RG: Machine Learning - Revision history

Svalluri: /* mini-GEM 2025 */

2025-12-05T19:23:20Z

mini-GEM 2025

Svalluri: /* mini-GEM 2025 */

2025-12-05T19:22:30Z

mini-GEM 2025

Svalluri at 19:19, 5 December 2025

2025-12-05T19:19:21Z

Svalluri at 19:17, 5 December 2025

2025-12-05T19:17:27Z

Svalluri at 19:14, 5 December 2025

2025-12-05T19:14:22Z

Svalluri at 13:15, 5 December 2025

2025-12-05T13:15:21Z

Svalluri at 13:10, 5 December 2025

2025-12-05T13:10:13Z

Svalluri: /* GEM/CEDAR 2025 */

2025-06-18T14:32:59Z

GEM/CEDAR 2025

Svalluri: /* GEM/CEDAR 2025 */

2025-06-18T14:00:39Z

GEM/CEDAR 2025

Svalluri: /* GEM/CEDAR 2025 */

2025-06-18T13:58:44Z

GEM/CEDAR 2025

← Older revision		Revision as of 19:23, 5 December 2025
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	We will be holding a mini-GEM meeting for ML-GEM, on '''Sunday, December 14, 2025''', from '''15:30 to 17:00''' in New Orleans, just before the AGU Fall Meeting. This session focuses on the ongoing ML activities across the heliosphere. The schedule is finalized. Please contact organizers for late entries.		We will be holding a mini-GEM meeting for ML-GEM, on '''Sunday, December 14, 2025''', from '''15:30 to 17:00''' in New Orleans, just before the AGU Fall Meeting. This session focuses on the ongoing ML activities across the heliosphere. The schedule is finalized. Please contact organizers for late entries.
		+
		+	Zoom link: https://cua.zoom.us/j/84852045206

	{\| class="wikitable"		{\| class="wikitable"

@@ Line 20: / Line 20: @@
 ==mini-GEM 2025==
-We will be holding a mini-GEM meeting for ML-GEM, on '''Sunday, December 14, 2025''',  from '''15:30 to 17:00''' in New Orleans, just before the AGU Fall Meeting. We kindly ask you to '''submit your presentation title''' so we can finalize the agenda. You are welcome to give a short presentation on your ongoing '''machine learning modeling efforts''' or any topic related to your AGU presentation, or present your results from the ML-GEM challenge storm events '''(January 4, 2023, May 6, 2023, and May 11, 2024)''' spanning any regions of the geospace system, from the Sun and solar wind to magnetosphere, ionosphere, and ground.
+We will be holding a mini-GEM meeting for ML-GEM, on '''Sunday, December 14, 2025''',  from '''15:30 to 17:00''' in New Orleans, just before the AGU Fall Meeting. This session focuses on the ongoing ML activities across the heliosphere. The schedule is finalized. Please contact organizers for late entries.
 {| class="wikitable"

@@ Line 27: / Line 27: @@
 ! Time !! Presenter !! Affiliation !! Title !! Format
 |-
-| 3:30-3:35 || '''Introduction''' || || Schedule and Overview ||
+| 3:30-3:35pm || '''Introduction''' || || Schedule and Overview ||
 |-
-| 3:35-3:45 || Raman Mukundan || University of New Hampshire || The Solar Wind's Influence on Localized Geomagnetic Disturbances: A Study via Interpretable Machine Learning || In-person
+| 3:35-3:45pm || Raman Mukundan || University of New Hampshire || The Solar Wind's Influence on Localized Geomagnetic Disturbances: A Study via Interpretable Machine Learning || In-person
 |-
-| 3:45-3:55 || Mike Coughlan || NASA GSFC/CUA || Predicting Field Aligned Currents from Solar Wind data using Convolutional Residual Networks || In Person
+| 3:45-3:55pm || Mike Coughlan || NASA GSFC/CUA || Predicting Field Aligned Currents from Solar Wind data using Convolutional Residual Networks || In Person
 |-
-| 3:55-4:05 || Joshua Pettit || George Mason University/NASA GSFC || Predicting Precipitating Electrons with MAPLE: A data‑driven approach using POES MEVED data || In-person
+| 3:55-4:05pm || Joshua Pettit || George Mason University/NASA GSFC || Predicting Precipitating Electrons with MAPLE: A data‑driven approach using POES MEVED data || In-person
 |-
-| 4:05-4:15 || Gowtam Valluri || NASA GSFC/CUA || Real-Time Prediction of Auroral Precipitation and Electrodynamics using AI techniques || In-Person
+| 4:05-4:15pm || Gowtam Valluri || NASA GSFC/CUA || Real-Time Prediction of Auroral Precipitation and Electrodynamics using AI techniques || In-Person
 |-
-| 4:15-4:25 || Hyunju Connor || NASA GSFC || AIMFAHR Project: AI Modeling of Solar Wind - Upper Atmopshere Interactions || in-person
+| 4:15-4:25pm || Hyunju Connor || NASA GSFC || AIMFAHR Project: AI Modeling of Solar Wind - Upper Atmopshere Interactions || in-person
 |-
-| 4:25-4:35 || Xiangning Chu || LASP || Temporal Correlation between Plasmaspheric Plume and Ionospheric Storm-Enhanced Density || In-person
+| 4:25-4:35pm || Xiangning Chu || LASP || Temporal Correlation between Plasmaspheric Plume and Ionospheric Storm-Enhanced Density || In-person
 |-
-| 4:35-4:45 || Brendan Powers || University of Iowa || Machine Learning Applications for Injection and Bursty Bulk Flow Identification || In Person
+| 4:35-4:45pm || Brendan Powers || University of Iowa || Machine Learning Applications for Injection and Bursty Bulk Flow Identification || In Person
 |-
-| 4:45-4:55 || Eunjin Choi || SWRI || Reconstruction from Measurements Along a Space Trajectory Using Physics-Informed Neural Networks (PINNs) || in-person
+| 4:45-4:55pm || Eunjin Choi || SWRI || Reconstruction from Measurements Along a Space Trajectory Using Physics-Informed Neural Networks (PINNs) || in-person
 |-
-| 4:55-5:00 || '''Discussion''' || || ||
+| 4:55-5:00pm || '''Concluding Remarks''' || || ||
 |}

