2nd Machine Learning in Heliophysics
Boulder, 21 – 25 March 2022
Monday 21st March
8:30 – 9:30
9:30 – 10:00

Registration and Coffee
Introductory Remarks

Session 1 – Chairs: Tom Berger (in-person), Naoto Nishizuka (virtual)
10:00 – 10:30(Invited) Rafal Angryk (Georgia State University)
Making Stuff Up: Ameliorating Data Scarcity in Flare Forecasting throughSynthetic Multivariate Time Series Generation with Deep Learning

10:30 – 11:00

Coffee break

11:00 – 11:20

Cedric Huwyler (FHNW, Switzerland)
Using Multiple Instance Learning for Explainable Flare Prediction

11:20 – 11:40Kiera van der Sande (University of Colorado)
Comparing Solar Flare Irradiance in GOES X-ray and SDO/AIA EUV Data
via Machine Learning Regression

11:40 – 12:00

Jonathan Donzallaz (iCoSys, HES-SO, Switzerland)
SolarNet: Solar Flares Prediction with Self-Supervised Learning

12:00 – 12:20

Dattaraj B Dhuri (NYU Abu Dhabi)
Deep learning reconstruction of sunspot vector magnetic fields for forecasting
solar storms

12:20 – 14:00

LunchSession 2 – Chairs: Tom Berger (in-person), Enrico Camporeale (in-person)
14:00 – 14:30(Invited) Eunsu Park (Kyung Hee University, South Korea)
Application of image translation methods based on deep learning to solar data

14:30 – 14:50

Elena G Broock (Instituto Astrofísico de Canarias)
Farnet-II: application of Convolutional LSTM and attention mechanisms tosolar far-side activity detection

14:50 – 15:10

Allison Liu (University of Colorado)
Data Augmentation of Magnetograms for Solar Flare Prediction using GANs

15:10 – 16:00

Coffee Break1

16:00 – 16:30(Invited) Andres Munoz-Jaramillo (Southwest Research Institute)
Using Neural Network Ensembles to Estimate Uncertainty in Heliophysics
Applications

16:30 – 16:50Robert Jarolim (Graz University)
Probing the coronal magnetic field with physics informed neural networks

16:50 – 17:10

Oleg Stepanyuk (Bulgarian Academy of Sciences)
Advanced Image Preprocessing and Feature Tracking for Remote CME
Characterization with Deep Learning

17:10 – 17:30

Benoit Tremblay (University of Colorado)
Emulation of MHD simulations to Infer Flows in Granulation, Sunspots, andActive Regions

18:30 – 20:00

Reception
Embassy Suites by Hilton, 2601 Canyon Blvd, Boulder

Tuesday 22nd March
8:00 – 9:30

(Hands-on tutorial) Hannah Marlowe (Amazon AWS)
Introduction to computer vision object detection for solar flare identification.

9:30 – 10:00

Coffee break

10:00 – 11:30

Wendy Carande (LASP, University of Colorado)
Tutorial on ML, part 1
https://github.com/wcarande/space_titanic

 Data exploration
 Supervised and unsupervised learning Parameter searches
11:30 – 12:30

Poster Session A (mostly virtual)

12:30 – 14:00

LunchSession 3 – Chairs: Pete Riley (virtual), Enrico Camporeale (in-person)
14:00 – 14:30(Invited) Katariina Nykyri (Embry-Riddle Aeronautical University)
Information Theory and Machine Learning Applications to Solar Wind
Magnetosphere Interactions

14:30 – 14:50

Sahib Julka (University of Passau)
An active learning approach for automatic detection of bow shock andmagnetopause crossing signatures in Mercury's magnetosphere using
MESSENGER magnetometer observations.

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14:50 – 15:10

Simon Wing (Johns Hopkins Applied Physics Lab)
Modeling radiation belt electrons with information theory informed neuralnetworks

15:10 – 15:30

Georgios Balasis (National Observatory of Athens)
Machine Learning Techniques for Automated ULF Wave Recognition in SwarmTime Series

15:30 – 15:50

Michael S. Kirk (ASTRA)The Center for HelioAnalytics

15:50 – 18:00

Poster Session A (mostly in-person) – including coffee and refreshments

Wednesday 23rd March
8:30 – 10:30

Enrico Camporeale (University of Colorado)
Tutorial on ML, part 2
https://github.com/ecamporeale/GP_lecture_MLHelio

10:30 – 11:00

Gaussian Process Regression

Coffee BreakSession 4 – Chairs: Jacob Bortnik (virtual), Amy Keesee (in-person)
11:00 – 11:20

