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Date Time
MST		 Temp
°F		 RH
%		 DewPt
°F		 Wind
mph		 Dir
°		 Gust
mph		 Dir
°		 Press
in Hg		 Solar
W/m^2		 Prec
in
2023-01-31 23:15 10.0 79.4 4.9 2.6 220 2.9 231 24.894 0.0 0.00
2023-01-31 23:10 9.3 79.6 4.3 3.1 220 4.4 249 24.894 0.0 0.00
2023-01-31 23:05 10.4 78.0 4.9 4.2 267 6.2 283 24.894 0.0 0.00
2023-01-31 23:00 9.2 80.0 4.3 3.3 264 4.7 264 24.893 0.0 0.00
2023-01-31 22:55 7.7 82.1 3.4 3.8 242 6.1 299 24.894 0.0 0.00
2023-01-31 22:50 6.1 81.9 1.8 3.7 315 6.8 313 24.894 0.0 0.00
2023-01-31 22:45 8.5 74.4 2.0 3.1 339 8.3 330 24.896 0.0 0.00
2023-01-31 22:40 9.3 76.1 3.3 4.2 333 7.4 333 24.900 0.0 0.00
2023-01-31 22:35 8.8 78.1 3.4 4.6 1 5.6 345 24.901 0.0 0.00
2023-01-31 22:30 10.8 76.4 4.8 2.3 288 3.2 288 24.900 0.0 0.00
2023-01-31 22:25 11.1 76.2 5.1 0.5 260 1.9 260 24.901 0.0 0.00
2023-01-31 22:20 11.5 75.5 5.2 2.1 217 3.1 261 24.898 0.0 0.00
2023-01-31 22:15 10.1 78.5 4.8 2.4 266 4.2 259 24.899 0.0 0.00
2023-01-31 22:10 10.6 78.0 5.1 2.2 216 4.9 244 24.900 0.0 0.00
2023-01-31 22:05 10.3 78.3 4.9 3.1 247 3.8 248 24.899 0.0 0.00
2023-01-31 22:00 9.5 80.2 4.6 4.5 219 5.5 255 24.897 0.0 0.00
2023-01-31 21:55 11.1 76.3 5.1 4.2 262 8.2 255 24.896 0.0 0.00
2023-01-31 21:50 9.0 86.9 5.9 5.5 251 8.9 253 24.898 0.0 0.00
2023-01-31 21:45 5.8 84.0 2.0 3.7 265 5.0 247 24.899 0.0 0.00
2023-01-31 21:40 6.2 83.5 2.2 3.5 259 4.4 247 24.897 0.0 0.00
2023-01-31 21:35 5.7 85.8 2.4 2.7 317 3.5 316 24.896 0.0 0.00
2023-01-31 21:30 5.3 84.5 1.7 3.1 299 4.3 261 24.899 0.1 0.00
2023-01-31 21:25 5.2 84.3 1.5 2.7 248 4.3 237 24.901 0.0 0.00
2023-01-31 21:20 5.3 82.4 1.1 1.8 249 3.2 249 24.899 0.0 0.00
2023-01-31 21:15 4.7 83.7 0.9 0.8 286 2.1 286 24.897 0.1 0.00
2023-01-31 21:10 4.9 82.6 0.7 1.8 343 2.4 351 24.898 0.1 0.00
2023-01-31 21:05 4.9 82.2 0.7 3.0 351 3.7 310 24.900 0.0 0.00
2023-01-31 21:00 5.1 80.8 0.5 3.2 310 3.9 326 24.900 0.0 0.00
2023-01-31 20:55 6.6 79.4 1.6 3.4 326 4.5 345 24.900 0.0 0.00
2023-01-31 20:50 7.8 78.0 2.3 3.6 1 4.2 4 24.895 0.0 0.00
Date Time
MST		 Temp
°C		 RH
