Machine Learning in Geospace - Revision history

Matthew.argall: /* Session 3: Industry Panel & Social Mixer */

2022-06-22T02:01:53Z

Session 3: Industry Panel & Social Mixer

Matthew.argall at 01:54, 22 June 2022

2022-06-22T01:54:58Z

Matthew.argall: /* Session 3: Industry Panel & Social Mixer */

2022-06-20T15:49:42Z

Session 3: Industry Panel & Social Mixer

Matthew.argall: /* VGEM 2022 */

2022-06-20T15:22:40Z

VGEM 2022

Matthew.argall: /* When */

2022-06-20T15:21:57Z

When

Matthew.argall at 15:10, 20 June 2022

2022-06-20T15:10:07Z

Matthew.argall at 14:55, 20 June 2022

2022-06-20T14:55:12Z

Matthew.argall: /* What */

2021-07-28T15:26:42Z

What

Matthew.argall: /* When */

2021-07-28T15:25:48Z

When

Matthew.argall at 10:55, 27 July 2021

2021-07-27T10:55:33Z

@@ Line 124: / Line 124: @@
 | 45 min || [https://gem.epss.ucla.edu/mediawiki/index.php/Machine_Learning_in_Geospace#Kyle_Bahr Kyle Bahr] || Emergent Analytics
 |-
-|  || Matt Gilson || Google, Argo
+|  || [https://gem.epss.ucla.edu/mediawiki/index.php/Machine_Learning_in_Geospace#Matt_Gilson Matt Gilson] || Google, Argo
 |-
 | || Megan Cartwright || Netflix

← Older revision		Revision as of 01:54, 22 June 2022
Line 136:		Line 136:

	Kyle Bahr is the owner and principal of Emergent Analytics, a management consulting company that specializes in bringing machine learning and other advanced analytics techniques to sustainable development problems. After completing his PhD. in Mining and Earth Systems Engineering at the Colorado School of Mines, he accepted a post-doctoral fellowship at the Missouri University of Science and Technology. Before founding Emergent Analytics, Kyle was an Assistant Professor for four years at Tohoku University in Sendai, Japan, where his research focused on resource development, especially in the area of the social license for geothermal energy. Kyle still conducts academic research and teaching through his associations with the University of Eastern Finland and the Colorado School of Mines. Kyle has worked on consulting projects on 6 continents. He has expertise in computer modelling, data science, and the application of machine learning methods to resource development challenges.		Kyle Bahr is the owner and principal of Emergent Analytics, a management consulting company that specializes in bringing machine learning and other advanced analytics techniques to sustainable development problems. After completing his PhD. in Mining and Earth Systems Engineering at the Colorado School of Mines, he accepted a post-doctoral fellowship at the Missouri University of Science and Technology. Before founding Emergent Analytics, Kyle was an Assistant Professor for four years at Tohoku University in Sendai, Japan, where his research focused on resource development, especially in the area of the social license for geothermal energy. Kyle still conducts academic research and teaching through his associations with the University of Eastern Finland and the Colorado School of Mines. Kyle has worked on consulting projects on 6 continents. He has expertise in computer modelling, data science, and the application of machine learning methods to resource development challenges.
		+
		+	===== Matt Gilson =====
		+
		+	Matt Gilson got his Ph.D from the University of New Hampshire in 2011. In 2013, he transitioned into industry working on internal tools at Google. At 2015, he joined an early-stage startup that was focused on building sales tools. As part of the early stages of product development, he spent a little time familiarizing himself with machine learning techniques. Ultimately, that startup did not succeed and he took a job working for Argo AI in 2017 where he has been since. He's currently a Staff Software Architect responsible for large portions of the data pipelines and processes that produce the maps for the autonomous vehicles. The maps are used for everything from large-scale point-to-point navigation to centimeter precision localization and everything in between.

