teams:

Publications

Since 2019 I spend most of my time working at CIRA:

Other roles:

Below are some samples of the work I’m involved in:

See longer list of publications below.

How to reach me:

Journal Publications

2025:

  • Lagerquist, R., J. Knaff, C. Slocum, K. Musgrave, and I. Ebert-Uphoff. Identifying data sources and physical strategies used by neural networks to predict TC rapid intensification. Weather and Forecasting (in press), April 2025.
  • Radford, J.T., Ebert-Uphoff, I. and Stewart, J.Q., 2025. A Comparison of AI Weather Prediction and Numerical Weather Prediction Models for 1–7-Day Precipitation Forecasts. Weather and Forecasting. Early online release, March 3, 2025.  https://doi.org/10.1175/WAF-D-24-0081.1 
  • Wirz, Christopher D., Julie L. Demuth, Ann Bostrom, Mariana G. Cains, Imme Ebert-Uphoff, David John Gagne II, Andrea Schumacher, Amy McGovern, and Deianna Madlambayan. “(Re) Conceptualizing trustworthy AI: A foundation for change.” Artificial Intelligence (2025): Vol 342, 104309. Early online release, Feb 2025. https://doi.org/10.1016/j.artint.2025.104309
  • Jacob T. Radford, Imme Ebert-Uphoff, Jebb Q. Stewart, Kate D. Musgrave, Robert DeMaria, Natalie Tourville, Kyle Hilburn, Accelerating Community-Wide Evaluation of AI Models for Global Weather Prediction by Facilitating Access to Model Output. Bulletin of the American Meteorological Society, 106(1), pp.E68-E76. Jan 15, 2025. https://doi.org/10.1175/BAMS-D-24-0057.1

2024:

  • 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, 2024. AI2ES: The NSF AI Institute for Research on Trustworthy AI for Weather, Climate, and Coastal Oceanography, AI magazine (Journal of the Association for the Advancement of AI), early online release, Feb 14, 2024.  https://doi.org/10.1002/aaai.12160 
  • McGovern, A., Bostrom, A., McGraw, M., Chase, R.J., Gagne, D.J., Ebert-Uphoff, I., Musgrave, K.D. and Schumacher, A., 2024. Identifying and Categorizing Bias in AI/ML for Earth Sciences. Bulletin of the American Meteorological Society. https://doi.org/10.1175/BAMS-D-23-0196.1 (Jan 2024)
  • White, C.H., Ebert-Uphoff, I., Haynes, J.M. and Noh, Y.J., 2024. Super-Resolution of GOES-16 ABI Bands to a Common High Resolution with a Convolutional Neural Network. Artificial Intelligence for the Earth Systems.  https://doi.org/10.1175/AIES-D-23-0065.1
  • Haynes, K., Stock, J., Dostalek, J., Anderson, C. and Ebert-Uphoff, I., 2024. Exploring the Use of Machine Learning to Improve Vertical Profiles of Temperature and Moisture. Artificial Intelligence for the Earth Systems, 3(1), p.e220090. https://doi.org/10.1175/AIES-D-22-0090.1 (Dec 2023 online, Jan 2024 print)

2023:

  • Ann Bostrom, Julie L. Demuth, Christopher D. Wirz, Mariana G. Cains, Andrea Schumacher, Deianna Madlambayan, Akansha Singh Bansal, Angela Bearth, Randy Chase, Katherine M. Crosman, Imme Ebert-Uphoff, David John Gagne II, Seth Guikema, Robert Hoffman, Branden B. Johnson, Christina Kumler-Bonfanti, John D. Lee, Anna Lowe, Amy McGovern, Vanessa Przybylo, Jacob T. Radford, Emilie Roth, Carly Sutter, Philippe Tissot, Paul Roebber, Jebb Q. Stewart, Miranda White, John K. Williams, 2023. Trust and trustworthy artificial intelligence: A research agenda for AI in the environmental sciences. Risk Analysis. https://doi.org/10.1111/risa.14245 (Nov 2023)
  • Lagerquist, R., Turner, D.D., Ebert-Uphoff, I. and Stewart, J.Q., 2023. Estimating full longwave and shortwave radiative transfer with neural networks of varying complexity. Journal of Atmospheric and Oceanic Technology.   https://doi.org/10.1175/JTECH-D-23-0012.1 (Aug 2023)
  • Bansal, A.S., Lee, Y., Hilburn, K. and Ebert-Uphoff, I., 2023. Leveraging spatiotemporal information in meteorological image sequences: From feature engineering to neural networks. Environmental Data Science, 2, p.e31. https://doi.org/10.1017/eds.2023.26 (July 2023)
  • Nature commentary: I. Ebert-Uphoff and Kyle Hilburn, The outlook for AI weather prediction, Nature 619, 473-474 (2023), https://doi.org/10.1038/d41586-023-02084-9 (July 2023). Free access link: https://rdcu.be/df8LB
  • McGovern, A., Gagne, D.J., Wirz, C.D., Ebert-Uphoff, I., Bostrom, A., Rao, Y., Schumacher, A., Flora, M., Chase, R., Mamalakis, A. and McGraw, M., 2023. Trustworthy Artificial Intelligence for Environmental Sciences: An Innovative Approach for Summer School. Bulletin of the American Meteorological Society, Apr 2023. https://doi.org/10.1175/BAMS-D-22-0225.1 
  • Ver Hoef, L., Adams, H., King, E.J. and Ebert-Uphoff, I., 2023. A Primer on Topological Data Analysis to Support Image Analysis Tasks in Environmental Science. Artificial Intelligence for the Earth Systems, pp.1-38, Jan 2023.  ​​https://doi.org/10.1175/AIES-D-22-0039.1 
  • Haynes, K., Lagerquist, R., McGraw, M., Musgrave, K. and Ebert-Uphoff, I., 2023. Creating and evaluating uncertainty estimates with neural networks for environmental-science applications. Artificial Intelligence for the Earth Systems, pp.1-58, Jan 2023. https://doi.org/10.1175/AIES-D-22-0061.1 
  • Mamalakis, Antonios, Elizabeth A. Barnes, and Imme Ebert-Uphoff. “Carefully choose the baseline: Lessons learned from applying XAI attribution methods for regression tasks in geoscience.” Artificial Intelligence for the Earth Systems, 2, no. 1 (2023): e220058, Jan 2023.  https://doi.org/10.1175/AIES-D-22-0058.1 

2022:

  • Lagerquist, R. and Ebert-Uphoff, I., 2022. Can We Integrate Spatial Verification Methods into Neural Network Loss Functions for Atmospheric Science? Artificial Intelligence for the Earth Systems, 1(4), p.e220021, Nov 2022.  https://doi.org/10.1175/AIES-D-22-0021.1 
  • Mamalakis, A., Barnes, E.A. and Ebert-Uphoff, I., 2022. Investigating the fidelity of explainable artificial intelligence methods for applications of convolutional neural networks in geoscience. Artificial Intelligence for the Earth Systems, 1(4), p.e220012, Sept 2022. https://doi.org/10.1175/AIES-D-22-0012.1 
  • McGovern, A., Bostrom, A., Davis, P., Demuth, J.L., Ebert-Uphoff, I., He, R., Hickey, J., Gagne II, D.J., Snook, N., Stewart, J.Q. and Thorncroft, C., 2022. NSF AI institute for research on trustworthy AI in weather, climate, and coastal oceanography (AI2ES). Bulletin of the American Meteorological Society, 103(7), pp.E1658-E1668, July 2022.  https://doi.org/10.1175/BAMS-D-21-0020.1 
  • Rader, J.K., Barnes, E.A., Ebert-Uphoff, I. and Anderson, C., 2022. Detection of forced change within combined climate fields using explainable neural networks. Journal of Advances in Modeling Earth Systems, 14(7), p.e2021MS002941, June 2022.  https://doi.org/10.1029/2021MS002941 
  • Mamalakis, A., Ebert-Uphoff, I. and Barnes, E.A., 2022. Neural network attribution methods for problems in geoscience: A novel synthetic benchmark dataset. Environmental Data Science, 1, p.e8., June 2022. https://doi.org/10.1017/eds.2022.7 
  • Haynes, J.M., Noh, Y.J., Miller, S.D., Haynes, K.D., Ebert-Uphoff, I. and Heidinger, A., Low Cloud Detection in Multilayer Scenes using Satellite Imagery with Machine Learning Methods, Journal of Atmospheric and Oceanic Technology, March 2022, https://doi.org/10.1175/JTECH-D-21-0084.1.