@@ Line 21: / Line 21: @@
 We will be holding a mini-GEM meeting for ML-GEM, on '''Sunday, December 14, 2025''',  from '''15:30 to 17:00''' in New Orleans, just before the AGU Fall Meeting. We kindly ask you to '''submit your presentation title''' so we can finalize the agenda. You are welcome to give a short presentation on your ongoing '''machine learning modeling efforts''' or any topic related to your AGU presentation, or present your results from the ML-GEM challenge storm events '''(January 4, 2023, May 6, 2023, and May 11, 2024)''' spanning any regions of the geospace system, from the Sun and solar wind to magnetosphere, ionosphere, and ground.
 {| class="wikitable"
 |-
 ! Time !! Presenter !! Affiliation !! Title !! Format
 |-
-| 3:30-3:40 || Raman Mukundan || University of New Hampshire || The Solar Wind's Influence on Localized Geomagnetic Disturbances: A Study via Interpretable Machine Learning || In-person
+| 3:30-3:35 || '''Introduction''' || || Schedule and Overview ||
 |-
-| 3:40-3:50 || Mike Coughlan || NASA GSFC/CUA || Predicting Field Aligned Currents from Solar Wind data using Convolutional Residual Networks || In Person
+| 3:35-3:45 || Raman Mukundan || University of New Hampshire || The Solar Wind's Influence on Localized Geomagnetic Disturbances: A Study via Interpretable Machine Learning || In-person
 |-
-| 3:50-4:00 || Joshua Pettit || George Mason University/NASA GSFC || Predicting Precipitating Electrons with MAPLE: A data‑driven approach using POES MEPED data || In-person
+| 3:45-3:55 || Mike Coughlan || NASA GSFC/CUA || Predicting Field Aligned Currents from Solar Wind data using Convolutional Residual Networks || In Person
 |-
-| 4:00-4:10 || Gowtam Valluri || NASA GSFC/CUA || Real-Time Prediction of Auroral Precipitation and Electrodynamics using AI techniques || In-Person
+| 3:55-4:05 || Joshua Pettit || George Mason University/NASA GSFC || Predicting Precipitating Electrons with MAPLE: A data‑driven approach using POES MEVED data || In-person
 |-
-| 4:10-4:20 || Hyunju Connor || NASA GSFC || AIMFAHR Project: AI Modeling of Solar Wind - Upper Atmopshere Interactions || in-person
+| 4:05-4:15 || Gowtam Valluri || NASA GSFC/CUA || Real-Time Prediction of Auroral Precipitation and Electrodynamics using AI techniques || In-Person
 |-
-| 4:20-4:30 || Xiangning Chu || LASP || Temporal Correlation between Plasmaspheric Plume and Ionospheric Storm-Enhanced Density || In-person
+| 4:15-4:25 || Hyunju Connor || NASA GSFC || AIMFAHR Project: AI Modeling of Solar Wind - Upper Atmopshere Interactions || in-person
 |-
-| 4:30-4:40 || Brendan Powers || University of Iowa || Machine Learning Applications for Injection and Bursty Bulk Flow Identification || In Person
+| 4:25-4:35 || Xiangning Chu || LASP || Temporal Correlation between Plasmaspheric Plume and Ionospheric Storm-Enhanced Density || In-person
 |-
-| 4:40-4:50 || Eunjin Choi || SWRI || Reconstruction from Measurements Along a Space Trajectory Using Physics-Informed Neural Networks (PINNs) || in-person
+| 4:35-4:45 || Brendan Powers || University of Iowa || Machine Learning Applications for Injection and Bursty Bulk Flow Identification || In Person
 |-
-| 4:50-5:00 || '''Discussion''' || || ||
+| 4:45-4:55 || Eunjin Choi || SWRI || Reconstruction from Measurements Along a Space Trajectory Using Physics-Informed Neural Networks (PINNs) || in-person
 |}