	= VGEM 2021 =		= VGEM 2021 =

@@ Line 122: / Line 122: @@
 ! Duration || Panelist  || Affiliation
 |-
-| 45 min || Kyle Bahr || Emergent Analytics
+| 45 min || [https://gem.epss.ucla.edu/mediawiki/index.php/Machine_Learning_in_Geospace#Kyle_Bahr Kyle Bahr] || Emergent Analytics
 |-
 |  || Matt Gilson || Google, Argo

@@ Line 1: / Line 1: @@
 ___TOC___
-= VGEM 2022 =
+= GEM 2022 =
 == Workshop Overview ==

@@ Line 26: / Line 26: @@
 |-
 | ML Session I   || June 23      || Thursday        || 10:30 AM - 12:00 PM
+|-
 | ML Session II  || June 23      || Thursday        || 1:30 PM - 3:00 PM
 |-

@@ Line 55: / Line 55: @@
 |}
-== Session 1: Submitted Talks ==
+== Agenda ==
 {| class="wikitable"
@@ Line 81: / Line 85: @@
 | 6+3 min  || Jinxing Li || Modeling Ring Current Proton Fluxes Using Machine Learning and Van Allen Probe Measurements
 |-
-|  || Open Discussion
+|  || || Open Discussion
 |}
 = VGEM 2021 =

← Older revision		Revision as of 14:55, 20 June 2022
Line 1:		Line 1:
	___TOC___		___TOC___
		+
		+	= VGEM 2022 =
		+
		+	== Workshop Overview ==
		+
		+	=== What ===
		+
		+	We are back for a third year organizing the Machine Learning in Geospace session at the GEM 2022 Workshop. This year, we have three sessions. One will be filled with invited and submitted talks, one will focus on tools and applications, and another will focus on connections to industry.
		+
		+	=== Registration ===
		+
		+	Register for the in-person workshop at [https://gemworkshop.org/pages/Registration.php GEM website]. Register for the virtual component through the [https://vgem.org/schedule#monday VGEM website]
		+
		+	=== When ===
		+
		+	The workshop begins on June 20 and ends on July 24.
		+
		+	{\| class="wikitable"
		+	\|+ style="caption-side:bottom; text-align:left \| Table 1: Workhop schedule. For more details see the [https://gemworkshop.org/pages/gem2022/gem2022schedule.php Workshop webpage] and [https://gem.epss.ucla.edu/mediawiki/index.php/Machine_Learning_in_Geospace#Agenda our agenda below].
		+	! Event !! Date \|\| Day \|\| Time (Hawaii)
		+	\|-
		+	\| Student Day \|\| June 19 \|\| Sunday \|\| 8:00 AM - 3:30 PM
		+	\|-
		+	\| GEM Workshop \|\| June 20 - 24 \|\| Monday - Friday \|\| 8:00 AM - 5:00 PM + Posters & Events
		+	\|-
		+	\| ML Session I \|\| June 23 \|\| Thursday \|\| 10:30 AM - 12:00 PM
		+	\|
		+	\| ML Session II \|\| June 23 \|\| Thursday \|\| 1:30 PM - 3:00 PM
		+	\|-
		+	\| ML Session III \|\| June 24 \|\| Friday \|\| 1:30 PM - 3:00 PM
		+	\|}
		+
		+	=== Who ===
		+
		+	{\| class="wikitable"
		+	\|+ style="caption-side:bottom; text-align:left \| Table 2: Session organizers. *Student Organizers
		+	! Name !! !! Email
		+	\|-
		+	\| Matthew Argall \|\| \|\| [mailto:matthew.argall@unh.edu matthew.argall@unh.edu]
		+	\|-
		+	\| Jacob Bortnik \|\| \|\| [mailto:jbortnik@gmail.com jbortnik@gmail.com]
		+	\|-
		+	\| Wendy Carande \|\| \|\| [mailto:Wendy.Carande@lasp.colorado.edu Wendy.Carande@lasp.colorado.edu]
		+	\|-
		+	\| Doğa Ozturk \|\| \|\| [mailto:dsozturk@alaska.edu dsozturk@alaska.edu]
		+	\|-
		+	\| Josh Rigler \|\| \|\| [mailto:erigler@usgs.gov erigler@usgs.gov]
		+	\|-
		+	\| Jason Shuster \|\| \|\| [mailto:jason.r.shuster@nasa.gov jason.r.shuster@nasa.gov]
		+	\|-
		+	\| Brian Swiger* \|\| \|\| [mailto:swigerbr@umich.edu swigerbr@umich.edu]
		+	\|-
		+	\| Alexandra Wold* \|\| \|\| [mailto:Alexandra.Wold@colorado.edu Alexandra.Wold@colorado.edu]
		+	\|}
		+
		+	== Session 1: Submitted Talks ==
		+
		+	{\| class="wikitable"
		+	\|+ style="caption-side:bottom; text-align:left \| Table 3: Speakers and presentation titles.
		+	! Duration \|\| Name \|\| Title
		+	\|-
		+	\| 6+3 min \|\| Boris Kramer \|\| Learning low-dimensional dynamical systems operators for advection-dominated solar wind models
		+	\|-
		+	\| 6+3 min \|\| Xiangning Chu \|\| Relativistic electron model in the outer radiation belt using a neural network approach
		+	\|-
		+	\| 6+3 min \|\| Heidi Nykyri \|\| Applications of Information Theory to Unravel the origin of the Energetic Electron Microinjections
		+	\|-
		+	\| 6+3 min \|\| Matthew Blandin \|\| Feature Importance: How a computer assesses our variables
		+	\|-
		+	\| 6+3 min \|\| Anthony Sciola (Slava Merkin) \|\| Ring Current Plasma Pressure Reconstructed from Data-mined Magnetometer Measurements Embedded Within a Global MHD Model
		+	\|-
		+	\| 6+3 min \|\| Connor O'Brien \|\| Neural Network Models of the Near-Earth Solar Wind and Magnetosheath
		+	\|-
		+	\| 6+3 min \|\| Andy Edmond \|\| Unsupervised Clustering of Magnetospheric Dayside Data
		+	\|-
		+	\| 6+3 min \|\| Xin Cao \|\| The Response of Equivalent Ionospheric Currents to the External Drivers via Machine Learning
		+	\|-
		+	\| 6+3 min \|\| Donglai Ma \|\| Modeling the dynamic variability of sub-relativistic outer radiation belt electron fluxes using machine learning.
		+	\|-
		+	\| 6+3 min \|\| Jinxing Li \|\| Modeling Ring Current Proton Fluxes Using Machine Learning and Van Allen Probe Measurements
		+	\|-
		+	\| \|\| Open Discussion
		+	\|}

