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.
Recent and Upcoming Invited Talks
- 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.
- 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.
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.