← Older revision		Revision as of 19:14, 5 December 2025
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	https://forms.gle/uNcP3YToc72WM4Ar8		https://forms.gle/uNcP3YToc72WM4Ar8
		+
		+	== mini-GEM 2025: MLGEM schedule ==
		+
		+	{\| class="wikitable"
		+	\|-
		+	! Time !! Presenter !! Affiliation !! Title !! Format
		+	\|-
		+	\| 3:30-3:40 \|\| Raman Mukundan \|\| University of New Hampshire \|\| The Solar Wind's Influence on Localized Geomagnetic Disturbances: A Study via Interpretable Machine Learning \|\| In-person
		+	\|-
		+	\| 3:40-3:50 \|\| Mike Coughlan \|\| NASA GSFC/CUA \|\| Predicting Field Aligned Currents from Solar Wind data using Convolutional Residual Networks \|\| In Person
		+	\|-
		+	\| 3:50-4:00 \|\| Joshua Pettit \|\| George Mason University/NASA GSFC \|\| Predicting Precipitating Electrons with MAPLE: A data‑driven approach using POES MEPED data \|\| In-person
		+	\|-
		+	\| 4:00-4:10 \|\| Gowtam Valluri \|\| NASA GSFC/CUA \|\| Real-Time Prediction of Auroral Precipitation and Electrodynamics using AI techniques \|\| In-Person
		+	\|-
		+	\| 4:10-4:20 \|\| Hyunju Connor \|\| NASA GSFC \|\| AIMFAHR Project: AI Modeling of Solar Wind - Upper Atmopshere Interactions \|\| in-person
		+	\|-
		+	\| 4:20-4:30 \|\| Xiangning Chu \|\| LASP \|\| Temporal Correlation between Plasmaspheric Plume and Ionospheric Storm-Enhanced Density \|\| In-person
		+	\|-
		+	\| 4:30-4:40 \|\| Brendan Powers \|\| University of Iowa \|\| Machine Learning Applications for Injection and Bursty Bulk Flow Identification \|\| In Person
		+	\|-
		+	\| 4:40-4:50 \|\| Eunjin Choi \|\| SWRI \|\| Reconstruction from Measurements Along a Space Trajectory Using Physics-Informed Neural Networks (PINNs) \|\| in-person
		+	\|-
		+	\| 4:50-5:00 \|\| '''Discussion''' \|\| \|\| \|\|
		+	\|}

	==GEM/CEDAR 2025==		==GEM/CEDAR 2025==

@@ Line 20: / Line 20: @@
 ==mini-GEM 2025==
-We will be holding a mini-GEM meeting for ML-GEM, on *Sunday, December 14, 2025*,  from *15:30 to 17:00* in New Orleans, just before the AGU Fall Meeting. We kindly ask you to *submit your presentation title* so we can finalize the agenda. You are welcome to give a short presentation on your ongoing *machine learning modeling efforts* or any topic related to your AGU presentation, or present your results from the *ML-GEM challenge storm events *(January 4, 2023, May 6, 2023, and May 11, 2024) spanning any regions of the geospace system, from the Sun and solar wind to magnetosphere, ionosphere, and ground.
+We will be holding a mini-GEM meeting for ML-GEM, on '''Sunday, December 14, 2025''',  from '''15:30 to 17:00''' in New Orleans, just before the AGU Fall Meeting. We kindly ask you to '''submit your presentation title''' so we can finalize the agenda. You are welcome to give a short presentation on your ongoing '''machine learning modeling efforts''' or any topic related to your AGU presentation, or present your results from the ML-GEM challenge storm events '''(January 4, 2023, May 6, 2023, and May 11, 2024)''' spanning any regions of the geospace system, from the Sun and solar wind to magnetosphere, ionosphere, and ground.
-An *online participation option* will be available for those who cannot attend in person. Please submit your talk here before *5 December 2025*:
+An *online participation option* will be available for those who cannot attend in person. Please submit your talk here before 5 December 2025:
 https://forms.gle/uNcP3YToc72WM4Ar8