2021:

  • Lagerquist, R., Turner, D., Ebert-Uphoff, I., Stewart, J. and Hagerty, V., Using Deep Learning to Emulate and Accelerate a Radiative Transfer Model. Journal of Atmospheric and Oceanic Technology, 38(10), pp.1673-1696, Oct 2021, https://doi.org/10.1175/MWR-D-21-0096.1.
  • Lagerquist, R., Stewart, J.Q., Ebert-Uphoff, I. and Kumler, C., Using Deep Learning to Nowcast the Spatial Coverage of Convection from Himawari-8 Satellite Data. Monthly Weather Review, 149(12), pp.3897-3921, Oct 2021, https://doi.org/10.1175/JTECH-D-21-0007.1.
  • Lee, Y., Kummerow, C. D., Ebert-Uphoff, I.  Applying machine learning methods to detect convection using GOES-16 ABI data, Atmospheric Measurement Techniques, April 2021, https://doi.org/10.5194/amt-2020-420.
  • Samarasinghe, S.M., Barnes, E.A., Connolly, C., Ebert-Uphoff, I., Sun, L.  Strengthened causal connections between the MJO and the North Atlantic with climate warming, Geophysical Research Letters, Feb 1 2021, https://doi.org/10.1029/2020GL091168.
    Featured as research highlight in Nature Climate Change: B. Langenbrunner, The Madden–Julian oscillation strengthens its reach. Nature Climate Change, 11, 183, 3 March 2021. https://doi.org/10.1038/s41558-021-01008-7
  • Hilburn, K. A., Ebert-Uphoff, I., and Miller, S. D., Development and Interpretation of a Neural Network-Based Synthetic Radar Reflectivity Estimator Using GOES-R Satellite Observations. Journal of Applied Meteorology and Climatology, https://doi.org/10.1175/JAMC-D-20-0084.1, Jan 2021. (arXiv preprint from April 2020: here)

2020:

2019:

2018 and before:

I. Ebert-Uphoff and G. Chirikjian, “Efficient Workspace Generation for Binary Manipulators with Many Actuators”, Journal of Robotics Systems, vol. 12, no. 6, pp. 383-400, 1995.

S. M. Samarasinghe, M. C. McGraw, E. A. Barnes, and I. Ebert-Uphoff, “A Study of Links Between the Arctic and the Midlatitude Jet-Stream Using Granger and Pearl Causality“, Environmetrics, pages e2540, 2018, https://doi.org/10.1002/env.2540.

A. Karpatne, I. Ebert-Uphoff, S. Ravela, H.A. Babaie and V. Kumar. “Machine Learning for the Geosciences: Challenges and Opportunities“, IEEE Transactions on Knowledge and Data Engineering, July 2018, DOI: 10.1109/TKDE.2018.2861006.  Available here.

I. Ebert-Uphoff and Y. Deng, ” Three Steps to Successful Collaboration with Data Scientists – A step-by-step cartoon guide to efficient, effective collaboration between Earth scientists and data scientists”, Eos Earth & Space Science News, vol. 98, Aug 2017, https://doi.org/10.1029/2017EO079977.  Available here.

A.H. Baker, D.M. Hammerling, S.A. Mickleson, H. Xu, M.B. Stolpe, P. Naveau, B. Sanderson, I. Ebert-Uphoff, S. Samarasinghe, F. De Simone, F. Carbone, C.N. Gencarelli, J.M. Dennis, J.E. Kay, and P. Lindstrom, “Evaluating Lossy Data Compression on Climate Simulation Data within a Large Ensemble“, Geoscentific Model Development, 9, pp. 4381-4403, 2016.  Get PDF here.

I. Ebert-Uphoff and Y. Deng, “Causal Discovery in the geosciences – Using synthetic data to learn how to interpret results“, Computers and Geosciences, Volume 99, February 2017, pp. 50–60, doi:10.1016/j.cageo.2016.10.008, 2016. 

I. Ebert-Uphoff and Y. Deng, “Identifying Physical Interactions from Climate Data: Challenges and Opportunities”, Computing in Science & Engineering, vol. 17, issue 6, pp.  27-34, 2015. DOI: 10.1109/MCSE.2015.129,

Y. Deng, I. Ebert‐Uphoff, “Weakening of Atmospheric Information Flow in a Warming Climate in the Community Climate System Model“, Geophysical Research Letters, 7 pages, doi: 10.1002/2013GL058646, Jan 2014.  Get PDF here.

I. Ebert-Uphoff, Y. Deng, “A New Type of Climate Network based on Probabilistic Graphical Models: Results of Boreal Winter versus Summer“, Geophysical Research Letters, vol. 39, L19701, 7 pages, doi:10.1029/2012GL053269, 2012.  Get PDF here.

I. Ebert-Uphoff, Y. Deng, “Causal Discovery for Climate Research Using Graphical Models“,  Journal of Climate, Vol. 25, No. 17, doi:10.1175/JCLI-D-11-00387.1, Sept 2012, pp. 5648-5665.  Get PDF here.

P. Bosscher, A. Riechel, I. Ebert-Uphoff, “Wrench-Feasible Workspace Generation for Cable-Driven Robots”, IEEE Transaction on Robotics, vol. 22, no. 5, October 2006, pp. 890-902.

K. Kozak, W. Singhose, I. Ebert-Uphoff, “Performance Measures for Input Shaping and Command Generation”, ASME Journal of Dynamic Systems, Measurement and Control, vol. 128, pp. 731-736, 2006.

P.A. Voglewede, I. Ebert-Uphoff, “Overarching Framework for Measuring Closeness to Singularities of Parallel Manipulators”, IEEE Transactions on Robotics, vol. 21, no. 6, Dec 2005, pp. 1037 – 1045.

P.A. Voglewede, I. Ebert-Uphoff, “Application of the Antipodal Grasp Theorem to Cable-Driven Robots”, IEEE Transactions on Robotics, vol. 21, no. 4, Aug 2005, pp. 713 – 718.

A.X.H. Dang, I. Ebert-Uphoff, “Active Acceleration Compensation for Transport Vehicles Carrying Delicate Objects”, IEEE Transactions on Robotics, Vol. 20, No. 5, Oct 2004, pp. 830 – 839.

P.A. Voglewede, I. Ebert-Uphoff, “Application of Workspace Generation Techniques to Determine the Unconstrained Motion of Parallel Manipulators”, ASME Journal of Mechanical Design, vol. 126, no. 2, March 2004, pp. 283-290.

K. Kozak, I. Ebert-Uphoff, W. Singhose, “Locally Linearized Dynamic Analysis of Parallel Manipulators and Application of Input Shaping to Reduce Vibration”, ASME Journal of Mechanical Design, vol. 126, no. 1, Jan. 2004, pp. 156-168.

I. Ebert-Uphoff, “Introducing Undergraduate Students to Parallel Manipulators Through Hands-on Experiments”, IEEE Robotics & Automation Magazine, special issue on “Robotics in Education”, Vol. 10, No. 3, pp. 13-19, Sept 2003.

I. Ebert-Uphoff, K. Johnson, “Practical Considerations for the Use of Static Balancing for Parallel Kinematic Machines”, IMechE Journal of Multi-body Dynamics, Special issue, Vol. 216, Part K, pp. 73-85, 2002.

I. Ebert-Uphoff, J.-K. Lee, H. Lipkin, “Characteristic Tetrahedron of Wrench Singularities for Parallel Manipulators with Three Legs”, IMechE Journal of Mechanical Engineering Science (Part C), Special issue on Spatial Mechamisms, Vol. 216, No. C1, pp. 81 – 93, 2002.

I. Ebert-Uphoff, J.F. Gardner, W.R. Murray, R. Perez, “Preparing for the Next Century: The State of Mechatronics Education”, IEEE/ASME Transactions on Mechatronics, Vol. 5, No. 2, pp. 226-227, June 2000.

I. Ebert-Uphoff, C.M. Gosselin, and T. Laliberté, “Static Balancing of Spatial Parallel Platform Mechanisms – Revisited”, ASME Journal of Mechanical Design, Vol. 122, No. 1, pp. 43-51, March 2000.

I. Ebert-Uphoff and G. Chirikjian, “Discretely Actuated Manipulator Workspace Generation by Closed-Form Convolution”, ASME Journal of Mechanical Design, pp. 245-251, Vol 120, June 1998.

G. Chirikjian and I. Ebert-Uphoff, “Numerical Convolution on the Euclidean Group with Applications to Workspace Generation”, IEEE Transactions on Robotics and Automation, pp. 123-136, Vol.14, No. 1, Feb. 1998.