%		 DewPt
°C		 Wind
m/s		 Dir
°		 Gust
m/s		 Dir
°		 Press
hPa		 Solar
W/m^2		 Prec
mm
2023-01-31 23:15 -12.2 79.4 -15.1 1.1 220 1.3 231 843.00 0.0 0.00
2023-01-31 23:10 -12.6 79.6 -15.4 1.4 220 2.0 249 843.01 0.0 0.00
2023-01-31 23:05 -12.0 78.0 -15.0 1.9 267 2.8 283 843.01 0.0 0.00
2023-01-31 23:00 -12.6 80.0 -15.4 1.5 264 2.1 264 842.99 0.0 0.00
2023-01-31 22:55 -13.5 82.1 -15.9 1.7 242 2.7 299 843.00 0.0 0.00
2023-01-31 22:50 -14.4 81.9 -16.8 1.6 315 3.1 313 843.01 0.0 0.00
2023-01-31 22:45 -13.1 74.4 -16.7 1.4 339 3.7 330 843.08 0.0 0.00
2023-01-31 22:40 -12.6 76.1 -15.9 1.9 333 3.3 333 843.20 0.0 0.00
2023-01-31 22:35 -12.9 78.1 -15.9 2.1 1 2.5 345 843.24 0.0 0.00
2023-01-31 22:30 -11.8 76.4 -15.1 1.0 288 1.4 288 843.19 0.0 0.00
2023-01-31 22:25 -11.6 76.2 -15.0 0.2 260 0.9 260 843.24 0.0 0.00
2023-01-31 22:20 -11.4 75.5 -14.9 1.0 217 1.4 261 843.16 0.0 0.00
2023-01-31 22:15 -12.2 78.5 -15.1 1.1 266 1.9 259 843.17 0.0 0.00
2023-01-31 22:10 -11.9 78.0 -15.0 1.0 216 2.2 244 843.20 0.0 0.00
2023-01-31 22:05 -12.0 78.3 -15.0 1.4 247 1.7 248 843.19 0.0 0.00
2023-01-31 22:00 -12.5 80.2 -15.2 2.0 219 2.5 255 843.12 0.0 0.00
2023-01-31 21:55 -11.6 76.3 -14.9 1.9 262 3.7 255 843.06 0.0 0.00
2023-01-31 21:50 -12.8 86.9 -14.5 2.5 251 4.0 253 843.13 0.0 0.00
2023-01-31 21:45 -14.6 84.0 -16.7 1.7 265 2.2 247 843.17 0.0 0.00
2023-01-31 21:40 -14.4 83.5 -16.5 1.5 259 2.0 247 843.12 0.0 0.00
2023-01-31 21:35 -14.6 85.8 -16.5 1.2 317 1.5 316 843.06 0.0 0.00
2023-01-31 21:30 -14.8 84.5 -16.8 1.4 299 1.9 261 843.18 0.1 0.00
2023-01-31 21:25 -14.9 84.3 -16.9 1.2 248 1.9 237 843.23 0.0 0.00
2023-01-31 21:20 -14.8 82.4 -17.2 0.8 249 1.4 249 843.16 0.0 0.00
2023-01-31 21:15 -15.2 83.7 -17.3 0.4 286 0.9 286 843.11 0.1 0.00
2023-01-31 21:10 -15.1 82.6 -17.4 0.8 343 1.1 351 843.16 0.1 0.00
2023-01-31 21:05 -15.1 82.2 -17.4 1.3 351 1.6 310 843.23 0.0 0.00
2023-01-31 21:00 -14.9 80.8 -17.5 1.4 310 1.7 326 843.21 0.0 0.00
2023-01-31 20:55 -14.1 79.4 -16.9 1.5 326 2.0 345 843.21 0.0 0.00
2023-01-31 20:50 -13.5 78.0 -16.5 1.6 1 1.9 4 843.03 0.0 0.00
CIRA