@@ Line 17: / Line 17: @@
 | 5. Valluri Sai Gowtam     || NASA GSFC/CUA || [mailto:saigowtam.valluri@nasa.gov ]
 |}
 ==GEM/CEDAR 2025==

@@ Line 73: / Line 73: @@
 | colspan="5" |
 |-
-! colspan="5" | Session 2: General Contributions (Cont.) + Storm Challenge Session - 06/25/2025 (Wed, 2:00 PM - 4:00 PM)
+! colspan="5" | Session 2: General Contributions (Cont.) + Storm Challenge Session - 06/25/2025 (Wed, 1:30 PM - 3:30 PM)
 |-
 | colspan="5" | Zoom link: https://cua.zoom.us/j/88222028213
@@ Line 79: / Line 79: @@
 | || Introduction (2 min) || || ||
 |-
-| 1 || 2:02-2:14 || Andong Hu || Ground || Long-Term and Short-Term Dst Forecasting of the Gannon Event Using Advanced ML Approach
+| 1 || 1:32-1:44 || Andong Hu || Ground || Long-Term and Short-Term Dst Forecasting of the Gannon Event Using Advanced ML Approach
 |-
-| 2 || 2:14-2:26 || Lucas Jia || Ground || Imbalanced Regression Artificial Neural Network Model for Auroral Electrojet Indices (IRANNA): Can We Predict Strong Events?
+| 2 || 1:44-1:56 || Lucas Jia || Ground || Imbalanced Regression Artificial Neural Network Model for Auroral Electrojet Indices (IRANNA): Can We Predict Strong Events?
 |-
-| 3 || 2:26-2:38 || Piyush Mehta || Thermosphere || Storm-Time Prediction of Thermospheric Density with Reduced Order Probabilistic Emulator
+| 3 || 1:56-2:08 || Piyush Mehta || Thermosphere || Storm-Time Prediction of Thermospheric Density with Reduced Order Probabilistic Emulator
 |-
-| 4 || 2:38-2:49 || Sai Gowtam Valluri (11 min) || Ionosphere || ML-based auroral particle precipitation and electrodynamics models
+| 4 || 2:08-2:19 || Sai Gowtam Valluri (11 min) || Ionosphere || ML-based auroral particle precipitation and electrodynamics models
 |-
-| 5 || 2:49-3:01 || Jubyaid Uddin || Ionosphere || ML-AIM model for Calculating High Latitude Ionospheric Electrodynamics
+| 5 || 2:19-2:31 || Jubyaid Uddin || Ionosphere || ML-AIM model for Calculating High Latitude Ionospheric Electrodynamics
 |-
-| 6 || 3:01-3:13 || Ben Martinez || Ionosphere || High Latitude Ionospheric Prediction and Forcing Deconvolution: A Tree-Based Machine Learning Approach using PFISR and SD-WACCM-X
+| 6 || 2:31-2:43 || Ben Martinez || Ionosphere || High Latitude Ionospheric Prediction and Forcing Deconvolution: A Tree-Based Machine Learning Approach using PFISR and SD-WACCM-X
 |-
-| 7 || 3:13-3:25 || Grant Stephens || Nightside Magnetosphere || Data mining reconstruction of extreme geomagnetic storms
+| 7 || 2:43-2:55 || Grant Stephens || Nightside Magnetosphere || Data mining reconstruction of extreme geomagnetic storms
 |-
-| 8 || 3:25-3:37 || Donglai Ma || Inner Magnetosphere || Machine Learning interpretation of radiation belt dynamics
+| 8 || 2:55-3:07 || Donglai Ma || Inner Magnetosphere || Machine Learning interpretation of radiation belt dynamics
 |-
-| 9 || 3:37-3:48 || Xiangning Chu (11 min) || Inner Magnetosphere || Imbalance regression is critical for space weather
+| 9 || 3:07-3:18 || Xiangning Chu (11 min) || Inner Magnetosphere || Imbalance regression is critical for space weather
 |-
-| 10 || 3:48-4:00 || Evan McPherson || Inner Magnetosphere || Imbalanced Regressive Model of Electron Fluxes in the Earth's Outer Radiation Belt
+| 10 || 3:18-3:30 || Evan McPherson || Inner Magnetosphere || Imbalanced Regressive Model of Electron Fluxes in the Earth's Outer Radiation Belt
 |}

@@ Line 40: / Line 40: @@
 {| class="wikitable"
 |+ MLGEM/CEDAR 2025 (Tentative Schedule)
 |-
 | colspan="5" | Zoom link: https://cua.zoom.us/j/84478187920
 |-
 ! Order !! !! Speaker !! Topic Area !! Title