A. Pamecha, I. Ebert-Uphoff and G. Chirikjian, “Useful Metrics for Modular Robot Motion Planning”, IEEE Transactions on Robotics and Automation, vol. 13, no. 4, pp. 531-545, August 1997.

G. Chirikjian, A. Pamecha and I. Ebert-Uphoff, “Evaluating Efficiency of Self-Reconfiguration in a Class of Modular Robots”, Journal of Robotics Systems, vol. 13, no. 5, pp. 317-38, 1996.

Non-Refereed Conference Publications and Presentations (only abstracts reviewed)

2025:
Oral presentations at 105th Annual Meeting of American Meteorological Society, New Orleans, Louisiana, Jan 12-16, 2025:

  • Imme Ebert-Uphoff, L. Ver Hoef, J. Schreck, J. Stock, M. J. Molina, A. McGovern, M. Yu, W. Petzke, K. A. Hilburn, D. M. Hall, D. J. Gagne II, W. F. Campbell, J. T. Radford, J. Q. Stewart, and S. Scheuerman, Measuring Sharpness of AI-Generated Meteorological Imagery, 24th Conference on Artificial Intelligence for Environmental Science, Jan 13, 2025.
  • Alexandra Mazurek, R. Lagerquist, R. S. Schumacher, I. Ebert-Uphoff, and J. T. Radford, A Study of Derived Convective Parameters from Deep Learning Weather Prediction Models, 24th Conference on Artificial Intelligence for Environmental Science, Jan 13, 2025.
  • Evan Andrew Krell, A. Mamalakis, S. A. King, P. E. Tissot, and I. Ebert-Uphoff, Hierarchical Superpixel Explanations Improve XAI Attribution Methods for Geospatial Models, 24th Conference on Artificial Intelligence for Environmental Science, Jan 14, 2025. 
  • Ryan A. Lagerquist, D. D. Turner, J. Q. Stewart, and I. Ebert-Uphoff, Shedding new light on radiative transfer: Deep learning for multispectral emulation of the RRTM, 24th Conference on Artificial Intelligence for Environmental Science, Jan 14, 2025.
  • Jacob T. Radford, I. Ebert-Uphoff and J. Q. Stewart, A Comparison of Pure AI and Physics-Based Weather Prediction Precipitation Forecasts, 24th Conference on Artificial Intelligence for Environmental Science, Jan 14, 2025.
  • Nathan Jeffrey Mitchell, L. Ver Hoef, K. Moen, K. A. Hilburn, Y. Lee, E. J. King, and I. Ebert-Uphoff, An Interpretable, Satellite-Based, Machine Learning Convection Detection Algorithm Created by an Explainable Boosting Machine and Refined using Domain Knowledge, 24th Conference on Artificial Intelligence for Environmental Science, Jan 15, 2025.
  • Marie McGraw, K. Haynes, K. Musgrave, I. Ebert-Uphoff, C. Slocum, and J. Knaff, Using AI-based Synthetic Passive Microwave Data to Increase Understanding of Tropical Cyclone Structure and Evolution, 24th Conference on Artificial Intelligence for Environmental Science, Jan 15, 2025.
  • Jacob T. Radford, J. Q. Stewart and I. Ebert-Uphoff, Powering AI-based Weather Prediction (Invited Presentation), 24th Conference on Artificial Intelligence for Environmental Science, Jan 16, 2025.
  • Katherine Haynes, C. Slocum, J. Knaff, K. Musgrave, R. Chase, M. N. Razin, and I. Ebert-Uphoff, Creating Synthetic Passive Microwave Observations Using Deep Learning to Aid Tropical Cyclone Forecasting, Seventh Special Symposium on Tropical Meteorology and Tropical Cyclones, Jan 13, 2025.
  • Matthew P. King, J. Apke, S. D. Miller, I. Ebert-Uphoff, R. Chase, J. M. Haynes, and Y. J. Noh, Combining Optical Flow and ML Methods for Satellite-based Cloud Nowcasting, 21st Annual Symposium on Operational Environmental Satellite Systems, Jan 15, 2025.
  • Charles White, Y. J. Noh, J. M. Haynes, and I. Ebert-Uphoff, Emulating Daytime Cloud Optical Properties at Night with Machine Learning, 21st Annual Symposium on Operational Environmental Satellite Systems, Jan 15, 2025.
  • Kate D. Musgrave, M. DeMaria, J. Franklin, G. Chirokova, J. T. Radford, R. DeMaria, I. Ebert-Uphoff, and A. Brammer, Evaluation of Tropical Cyclone Forecasts from Artificial Intelligence Weather Prediction (AIWP) Models, 15th Conference On Transition Of Research To Operations (15R2O). Jan 16, 2025. 
  • Galina Chirokova, G. Foltz, M. DeMaria, J. Kaplan, I. Ebert-Uphoff, D. Molenar, J. Darlow, and W. A. Hogsett, Improving Operational Statistical-Dynamical Tropical Cyclone Intensity Guidance with a New Formulation of Ocean-TC interaction and Machine Learning, 15th Conference On Transition Of Research To Operations (15R2O), Jan 15, 2025.

2024:

  • Imme Ebert-Uphoff, J. Q. Stewart, K. A. Hilburn, J. T. Radford, R. T. DeMaria, R. Chase, R. A. Lagerquist, C. White, Y. Lee, J. Apke, K. D. Musgrave, L. Ver Hoef, C. E. Kumler, M. S. Wandishin, J. Duda, I. Jankov, and D. D. Turner,  A Research Agenda for the Evaluation of AI-Based Weather Forecasting Models (Core Science Keynote), 104th AMS Annual Meeting, Baltimore, MD, Jan 2024.
  • Charles White, I. Ebert-Uphoff, J. M. Haynes, and Y. J. Noh, Super-Resolution of GOES-16 ABI Channels to a Common High Resolution with a Convolutional Neural Network, 104th AMS Annual Meeting, Baltimore, MD, Jan 2024.
  • Charles White, Y. J. Noh, J. M. Haynes, and I. Ebert-Uphoff, Neural Network Estimates of Cloud Water Content Profiles from Passive Satellite Imagery, 104th AMS Annual Meeting, Baltimore, MD, Jan 2024.
  • Jacob T. Radford, I. Ebert-Uphoff, J. Q. Stewart, R. T. DeMaria, T. Wilson, J. L. Demuth, M. S. Wandishin, J. Duda, A. McGovern, C. D. Wirz, and M. G. Cains, Visualizing Data-Driven AI Models to Engage Operational Forecasters, 104th AMS Annual Meeting, Baltimore, MD, Jan 2024.
  • Robert T. DeMaria, M. DeMaria, G. Chirokova, K. Musgrave, J. T. Radford, and I. Ebert-Uphoff, Evaluation of Tropical Cyclone Track and Intensity Forecasts from Purely ML-based Weather Prediction Models, Illustrated with FourCastNet, 104th AMS Annual Meeting, Baltimore, MD, Jan 2024.
  • Ryan A. Lagerquist, D. D. Turner, J. Q. Stewart, and I. Ebert-Uphoff, Machine-Learned Uncertainty Quantification Is Not Magic: Lessons Learned from Emulating Radiative Transfer with ML, 104th AMS Annual Meeting, Baltimore, MD, Jan 2024.
  • Ryan A. Lagerquist, J. Knaff, C. Slocum, K. Musgrave, and I. Ebert-Uphoff, Identifying Data Sources and Physical Strategies Used By Neural Networks to Predict TC Rapid Intensification, 104th AMS Annual Meeting, Baltimore, MD, Jan 2024.
  • Ryan A. Lagerquist, G. Chirokova, M. DeMaria, R. DeMaria, I. Ebert-Uphoff, J. Knaff, and C. Slocum, Geocenter: A New Deep-Learning Ensemble for Determining Tropical-Cyclone Center Locations from Geostationary Satellite Data, 104th AMS Annual Meeting, Baltimore, MD, Jan 2024.
  • Kristina Moen, N. J. Mitchell, Y. Lee, L. Ver Hoef, E. J. King, I. Ebert-Uphoff, K. A. Hilburn, and W. Line, Exploring Texture Analysis to Aid Classification of Meteorological Phenomena in Satellite Imagery, 104th AMS Annual Meeting, Baltimore, MD, Jan 2024.
  • Marie McGraw, 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, 104th AMS Annual Meeting, Baltimore, MD, Jan 2024.
  • Galina Chirokova, R. T. DeMaria, A. Brammer, I. Ebert-Uphoff, M. DeMaria, S. N. Stevenson, J. Knaff, S. D. Miller, B. Zeschke, and R. Mazur, ProxyVis Geostationary Satellite Imagery: Current product Status and Future Development, 104th AMS Annual Meeting, Baltimore, MD, Jan 2024.
  • Maria J. Molina, K. Dagon, J. Schreck, J. S. Perez-Carrasquilla, K. J. Mayer, N. Sobhani, D. J. Gagne II, Ph.D., I. Ebert-Uphoff, C. Metzler, and G. A. Meehl, When Machine Learning Objectives Compete for Improved Subseasonal Bias Correction, Who Wins?, 104th AMS Annual Meeting, Baltimore, MD, Jan 2024.
  • Matthew P. King, J. Apke, S. D. Miller, I. Ebert-Uphoff, R. Chase, J. M. Haynes, and Y. J. Noh, Advancing 3D Cloud Nowcasting: Comparative Analysis of Advection Methods and Machine Learning, 104th AMS Annual Meeting, Baltimore, MD, Jan 2024.