	= VGEM 2021 =		= VGEM 2021 =

@@ Line 7: / Line 7: @@
 === What ===
-We are back for a second year organizing the Machine Learning in Geospace session at the VGEM 2021 Workshop. This year, we have two sessions. One will be filled with invited and submitted talks and the other will focus on organizing a Kaggle Competition within the community to address a large, inter-disciplinary science problem using machine learning and data science techniques. See our [https://docs.google.com/spreadsheets/d/13Kwn0ljt6id86m8ozTxDBIUbqG3PH0NXgU8Gax-um1c/edit?usp=sharing detailed schedule here] and [https://gem.epss.ucla.edu/mediawiki/index.php?title=Machine_Learning_in_Geospace&action=submit#Agenda below].
+We are back for a second year organizing the Machine Learning in Geospace session at the VGEM 2021 Workshop. This year, we have two sessions. One will be filled with invited and submitted talks and the other will focus on organizing a Kaggle Competition within the community to address a large, inter-disciplinary science problem using machine learning and data science techniques. See our [https://docs.google.com/spreadsheets/d/13Kwn0ljt6id86m8ozTxDBIUbqG3PH0NXgU8Gax-um1c/edit?usp=sharing Schedule Overview here] and [https://gem.epss.ucla.edu/mediawiki/index.php?title=Machine_Learning_in_Geospace&action=submit#Agenda our detailed schedule below].
 === Registration ===

@@ Line 18: / Line 18: @@
 {| class="wikitable"
-|+ style="caption-side:bottom; text-align:left | Table 1: Workhop schedule. For more details see the [https://gemworkshop.org/pages/gem2021/gem2021schedule.php Workshop webpage] and [https://gem.epss.ucla.edu/mediawiki/index.php?title=Machine_Learning_in_Geospace&action=submit#Agenda our agenda].
+|+ style="caption-side:bottom; text-align:left | Table 1: Workhop schedule. For more details see the [https://gemworkshop.org/pages/gem2021/gem2021schedule.php Workshop webpage] and [https://gem.epss.ucla.edu/mediawiki/index.php/Machine_Learning_in_Geospace#Agenda our agenda below].
 ! Event         !! Date         || Day             || Time (Eastern)
 |-