Cooperative Institute for Research in the Atmosphere

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Imme Ebert-Uphoff

Imme Ebert-Uphoff


Job Title:

Machine Learning Lead

CIRA Teams:

Email Address:

Office Location:


ACRC 207

About Me:


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

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

  1.  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.
  2.  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.
  3.  Research Professor in Electrical and Computer Engineering (ECE). I joined ECE in 2011.

Since 2019 I spend most of my time working at CIRA. Key activities include:

  • Working directly on CIRA projects,
  • Providing support for CIRA research scientists working with ML,
  • Teaching customized ML lessons (Ex.: CIRA Short Course on ML for Weather and Climate)
  • Coordinating ML working groups at CIRA (ML core) and at NOAA (LEAP-RT).

Key applications include:

  • Using ML to make the most out of satellite imagery, especially for applications related to tropical cyclones, and for identifying convection;
  • Generating synthetic imagery (e.g., MRMS or MW) from GOES imagery;
  • Using ML to speed up radiative transfer in numerical weather prediction models (Ryan Lagerquist‘s project).

The work related to the AI institute ties directly into many of these applications, with emphasis on tropical cyclones. Key interests and to learn about our approaches:

See longer list of publications below.

How to reach me:

  • Send email to iebert@colostate.edu
  • My office (during non-COVID times) is at CSU’s Foothills campus: ACRC 207.

Past Work

Journal Publications

  • 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, early online release Dec 15, 2021, https://doi.org/10.1175/JTECH-D-21-0084.1.
  • 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
  • McGovern, Amy; Bostrom, Ann; Ebert-Uphoff, Imme; He, Ruoying; Thorncroft, Chris; Tissot, Philippe; Boukabara, Sid; Demuth, Julie; Gagne II, David John; Hickey, Jason; Williams, John K. (2020) Weathering Environmental Change Through Advances in AI. EOS, Volume 101, https://doi.org/10.1029/2020EO147065, July 2020.
  • Barnes, E. A., Toms, B., Hurrell, J. W., Ebert-Uphoff, I., Anderson, C., Anderson, D. Indicator patterns of forced change learned by an artificial neural network. Journal of Advances in Modeling Earth Systems (JAMES), https://doi.org/10.1029/2020MS002195, Aug 2020.  (arXiv preprint from May 2020: here).
  • Ebert-Uphoff, I., Hilburn, K. A. Evaluation, Tuning and Interpretation of Neural Networks for Meteorological Applications, Bulletin of the American Meteorological Society (BAMS), https://doi.org/10.1175/BAMS-D-20-0097.1, Aug 2020.  (arXiv preprint from May 2020 here).
  • 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)
  • Toms, B. A., Barnes, E. A., & Ebert-Uphoff, I. Physically Interpretable Neural Networks for the Geosciences: Applications to Earth System Variability. Journal of Advances in Modeling Earth Systems (JAMES), https://doi.org/10.1029/2019MS002002, June 2020. (arXiv preprint V1 from Dec 2019: here)
  • S.M. Samarasinghe, Y. Deng, I. Ebert-Uphoff, “A Causality-Based View of the Interaction between Synoptic- and Planetary-Scale Atmospheric Disturbance“, Journal of the Atmospheric Sciences, 77 (3): 925–941, https://doi.org/10.1175/JAS-D-18-0163.1, Feb 2020.
  • Barnes, E. A., Hurrell, J. W., Ebert‐Uphoff, I., Anderson, C., & Anderson, D., Viewing forced climate patterns through an AI Lens. Geophysical Research Letters, 46(22), 13389-13398, https://doi.org/10.1029/2019GL084944, Nov 2019.
  • I. Ebert-Uphoff, S.M. Samarasinghe, E.A. Barnes, “On the Thoughtful Use of AI in Earth Science“, EOS Opinion, 100, Oct 2019.
  • D. Pennington, I. Ebert-Uphoff,  N. Freed, J. Martin, S.A. Pierce, “Bridging Sustainability Science, Earth Science and Data Science through interdisciplinary education“, Sustainability Science, 15, 647–66, https://doi.org/10.1007/s11625-019-00735-3, 2019.
  • E.A. Barnes, S.M. Samarasinghe, I. Ebert-Uphoff, J. Furtado, “Tropospheric and stratospheric causal pathways between the MJO and NAO“, Journal of Geophysical Research – Atmospheres, 124(16), 9356-9371, https://doi.org/10.1029/2019JD031024, Aug 2019.
  • Whitney K. Huang, Daniel S. Cooley, Imme Ebert-Uphoff, Chen Chen and Snigdhansu Chatterjee, “New Exploratory Tools for Extremal Dependence: Chi Networks and Annual Extremal Networks“, Journal of Agricultural, Biological, and Environmental Statistics (JABES), special issue on Earth and Climate System, 24, pages 484–501, DOI 10.1007/s13253-019-00356-4, 2019.  (Preprint on ArXiv)
  • Yolanda Gil, Suzanne Pierce, and 26 other authors, “Intelligent Systems for Geosciences: An Essential Research Agenda“, Communications of the ACM, January 2019, Vol. 62 No. 1, Pages 76-84.
  • 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.  Get PDF here.
  • 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.
  • 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.