2023:

  1. Bansal, A.S., Hilburn, K. and Ebert-Uphoff, I., 2023, January. Artificial Intelligence for Low-Level Moisture from GOES-R Series. In 103rd AMS Annual Meeting. AMS.
  2. Bansal, A.S., Lee, Y., Hilburn, K. and Ebert-Uphoff, I., 2023, January. A Primer on Neural Network Architectures to Extract Information from Meteorological Image Sequences. In 103rd AMS Annual Meeting. AMS.
  3. Cains, M.G., Wirz, C.D., Demuth, J.L., Bostrom, A., McGovern, A., Ebert-Uphoff, I., Gagne, D.J., Burke, A. and Sobash, R.A., 2023, January. Exploring what AI/ML guidance features NWS forecasters deem trustworthy. In 103rd AMS Annual Meeting. AMS.
  4. Haynes, K., Stock, J., Dostalek, J., Grasso, L., Anderson, C. and Ebert-Uphoff, I., 2023, January. Exploring the Use of Machine Learning to Improve Vertical Profiles of Temperature and Moisture. In 103rd AMS Annual Meeting. AMS.
  5. Haynes, K., Lagerquist, R., McGraw, M., Musgrave, K. and Ebert-Uphoff, I., 2023. Creating and evaluating uncertainty estimates with neural networks for environmental-science applications. In 103rd AMS Annual Meeting. AMS.
  6. Mamalakis, A., Barnes, E.A. and Ebert-Uphoff, I., 2023, January. Explainable Artificial Intelligence for Environmental Science: The choice of baseline matters. In 103rd AMS Annual Meeting. AMS.
  7. McGovern, A., Bostrom, A., Gagne, D.J., Ebert-Uphoff, I., Musgrave, K., McGraw, M. and Chase, R., 2023, January. Classifying and Addressing Bias in AI/ML for the Earth Sciences. In 103rd AMS Annual Meeting. AMS.
  8. Ver Hoef, L., Gagne, D.J., King, E.J. and Ebert-Uphoff, I., 2023, January. Comparing Rotationally Invariant CNNs with Classical CNNs on Storm Forecast Data. In 103rd AMS Annual Meeting. AMS.
  9. Lander Ver Hoef, H. Adams, E. J. King, and I. Ebert-Uphoff, A Primer on Topological Data Analysis to Support Image Analysis Tasks in Environmental Science. In 103rd AMS Annual Meeting. AMS.
  10. Marie McGraw, K. Musgrave, I. Ebert-Uphoff, J. Knaff, and C. Slocum, What Can Machine Learning Methods Tell Us About the Tropical Cyclone Intensity Forecasting Problem? In 103rd AMS Annual Meeting. AMS.
  11. Ryan A. Lagerquist, D. D. Turner, J. Q. Stewart, I. Ebert-Uphoff, and N. Wang, How Complex Must Neural Networks be to Accurately Estimate Radiative Transfer in Different Situations? In 103rd AMS Annual Meeting. AMS.
  12. McGovern, A., Gagne, D.J., Ebert-Uphoff, I., Bostrom, A., Wirz, C.D., Chase, R., Fagg, A.H. and Barnes, E.A., 2023, January. Creating Personalized Learning Journeys for All Levels of Learning in AI with Applications to Weather and Climate. In 103rd AMS Annual Meeting. AMS.
  13. Charles White, J. M. Haynes, Y. J. Noh, I. Ebert-Uphoff, and S. D. Miller, Estimation of Vertical Cloud Water Content Profiles from VIIRS Imagery using Neural Networks. In 103rd AMS Annual Meeting. AMS.
  14. Galina Chirokova, G. Foltz, J. Kaplan, D. Molenar, I. Ebert-Uphoff, M. DeMaria, A. Brammer, S. N. Stevenson, W. Hogsett, and J. Darlow, Improving NHC’s Operational Intensity Guidance Suite and Situational Awareness with Better Metrics of Ocean-TC interaction. In 103rd AMS Annual Meeting. AMS.

2022:

  1. Mamalakis, A., Barnes, E.A. and Ebert-Uphoff, I., 2022, December. Using different XAI baselines to answer different science questions. In Fall Meeting 2022. AGU.
  2. Haynes, K., Slocum, C., Knaff, J., Musgrave, K. and Ebert-Uphoff, I., 2022, May. Aiding Tropical Cyclone Forecasting by Simulating 89-GHz Imagery from Operational Geostationary Satellites. In 35th Conference on Hurricanes and Tropical Meteorology. AMS.
  3. Lagerquist, R., D. Turner, I. Ebert-Uphoff, and J. Stewart, 2022: “Using deep learning to create a faster radiative-transfer scheme for use in the Global Forecast System”. Conference on Atmospheric Radiation, Madison, Wisconsin, AMS.
  4. Lagerquist, R., and I. Ebert-Uphoff, 2022: “Nowcasting convection from satellite data with deep learning and uncertainty quantification”. Conference on Satellite Meteorology, Oceanography, and Climatology, Madison, Wisconsin, AMS.
  5. J. Rader, E. Barnes, I. Ebert-Uphoff, and C. Anderson, Detecting Forced Change within Combined Climate Fields Using a Neural Network, AMS 102st Annual Meeting, 21th Conference on Artificial Intelligence for Environmental Science, Jan 23-27, 2022.
  6. Lagerquist, R. and Ebert-Uphoff, I., 2022, January. Exploring the Benefits of Integrating Fourier and Wavelet Transforms into Neural Networks for Meteorological Applications. In 102nd American Meteorological Society Annual Meeting. AMS.
  7. Ebert-Uphoff, I., Lagerquist, R., Hilburn, K.A., Lee, Y., Haynes, K., Stock, J., Kumler, C. and Stewart, J.Q., 2022, January. How to Develop Custom Loss Functions for Neural Networks in Meteorology. In 102nd American Meteorological Society Annual Meeting. AMS.
  8. Mamalakis, A., Ebert-Uphoff, I. and Barnes, E., 2022, January. Explainable Artificial Intelligence for Environmental Science: Introducing Objectivity into the Assessment of Neural Network Attribution Methods. In 102nd American Meteorological Society Annual Meeting. AMS.
  9. Lagerquist, R., Ebert-Uphoff, I., Stewart, J.Q. and Kumler, C., 2022, January. Nowcasting Convection with Deep Learning and Custom Spatially Aware Loss Functions. In 102nd American Meteorological Society Annual Meeting. AMS.
  10. Haynes, K., Slocum, C., Knaff, J.A., Musgrave, K. and Ebert-Uphoff, I., 2022, January. Simulating 89-GHz Imagery from Operational Geostationary Satellites Using Machine Learning. In 102nd American Meteorological Society Annual Meeting. AMS.
  11. McGovern, A., Ebert-Uphoff, I., Bostrom, A. and Gagne, D.J., 2022, January. Ethical and Responsible AI and Trust for Weather and Climate. In 102nd American Meteorological Society Annual Meeting. AMS.
  12. Ryan A. Lagerquist, D. D. Turner, I. Ebert-Uphoff, J. Q. Stewart, and V. Hagerty, Grid-Agnostic Deep Learning for Parameterizing Radiative Transfer. . In 102nd AMS Annual Meeting. AMS.
  13. Mariana Goodall Cains, C. D. Wirz, J. L. Demuth, A. Bostrom, A. McGovern, I. Ebert-Uphoff, D. J. Gagne II, A. Burke, and R. A. Sobash, NWS Forecasters’ Perceptions and Potential Uses of Trustworthy AI/ML for Hazardous Weather Risks. . In 102nd AMS Annual Meeting. AMS.