@@ Line 7: / Line 7: @@
 === What ===
-We are back for a second year organizing the Machine Learning in Geospace session at the VGEM 2021 Workshop. This year, we have two sessions. One will be filled with invited and submitted talks and the other will focus on organizing a Kaggle Competition within the community to address a large, inter-disciplinary science problem using machine learning and data science techniques.
+We are back for a second year organizing the Machine Learning in Geospace session at the VGEM 2021 Workshop. This year, we have two sessions. One will be filled with invited and submitted talks and the other will focus on organizing a Kaggle Competition within the community to address a large, inter-disciplinary science problem using machine learning and data science techniques. See our [https://docs.google.com/spreadsheets/d/13Kwn0ljt6id86m8ozTxDBIUbqG3PH0NXgU8Gax-um1c/edit?usp=sharing detailed schedule here] and [https://gem.epss.ucla.edu/mediawiki/index.php?title=Machine_Learning_in_Geospace&action=submit#Agenda below].
 === Registration ===
@@ Line 57: / Line 57: @@
 ! Duration || Name  || Title
 |-
-| 10 min || Matthew Argall  || Session overview, decadal planning
+| 10 min || Matthew Argall   || Session overview, decadal planning
 |-
-| 10 min || Breakout Rooms  || Ice Breaker
+| 10 min || Breakout Rooms   || Ice Breaker
 |-
-| 10 min || Ayris Narock    || Ethical AI
+| 10 min || Ayris Narock     || Ethical AI
 |-
-| TBD    || Matthew Blandin || Prediction of Geomagnetic Field Disturbances across Alaska using Machine Learned LSTM Neural Networks
+| 6 min  || Matthew Blandin  || Prediction of Geomagnetic Field Disturbances across Alaska using Machine Learned LSTM Neural Networks
 |-
-| TBD    || Sheng Huang     || Recurrent neural network implementation of modelling global plasmaspheric density
+| 6 min  || Sheng Huang      || Recurrent neural network implementation of modelling global plasmaspheric density
 |}
@@ Line 78: / Line 88: @@
 | 15 min || Addison Howard   || How to Design a Successful Kaggle Competition
 |-
-| 5 min || Manoj Nair        || NOAA's DataDriven [https://www.drivendata.org/competitions/73/noaa-magnetic-forecasting/ MagNet Chellenge]
+| 5 min  || Manoj Nair       || NOAA's DataDriven [https://www.drivendata.org/competitions/73/noaa-magnetic-forecasting/ MagNet Chellenge]
 |-
-| 5 min || Barbara Thompson  || AI-Ready Datasets
+| 5 min  || Raphael Attie    || Data classification tools
 |-
-| 5 min || Raphael Attie     || Data classification tools
+| 5 min  || Wendy Carande    || Getting Started with ML: Common Problems
 |-
-| 5 min || Wendy Carande     || Getting Started with ML: Common Problems
+| 20 min || Breakout Rooms   || Designing the competition.<br>* What problems can be addressed with an ML Competition?<br>* What missions or datasets would be useful?<br>* What resources are required to prepare for a successful competition?<br>* What evaluation criteria will lead to a fair and meaningful winner?<br>* Get the discussion started early by contributing to our [https://jamboard.google.com/d/1SHbGU6FyxdiOaXhY_r8BnUbEMh4UMu_DI1fTL6MR1GU/edit?usp=sharing Jam Board]
 |-
-| TBD   || Panelists        || Breakout Rooms
+| 15 min || Andrés Muñoz-Jaramillo<br>Enrico Camporeale<br>Shasha Zou || "What & How" A recap of the breakout sessions
 |-
-| TBD   || Panelists        || Summary & Discussion
+| 20 min || Speakers & Panelists || Open Discussion on Kaggle Competition
 |}