Refereed Conference Papers

  • 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.

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

  • M.G. 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, AMS 102st Annual Meeting, 21th Conference on Artificial Intelligence for Environmental Science, Jan 23-27, 2021.
  • A. McGovern, I. Ebert-Uphoff, A. Bostrom, and D. J. Gagne II, Ethical and Responsible AI and Trust for Weather and Climate, AMS 102st Annual Meeting, 21th Conference on Artificial Intelligence for Environmental Science, Jan 23-27, 2021.
  • A. Mamalakis, I. Ebert-Uphoff and E. Barnes, Explainable Artificial Intelligence for Environmental Science: Introducing Objectivity into the Assessment of Neural Network Attribution Methods, AMS 102st Annual Meeting, 21th Conference on Artificial Intelligence for Environmental Science, Jan 23-27, 2021.
  • 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, 2021.
  • R. Lagerquist, I. Ebert-Uphoff, J. Q. Stewart, and C. Kumler, Nowcasting Convection with Deep Learning and Custom Spatially Aware Loss Functions, AMS 102st Annual Meeting, 21th Conference on Artificial Intelligence for Environmental Science, Jan 23-27, 2021.
  • R. A. Lagerquist, D. D. Turner, I. Ebert-Uphoff, J. Q. Stewart, and V. Hagerty, Grid-Agnostic Deep Learning for Parameterizing Radiative Transfer, AMS 102st Annual Meeting, 21th Conference on Artificial Intelligence for Environmental Science, Jan 23-27, 2021.
  • I. Ebert-Uphoff, R. Lagerquist, K. A. Hilburn, Y. Lee, K. Haynes, J. Stock, C. Kumler, and J. Q. Stewart, How to Develop Custom Loss Functions for Neural Networks in Meteorology, AMS 102st Annual Meeting, 21th Conference on Artificial Intelligence for Environmental Science, Jan 23-27, 2021.
  • R. Lagerquist, I. Ebert-Uphoff, Exploring the Benefits of Integrating Fourier and Wavelet Transforms into Neural Networks for Meteorological Applications, AMS 102st Annual Meeting, 21th Conference on Artificial Intelligence for Environmental Science, Jan 23-27, 2021.
  • K. Haynes, C. Slocum, J. A. Knaff, K. Musgrave, and I. Ebert-Uphoff, Simulating 89-GHz Imagery from Operational Geostationary Satellites Using Machine Learning, AMS 102st Annual Meeting, 21th Conference on Artificial Intelligence for Environmental Science, Jan 23-27, 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.
  • 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.)
  • 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.
  • 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.

Book Chapters

  1. 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.
  2. 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.
  3. 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

  1. 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.
  2. 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.
  3. 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.
  4. 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.  Get PDF here.
  5. 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.

Recent and Upcoming Invited Talks

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)

  1. 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.
  2. 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.
  3. 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.
  4. 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.
  5. 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.
  6. 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.
  7. 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.
  8. 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.