2021:

  • Ryan Lagerquist, J. Stewart, C. Kumler, and I. Ebert-Uphoff, Deep Learning for Short-Term Forecasting of Convective Initiation and Decay over Taiwan, AMS 101st Annual Meeting, 20th Conference on Artificial Intelligence for Environmental Science, Jan 10-15, 2021.
  • Ryan Lagerquist, D. D. Turner, I. Ebert-Uphoff, V. Hagerty, C. Kumler, and J. Stewart, Deep Learning for Parameterization of Shortwave Radiative Transfer, AMS 101st Annual Meeting, 20th Conference on Artificial Intelligence for Environmental Science, Jan 10-15, 2021.
  • Lander Ver Hoef, Y. Lee, H. Adams, E. King, and I. Ebert-Uphoff, Topological Data Analysis for Identifying Convection in GOES-R Imagery, AMS 101st Annual Meeting, 20th Conference on Artificial Intelligence for Environmental Science, Jan 10-15, 2021.
  • Jason Stock, J. Dandy, I. Ebert-Uphoff, C. Anderson, J. Dostalek, L. Grasso, J. Zeitler, and H. Weinman, Using Machine Learning to Improve Vertical Profiles of Temperature and Moisture for Severe Weather Nowcasting, AMS 101st Annual Meeting, 20th Conference on Artificial Intelligence for Environmental Science, Jan 10-15, 2021.

2020:

  • Christian Kummerow, Imme Ebert-Uphoff. Satellite Precipitation Algorithms and AI, AGU Fall Meeting, Dec 7-11, 2020.
  • Savini M. Samarasinghe, Elizabeth A. Barnes, Charlotte Connolly, Imme Ebert-Uphoff, Lantao Sun.  Strengthening Causal Connections Between the MJO and the North Atlantic in Future Climate Projections, AGU Fall Meeting, Dec 7-11, 2020.
  • Elizabeth A. Barnes, Kirsten J. Mayer, Jamin Rader, Benjamin A. Toms, Imme Ebert-Uphoff, “Leveraging Interpretable Neural Networks for Scientific Discovery”, AGU Fall Meeting, Dec 7-11, 2020.
  • I. Ebert-Uphoff and Kyle Hilburn, On the Interpretation of Neural Networks Trained for Meteorological Applications, ECMWF-ESA Workshop on Machine Learning for Earth System Observation and Prediction, Oct 2020.
  • I. Ebert-Uphoff and Kyle Hilburn, Which strategies did my neural network learn? The 2nd NOAA Workshop on Leveraging AI in Environmental Sciences, Sept 17, 2000, link to recording.
  • Amy McGovern, Ryan Lagerquist, Elizabeth Barnes, Imme Ebert-Uphoff. The Importance of Neural Network Interpretation Techniques for Climate and Weather Science. Workshop “Data Science in Climate and Climate Impact Research” – Conceptual Issues, Challenges, and Opportunities, ETH Zürich, Aug 20-21, 2020.
  • Ebert-Uphoff, I., Hilburn, K., Toms, B. A., & Barnes, E. A.  Selected Methods from Explainable AI to Improve Understanding of Neural Network Reasoning for Environmental Science Applications. In 100th American Meteorological Society Annual Meeting. AMS, Jan 2020. (Abstract and recording of oral presentation)
  • Barnes, E. A., Ebert-Uphoff, I., Hurrell, J., Anderson, C. W., & Anderson, D. Viewing Climate Signals through an AI Lens (Core Science Keynote). In 100th American Meteorological Society Annual Meeting. AMS, Jan 2020.  (Abstract and recording of oral presentation.)
  • McGovern, Amy, Jason Hickey, David Hall, Imme Ebert-Uphoff, Christopher Thorncroft, John Williams, Robert J. Trapp, Ruoying He, and Carla Bromberg. “AI2ES: Alpha-Institute: Artificial Intelligence for Environmental Sciences.” In 100th American Meteorological Society Annual Meeting. AMS, Jan. 2020. (Abstract and recording of oral presentation)
  • Toms, B. A., Barnes, E. A., & Ebert-Uphoff, I. Using Physically Interpretable Neural Networks to Discover Modes of Climate and Weather Variability. In 100th American Meteorological Society Annual Meeting. AMS, Jan 2020. (Abstract and recording of oral presentation.)

Before 2020:

I. Ebert-Uphoff, J.-K. Lee, H. Lipkin, “Characteristic Tetrahedron of Wrench Singularities for Parallel Manipulators with Three Legs”, presented at Ball 2000 Symposium, Cambridge, GB, July 2000.

Ebert-Uphoff, I., Hilburn, K. A., Toms, B. A., & Barnes, E. A. (2019). Opening the “Black Box”: Tools to Improve Understanding of Neural Network Reasoning for Geoscience Applications. Poster presentation, AGU Fall meeting, Dec 2019, Poster A51U-2666. (Abstract)

Samarasinghe, S. M., Barnes, E. A., Ebert-Uphoff, I., & Sun, L. (2019). Using Causal Discovery Methods to Explore Subseasonal Teleconnections in a Changing Climate. Poster presentation, AGU Fall Meeting, Dec 2019, Poster A51U-2670. (Abstract)

I. Ebert-Uphoff, “Building bridges between domain scientists and machine learning experts: The essential role of weather/climate scientists in machine learning collaborations”, 1st Workshop on Leveraging AI in the Exploitation of Satellite Earth Observations & Numerical Weather Prediction, NOAA STAR Center for Satellite Applications and Research, April 23-25, 2019.

D.D. Pennington, I. Ebert-Uphoff, N. Freed, J. Martin, and S.A. Pierce, “Interdisciplinary Earth Data Science Education”, Oral presentation, AGU Fall meeting, Washington, D.C., Dec 14, 2018.  Get abstract.

Y. Deng, I. Ebert-Uphoff, “Characterizing Sub-Seasonal Coupling between Surface Temperature and Top-of-Atmosphere Radiative Imbalance through Graphs of 3D Information Flow”, Poster presentation,  29th Conference on Climate Variability and Change, San Francisco, CA, Jan 2017.

I. Ebert-Uphoff, D. Hammerling, S. Samarasinghe, A. Baker, “Applying Causal Discovery to the Output of Climate Models – What Can We Learn from the Causal Signatures?”  Oral presentation, AGU Fall meeting, San Francisco, CA, Dec 14-18, 2015.

Y. Deng, I. Ebert-Uphoff, “Characterizing and Understanding Large-Scale Wave Propagation in the Atmosphere through Graphs of 3D Information Flow”.  Poster presentation, AGU Fall meeting, San Francisco, CA, Dec 14-18, 2015.

Christian Rodriguez, Imme Ebert-Uphoff, Yi Deng, “Using causal discovery to study connections between TOA radiative flux and surface temperature”. Poster presentation, The Fifth International Workshop on Climate Informatics (CI2015), NCAR, Boulder, CO, Sept 24-25, 2015.  Get PDF here.

Dorit Hammerling, Allison H. Baker, Imme Ebert-Uphoff, “What can we learn about climate model runs from the causal signatures?”  Poster presentation, The Fifth International Workshop on Climate Informatics (CI2015), NCAR, Boulder, CO, Sept 24-25, 2015.  Get PDF here.

Charles Anderson, Imme Ebert-Uphoff, Yi Deng, Melinda Ryan, “Discovering spatial and temporal patterns in climate data using deep learning”. Poster presentation, The Fifth International Workshop on Climate Informatics (CI2015), NCAR, Boulder, CO, Sept 24-25, 2015.  Get PDF here.

I. Ebert-Uphoff, Y. Deng, “Using Causal Discovery to Learn about our Planet’s Climate – Recent Progress”, The Fourth International Workshop on Climate Informatics (CI2014), NCAR, Boulder, CO, September 25 – 26, 2014, 2 pages.  Get PDF here.

I. Ebert-Uphoff, Y. Deng, “Causal Discovery in Climate Science Using Graphical Models”, The Third International Workshop on Climate Informatics (CI2013), NCAR, Boulder, CO, September 26 – 27, 2013, 3 pages.  Get PDF here.

I. Ebert-Uphoff, Y. Deng, “A new type of climate network based on causal discovery methods”, Frontiers in Computational Physics: Modeling the Earth System, Dec 16 – 20, 2012, Boulder, CO, USA.

A.T. Riechel, P. Bosscher, H. Lipkin, I. Ebert-Uphoff, “Concept Paper: Cable-Driven Robots for Use in Hazardous Environments”, 10th International Conference on Robotics & Remote Systems for Hazardous Environments, Florida, March 2004.

Jarek Rossignac, Mark Allen, Wayne J. Book, Ari Glezer, Imme Ebert-Uphoff, Chris Shaw, David Rosen, Stephen Askins, Jing Bai, Paul Bosscher, Joshua Gargus, ByungMoon Kim, Ignacio Llamas, Austina Nguyen, Guang Yuan, Haihong Zhu, “Finger Sculpting with Digital Clay: 3D Shape Input and Output through a Computer-Controlled Real Surface”, Proceedings of the Shape Modeling International Conference, Seoul, Korea, May 12-16, 2003.

Refereed Conference Papers

Most of the conferences in earth science only review abstracts. Please see the list of non-refereed (= only abstracts reviewed) conference presentations above.

E.A. Barnes, C. Anderson and I. Ebert-Uphoff, “An AI Approach To Determining Time of Emergence of Climate Change”, Proceedings of the Eighth International Workshop on Climate Informatics (CI 2018), 4 pages, Sept 2018. Get PDF here.

I. Ebert-Uphoff, W. Huang, A. Mitra, D. Cooley, S. Chatterjee, C. Chen and Z. Wang, “Studying Extremal Dependence in Climate Using Complex Networks”, Proceedings of the Eighth International Workshop on Climate Informatics (CI 2018), 4 pages, Sept 2018.  Get PDF here.

S. Samarasinghe, E. Barnes and I. Ebert-Uphoff, “Causal discovery in the presence of latent variables for climate science”, Proceedings of the Eighth International Workshop on Climate Informatics (CI 2018), 4 pages, Sept 2018.  Get PDF here.

D. Hammerling, I. Ebert-Uphoff and A. Baker, “Ensemble Consistency Testing Using Causal Connectivity”, Proceedings of the Eighth International Workshop on Climate Informatics (CI 2018), 4 pages, Sept 2018.  Get PDF here.

J. Ramsey, K. Zhang, M. Glymour, R. Sanchez Romero, B. Huang, I. Ebert-Uphoff, S. Samarasinghe, E. Barnes and C. Glymour, “A Toolbox for Causal Discovery”, Proceedings of the Eighth International Workshop on Climate Informatics (CI 2018), 4 pages, Sept 2018.  Get PDF here.

J. Golmohammadi, I. Ebert-Uphoff, S. He, Y. Deng, A. Banerjee, “High-Dimensional Dependency Structure Learning for Physical Processes”, IEEE International Conference on Data Mining (ICDM), New Orleans,  Louisiana, USA, pp. 883-888, Nov 18-21, 2017, doi:10.1109/ICDM.2017.109.  Available here.

S. Samarasinghe, M. McGraw, E.A. Barnes, I. Ebert-Uphoff, “A Study of Causal Links Between the Arctic and the Midlatitude Jet-Streams”, Proceedings of the Seventh International Workshop on Climate Informatics (CI 2017), NCAR Technical Note NCAR/TN-536+PROC, Sept 2017.  Get PDF here.

I. Ebert-Uphoff, D.R. Thompson, I. Demir, Y.R. Gel, M.C. Hill, A. Karpatne, M. Guereque, V. Kumar, E. Cabral-Cano, P. Smyth, “A Vision for the Development of Benchmarks to Bridge Geoscience and Data Science”, Proceedings of the Seventh International Workshop on Climate Informatics (CI 2017), NCAR Technical Note NCAR/TN-536+PROC, Sept 2017. Get PDF here.

S. Samarasinghe, Y. Deng and I. Ebert-Uphoff, “Structure Learning in Spectral Space with Applications in Climate Science”, Workshop on Mining Big Data in Climate and Environment (MBDCE 2017), 17th SIAM International Conference on Data Mining (SDM 2017), April 27 – 29, 2017, Houston, Texas, USA, 5 pages, 2017.  Get PDF here.

Anuj Karpatne, Hassan Ali Babaie, Sai Ravela, Vipin Kumar, Imme Ebert-Uphoff, “Machine Learning for the Geosciences – Opportunities, Challenges, and Implications for the ML process”, Workshop on Mining Big Data in Climate and Environment (MBDCE 2017), 17th SIAM International Conference on Data Mining (SDM 2017), April 27 – 29, 2017, Houston, Texas, USA, 10 pages, 2017.  Get PDF here.

I. Ebert-Uphoff and Y. Gil, “Exploring Synergies between Machine Learning and Knowledge Representation to Capture Scientific Knowledge”, First International Workshop on Capturing Scientific Knowledge, Eighth International Conference on Knowledge Capture (K-CAP), 9 pages, Palisades, NY, Oct 2015.  Get PDF here.

I. Ebert-Uphoff, Y. Deng, “Causal Discovery from Spatio-Temporal Data with Applications to Climate Science”, 13th International Conference on Machine Learning and Applications (ICMLA’14), Detroit, MI, USA, Dec 3-6, 2014, 8 pages.  Get PDF here.

S. Wolff, I. Ebert-Uphoff, “Preliminary Results on Generating Assembly Sequences for Shape Display”, Proceedings of the ASME International 26th Computers and Information in Engineering Conference (CIE), Philadelphia, PA, Sept 10-13, 2006, paper number DETC2006-99233.

P. Bosscher, I. Ebert-Uphoff, “Disturbance Robustness Mesasures for Underconstrained Cable-Driven Robots”, 2006 IEEE International Conference on Robotics and Automation, Orlando, FL, May 15-19, 2006, pp. 4205-4212.

I. Ebert-Uphoff, P.A. Voglewede, “On the Connections between Cable-Driven Robots, Parallel Manipulators and Grasping”, 2004 IEEE International Conference on Robotics and Automation, Vol. 5, pp. 4521-4526, New Orleans, LA, April 26 – May 1, 2004.

P.A. Voglewede, I. Ebert-Uphoff, “Measuring ”Closeness” to Singularities for Parallel Manipulators”, 2004 IEEE International Conference on Robotics and Automation, Vol. 5, pp. 4539-4544, New Orleans, LA, April 26 – May 1, 2004.

P. Bosscher, I. Ebert-Uphoff, “A Stability Measure for Underconstrained Cable-Driven Robots”, 2004 IEEE International Conference on Robotics and Automation, Vol. 5, pp. 4943-4949, New Orleans, LA, April 26 – May 1, 2004.

P. Bosscher, I. Ebert-Uphoff, “Wrench-Based Analysis of Cable-Driven Robots”, 2004 IEEE Interna- tional Conference on Robotics and Automation, Vol. 5, pp. 4950-4955, New Orleans, LA, April 26 – May 1, 2004.

A.T. Riechel, I. Ebert-Uphoff, “Force-Feasible Workspace Analysis for Underconstrained, Point-Mass Cable Robots”, 2004 IEEE International Conference on Robotics and Automation, Vol. 5, pp. 4956- 4962, New Orleans, LA, April 26 – May 1, 2004.

P. Bosscher, I. Ebert-Uphoff, “Digital Clay: Architecture Designs for Shape-Generating Mechanisms”, 2003 IEEE International Conference on Robotics and Automation, vol. 1, 2003, pp. 834-841.

P. Bosscher, I. Ebert-Uphoff, “A Novel Mechanism for Implementing Multiple Collocated Spherical Joints”, 2003 IEEE International Conference on Robotics and Automation, vol. 1, 2003, pp. 336-341.

K. Kozak, I. Ebert-Uphoff, P.A. Volgewede, W. Singhose, “Concept Paper: On the Significance of the Lowest Natural Frequency of a Parallel Manipulator as a Performance Measure for Concurrent Design”, Proceedings of the Workshop on Fundamental Issues and Future Research Directions for Parallel Mechanisms and Manipulators, Qu ́ebec City, QC, Canada, Oct 2002, pp. 112-118.

K. Kozak, I. Ebert-Uphoff, “Review of the Role of Quasi-Coordinates for the Kinematic and Dynamic Modeling of Parallel Manipulators”, Proceedings of the Workshop on Fundamental Issues and Future Research Directions for Parallel Mechanisms and Manipulators, Québec City, QC, Canada, Oct 2002, pp. 328-338.

P.A. Voglewede, I. Ebert-Uphoff, “Two Viewpoints on the Unconstrained Motion of Parallel Manipulators at or near Singular Configurations”, IEEE International Conference on Robotics and Automation, Washington, D.C., pp. 503 – 510, May, 2002.

K. Kozak, I. Ebert-Uphoff, W. Singhose, “Analysis of Varying Natural Frequencies and Damping Ratios of a Sample Parallel Manipulator Throughout Its Workspace Using Linearized Equations of Motion”, Symposium on Dynamics and Vibration of Robotic Systems, Proceedings of the ASME Design Engineering Technical Conferences, Pittsburgh, PA, Paper # DETC2001/VIB-21529, Sept 2001.

V.K. Chan, I. Ebert-Uphoff, “Investigation of the Deficiencies of Parallel Manipulators in Singular Configurations Through the Jacobian Nullspace”, IEEE International Conference on Robotics and Automation, vol. 2, pp. 1313 – 1320, Seoul, Korea, May 2001.

M. Decker, A. Dang, I. Ebert-Uphoff, “Motion Planning for Active Acceleration Compensation”, IEEE International Conference on Robotics and Automation, vol. 2, pp. 1257 – 1264, Seoul, Korea, May 2001.

K. Johnson, I. Ebert-Uphoff, ”Development of a Spatial Statically-Balanced Parallel Platform Mechanism”, Proceedings of the Year 2000 Parallel Kinematic Machines International Conference and Second European-American PKM Forum, Ann Arbor, MI, pp. 143-159, Sept 2000.

B. Geving, I. Ebert-Uphoff, ”Development of Technology to Support the Construction of Robotic Mechanisms in SLA Machines”. Part of a “Special Session on Rapid Prototyping of Mechanisms and Robotic Systems” at the 26th ASME Biennial Mechanisms Conference, Paper Number DETC00/MECH-14207, Baltimore, MD, Sept 2000.

B. Geving, A. Kataria, C. Moore, I. Ebert-Uphoff, T. Kurfess, D. Rosen, “Conceptual Design of a Generalized Stereolithography Machine”, ‘2000 Japan-USA Symposium on Flexible Automation’, paper # 2000JUSFA-13172, Ann Arbor, MI, July 23-26, 2000.

C.M. Gosselin, J. Wang, T. Laliberté, I. Ebert-Uphoff, “On the Design of a Statically Balanced 6- DOF Parallel Manipulator”, the Tenth World Congress on the Theory of Machines and Mechanisms, Oulu, Finland, pp. 1045-1050, June 1999.

I. Ebert-Uphoff, C.M. Gosselin, “Dynamic Modeling of a Class of Spatial Statically-Balanced Parallel Platform Mechanisms”, 1999 IEEE International Conference on Robotics and Automation, vol. 2, pp. 881–888, Detroit, MI, May 1999.

I. Ebert-Uphoff, C.M. Gosselin, and T. Laliberté, “Static Balancing of a Class of Spatial Parallel Platform Mechanisms”, 1998 ASME Design Engineering Technical Conferences, DETC/MECH-5964, Atlanta, GA, Sept 1998.

I. Ebert-Uphoff, C.M. Gosselin, “Kinematic Study of a new Type of Spatial Parallel Platform Mechanism”, 1998 ASME Design Engineering Technical Conferences, DETC/MECH-5962, Atlanta, GA, Sept 1998.

G. Chirikjian and I. Ebert-Uphoff, “Discretely Actuated Manipulator Workspace Generation Using Numerical Convolution on the Euclidean Group”, vol. 1, pp. 742-749, IEEE Conference on Robotics and Automation, Leuven, Belgium, May 1998.

I. Ebert-Uphoff and G. Chirikjian, “Discretely Actuated Manipulator Workspace Generation by Closed-Form Convolution”, ASME Mechanisms Conference, 96-DETC/MECH-1162, Irvine, CA, Au- gust 1996.

I. Ebert-Uphoff and G. Chirikjian, “Inverse Kinematics of Discretely Actuated Hyper- Redundant Manipulators Using Workspace Densities”, IEEE Conference on Robotics and Automation, pp. 139-145, Minneapolis, Minnesota, April 1996.

I. Ebert-Uphoff and G. Chirikjian, “Generation of Binary Manipulator Workspaces and Work Envelopes”, Proceedings of the Third IASTED International Conference on Robotics and Manufacturing, pp. 14-20, Cancun, Mexico, June 1995.

Recent and Upcoming Invited Talks

Jacob T. Radford, J. Q. Stewart and I. Ebert-Uphoff, Powering AI-based Weather Prediction (Invited Presentation), 24th Conference on Artificial Intelligence for Environmental Science, 105th Annual Meeting of American Meteorological Society, Jan 16, 2025.

Participant and Panelist, I. Ebert-Uphoff, May 6, 2024.  One-day (invitation-only) workshop organized jointly by the White House Office of Science and Technology Policy (OSTP) and NOAA on The Potential for Artificial Intelligence and Machine Learning (AI/ML) to Transform Numerical Weather Prediction.  Workshop was held in Washington D.C. in the Herbert C. Hoover Building, followed by a reception in the Eisenhower Executive Office Building (part of the White House complex).

Invited talk, I. Ebert-Uphoff, et al., A Research Agenda for the Evaluation of AI-Based Weather Forecasting Models, EGU General Assembly, Vienna, Austria, April 2024.

Core science keynote: Imme Ebert-Uphoff, J. Q. Stewart, K. A. Hilburn, J. T. Radford, R. T. DeMaria, R. Chase, R. A. Lagerquist, C. White, Y. Lee, J. Apke, K. D. Musgrave, L. Ver Hoef, C. E. Kumler, M. S. Wandishin, J. Duda, I. Jankov, and D. D. Turner,  A Research Agenda for the Evaluation of AI-Based Weather Forecasting Models, 104th AMS Annual Meeting, Baltimore, MD, Jan 2024.

Town Hall Meeting – Panelist, Forecasting Trends: The Balance between NWP and AI and How It Will Evolve in the Near or Distant Future, 104th AMS Annual Meeting, Baltimore, MD, Jan 30, 2024. (https://ams.confex.com/ams/104ANNUAL/meetingapp.cgi/Session/66096)

Panelist, GPAI Tokyo Summit 2022, Global Partnership on Artificial Intelligence (GPAI), Expert Panel on AI for extreme weather in a changing climate, Nov 21, 2022.

Invited talk, A Gentle Introduction to Creating and Evaluating Uncertainty Estimates with Neural Networks for Earth Science Applications, ECMWF-ESA Workshop on machine learning for earth observation and prediction, Nov 15, 2022.  Link to slides and recording.

National Academies of Sciences, Engineering and Medicine, Workshop on Machine Learning and Artificial Intelligence to Advance Earth System Science: Opportunities and Challenges

Served on the following panel: Responsible and Ethical Use and JEDI Issues for ML/AI in Weather, Climate and Earth System Science.  Feb 10, 2022. LINK

AI for Good – International seminar series organized by the United Nations’ International Telecommunication Union (ITU) in partnership with 40 United Nations Sister Agencies: 1-hour presentation, jointly with Jakob Runge (DLR), on “Causal inference for earth system sciences”. Jan 19, 2022. LINK

I. Ebert-Uphoff, K. Hilburn, R. Lagerquist, Y. Lee, Using neural network methods to estimate cloud properties from satellite imagery – challenges and recent approaches (Invited talk), AGU Fall meeting, Dec 13-17, 2021.

I. Ebert-Uphoff, “Two applications of causal discovery in climate science”,  Workshop on Case Studies of Causal Discovery with Model Search, Carnegie Mellon University, Pittsburgh, PA, October 25-27, 2013. Video recording of the talk.

I. Ebert-Uphoff and Kyle Hilburn, On the Interpretation of Neural Networks Trained for Meteorological Applications, ECMWF-ESA Workshop on Machine Learning for Earth System Observation and Prediction, Oct 2020.

I. Ebert-Uphoff and Kyle Hilburn, Which strategies did my neural network learn? The 2nd NOAA Workshop on Leveraging AI in Environmental Sciences, Sept 17, 2000, Link to Recording.

I. Ebert-Uphoff and Elizabeth Barnes, Overview of Knowledge-Guided Machine Learning for Weather and Climate, Workshop on Knowledge Guided Machine Learning (KGML):
A Framework for Accelerating Scientific Discovery, Aug 19, 2020.

I. Ebert-Uphoff,  Peering Inside the Black Box of Machine Learning for Earth Science – Part 2, Invited lecture at NCAR Summer school on Artificial Intelligence for Earth System Science (AI4ESS), June 25, 2020.  (Recording available here)

E.A. Barnes (presenter), I. Ebert-Uphoff,  J. Hurrell, C.W. Anderson, and D. Anderson. Viewing Climate Signals through an AI Lens (Core Science Keynote). In 100th American Meteorological Society Annual Meeting. (Abstract and recording of oral presentation), Jan 2020.

I. Ebert-Uphoff, Machine learning for weather and climate – opportunities, challenges and promising strategies, Workshop – Women in Data Science @ Stanford Earth, Nov 1, 2019.

I. Ebert-Uphoff, E. Barnes, B. Toms, Tools for Interpreting how and what neural networks learn, and their applications for climate and weather (presenting remotely), NOAA-STAR seminar, Oct 28, 2019.

C. Kumler and I. Ebert-Uphoff, Machine Learning Specific to Climate and Weather Applications (presented remotely), July 18, 2019.  Repeated on August 8, 2019 (due to high demand),  NOAA-STAR seminar.

I. Ebert-Uphoff, Title: “An Overview of Network Methods Focusing on Extremal Dependence”, Joint Statistical Meetings (JSM2019), July 30, 2019, Denver, CO.

I. Ebert-Uphoff, Title: “Learning networks from data in Climate and Geosciences: Correlation Networks, Event Synchronization and Causal Networks”, Workshop on Data Analytics for Climate and Earth (DANCE): Causality, patterns and prediction, March 27-29, 2019, Arrowhead, CA.

I. Ebert-Uphoff, “Causal Discovery for the geosciences & Strategies for successful collaboration between geoscientists and data scientists”, workshop on Emerging Data Sciences and Machine Learning Opportunities in the Weather and Climate Sciences at AGU Fall meeting, Dec 13, 2018.

I. Ebert-Uphoff, “Methods for Causality Analysis in Climate Science” , SAMSI Program on Mathematical and Statistical Methods for Climate and the Earth System (CLIM), opening workshop, Aug 23, 2017, North Carolina Research Triangle, NC.  Video recording of the talk.

I. Ebert-Uphoff, “Causal discovery for geoscience 101”, Invited Talk at the NCAR workshop on Uncertainty and Causality Assessment in modeling extreme and rare events, April 2016, (no recording available).

I. Ebert-Uphoff,Applying Causal Discovery Methods in the Geosciences — Challenges and Opportunities, Big Data Tsunami at the Interface of Statistics, Environmental Sciences and Beyond, Banff International Research Station for Mathematical Innovation and Discovery, Banff, Alberta, Canada, March 11-13, 2016.  Video recording of the talk.

I. Ebert-Uphoff, “Knowledge Discovery in Climate Science”, Invited Talk at the Fifth International workshop on Climate Informatics (CI2015), Sept 25, 2015. Video recording of the talk.

I. Ebert-Uphoff, “The Potential of Causal Discovery Methods in Climate Science”, NCAR CISL presentation, National Center for Atmospheric Research, Boulder, CO, Feb 12, 2015.  Video recording of the talk.

I. Ebert-Uphoff, “Weakening of atmospheric information flow in a warming climate – preliminary results”, Fourth Workshop on Understanding Climate Change from Data (June 30 – July 2, 2014, NCAR, Boulder, CO).  Video recording of the talk.

Book Chapters

T. Beucler, I. Ebert-Uphoff, S. Rasp, M. Pritchard, P. Gentine, “Machine Learning for Clouds and Climate”, book chapter in Geophysical Monograph Series, accepted Mar 2021,  Preprint here.

Mamalakis, A., Ebert-Uphoff, I. and Barnes, E.A., 2022, April. Explainable artificial intelligence in meteorology and climate science: Model fine-tuning, calibrating trust and learning new science. In xxAI-Beyond Explainable AI: International Workshop, Held in Conjunction with ICML 2020, July 18, 2020, Vienna, Austria, Revised and Extended Papers (pp. 315-339). Cham: Springer International Publishing.

I. Ebert-Uphoff and Y. Deng, “Using Causal Discovery Algorithms to Learn about our Planet’s Climate“, book chapter in Machine Learning and Data Mining Approaches to Climate Science, Proceedings of the 4th International Workshop on Climate Informatics, Editors: V. Lakshmanan, E. Gilleland, A. McGovern, M. Tingley, Springer, 2015, pp. 113-.126  Chapter available here.

I. Ebert-Uphoff, C.M. Gosselin, D.W. Rosen, T. Laliberté, “Rapid Prototyping for Robotics”, book chapter in “Cutting Edge Robotics”, Pro Literatur Verlag, Mammendorf, Germany, 2005, pp. 17-46.

Editor of Proceedings

J. Brajard, A. Charantonis, C. Chen, J. Runge (Eds.), I. Ebert-Uphoff, C. Monteleoni, D. Nychka, E. Szekely (Series Eds.), Proceedings of the 9th International Workshop on Climate Informatics: CI 2019“, Sept 2019.  NCAR Technical Note NCAR/ TN-561+PROC, 278 pp,  doi:10.5065/y82j-f154.  Get PDF here.

C. Chen, D. Cooley, J. Runge, and E. Szekely (Eds.), I. Ebert-Uphoff, D. Hammerling, C. Monteleoni, D. Nychka (Series Eds.), “Proceedings of the 8th International Workshop on Climate Informatics: CI 2018“, Sept 2018.  NCAR Technical Note NCAR/ TN-550+PROC, 151 pp, doi:10.5065/D6BZ64XQ.  Get PDF here.

V. Lyubchich, N. C. Oza, A. Rhines, and E. Szekely, eds., (Eds.), I. Ebert-Uphoff, C. Monteleoni, D. Nychka (Series Eds.), “Proceedings of the 7th International Workshop on Climate Informatics: CI 2017“, Sept 2017.  NCAR Technical Note NCAR/TN-536+PROC, 132 pp, doi:10.5065/D6222SH7.  Get PDF here.

A. Banerjee, W. Ding, J. Dy, V. Lyubchich, A. Rhines (Eds.), I. Ebert-Uphoff, C. Monteleoni, D. Nychka (Series Eds.), “Proceedings of the 6th International Workshop on Climate Informatics: CI 2016“, Sept 2016.  NCAR Technical Note NCAR/TN-529+PROC, 159 pp, doi: 10.5065/D6K072N6. 

C.M. Gosselin and I. Ebert-Uphoff, editors, Proceedings of the Workshop on Fundamental Issues and Future Research Directions for Parallel Mechanisms and Manipulators, Québec City, QC, Canada, Oct 2002. Published in two versions: Printed Proceedings (359 pages) and CD Proceedings.

Patents

United States Letters Patent No. 6,836,736 (issued Dec 28, 2004). Inventors: Mark Allen, Wayne Book, Imme Ebert-Uphoff, Ari Glezer, David Rosen, Joroslaw Rossignac. Title: “Digital Clay Apparatus and Method”.

Technical Reports (additional preprints listed within categories above)

J. Golmohammadi, I. Ebert-Uphoff, S. He, Y. Deng, A. Banerjee, “High-Dimensional Dependency Structure Learning for Physical Processes”, arXiv:1709.03891 [cs.LG], Sept 12, 2017.  Get pdf here.

I. Ebert-Uphoff, Y. Deng, “Using Causal Discovery to Track Information Flow in Spatio-Temporal Data – A Testbed and Experimental Results Using Advection-Diffusion Simulations”, Dec 27, 2015, arXiv:1512.08279 [cs.LG].   Get PDF here.

I. Ebert-Uphoff, Y. Deng, “High efficiency implementation of PC and PC stable algorithms yields three-dimensional graphs of information flow for the Earth’ atmosphere”, Technical Report, Colorado State University, Department of Electrical and Computer Engineering, CSU-ECE-2014-1,  September 3, 2014.  Available at http://hdl.handle.net/10217/83709.  Get PDF here.

I. Ebert-Uphoff, Y. Deng, “Causal Discovery Methods for Climate Networks”, Technical Report, Georgia Institute of Technology, School of Mechanical Engineering, GT-ME-2010-001, December 2010. Get PDF here.

I. Ebert-Uphoff, “A Probability-Based Approach to Soft Discretization for Bayesian Networks”, Technical Report, Georgia Institute of Technology, School of Mechanical Engineering, GT-ME-2009-002, September 2009. Get PDF here.

I. Ebert-Uphoff, “Tutorial on How to Measure Link Strengths in Discrete Bayesian Networks”, Technical Report, Georgia Institute of Technology, School of Mechanical Engineering, GT-ME-2009-001, September 2009. Get PDF here.

Sebastien J. Wolff, Imme Ebert-Uphoff, Harvey Lipkin, “Statically Stable Assembly Sequence Generation for Many Identical Assembly Blocks”, Technical Report, Georgia Institute of Technology, College of Computing, GIT-IC-07-06, October 2007.

I. Ebert-Uphoff, “Measuring Connection Strengths and Link Strengths in Discrete Bayesian Networks”, Technical Report, Georgia Institute of Technology, College of Computing, GT-IIC-07-01, January 2007. Get PDF here.