Ramiz A. Qudsi (Boston University)
Algorithm Development for Magnetic Field topology Reconstruction in a 3-D
Simulation Box Using Machine Learning

11:20 – 11:40

Brian Swiger (University of Michigan)
Energetic Electron Flux Predictions in the near-Earth Plasma Sheet fromSolar Driving

11:40 – 12:00

Mikhail Sitnov (Johns Hopkins Applied Physics Lab)
Resolving the geomagnetic tail current sheet structure with data mining

12:00 – 12:20

Sigiava Aminalragia-Giamini (SPARC)
Radiation belt model including semi-annual variation and Solar driving(SENTINEL)

12:20 – 12:40

Sergio Vidal-Luengo (University of Colorado)
Whistler-mode Waves and Relativistic Precipitation Event Detection byEmploying Self-Organizing-Maps

12:40 – 14:00

Lunch3

Session 5 – Chairs: Simon Wing (in-person), Pete Riley (virtual)
14:00 – 14:30(invited) Jay Johnson (Andrews University)
Information Horizon of Flares and Magnetic Active Regions

14:30 – 14:50

Marius Giger (FHNW, Switzerland )Unsupervised event detection in heliophysics

14:50 – 15:10

Sophie Teichmann (Georg-August-Universität Göttingen)
Influence of solar wind parameters on unsupervised solar wind classification

15:10 – 15:30

Talwinder Singh (University of Alabama)
Improving the Arrival Time Prediction of Coronal Mass Ejections using
Magnetohydrodynamic Ensemble Modeling, Heliospheric Imager data andMachine Learning

15:30 – 16:00(invited) Gary Doran (JPL)
Responsive Onboard Science for Europa Clipper

16:00 – 16:30

Coffee16:30 – 17:00(invited) Maria Elena Innocenti (Ruhr University Bochum)
A versatile technique for unsupervised classification and preliminary analysis
of simulation results

17:00 – 17:20

Hannah Marlowe (Amazon)
An unsupervised learning approach to superstorm signature identification inprecipitating particle data

17:20 – 17:40

Andong Hu (University of Colorado)
Innovative Dst predictions using neural networks

17:40 – 18:00

Kiley Yeakel (Johns Hopkins Applied Physics Lab)
Automated algorithm for the detection of dispersionless electron injection
events in Earth’s magnetotail

Thursday 24th March
Session 6 – Chairs: Michael Kirk (virtual), Amy Keesee (in-person)
8:30 – 9:00

(invited) Maziar Raissi (University of Colorado)
Data-Efficient Deep Learning using Physics-Informed Neural Networks

9:00 – 9:20

Xiaoyue Li (Zhejiang University)
Transfer-Solar-GAN: Generation of Input Sources for Solar Wind Models with
Deep Learning4

9:20 – 9:40

Panagiotis Tigas (Oxford University)
Global geomagnetic perturbation forecasting using deep learning

9:40 – 10:00

Mohamed Nedal (Bulgarian Academy of Sciences)
Forecasting the Solar Energetic Protons Integral Flux using the Bi-Directional
Long Short-Term Memory Neural Network

10:00 – 10:20

Andrew Smith (University College London)
Producing ML-driven Real-Time Forecasts of the Probability of Large Rates of
Change of the Surface Magnetic Field in the UK

10:20 – 11:00

Coffee Break

11:00 – 11:30(invited) Alan Kaptanoglu (University of Washington)
Machine Learning for discovering sparse models of fluids, plasmas, and muchmore

11:30 – 12:30

Poster Session B (mostly virtual)

12:30 – 14:00

LunchSession 7: - Chairs: Pete Riley (virtual), Delores Knipp (in-person)
14:00 – 14:20

Sai Gowtam Valluri (University of Alaska)
An Artificial Neural Network-based global three-dimensional ionosphericelectron density model: present state, challenges, and future directions

14:20 – 14:40

Vivian Otugo (Rivers State University)
Estimation of ionospheric critical plasma frequencies from GNSS-TEC
measurements using artificial neural networks

14:40 – 15:00

Jhassmin A Aricoché (Universidad Nacional del Callao)
Modeling ionograms with deep neural networks: Contrasting models

15:00 – 15:30

Brianna Maze and Alec Engell (NextGen)
The Weather Machine Learning Platform and the Space Radiation Intelligence
System

15:30 – 17:30

Poster Session B (mostly in-person) – including coffee and refreshments

19:30

Social DinnerChampions Center – Rooftop Terrace Club, 2150 Stadium Dr, Boulder, 80302

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Friday 25th MarchSession 8 – Chairs: Enrico Camporeale (in-person), Tom Berger (in-person)
8:30 – 9:00

Early career awards

9:00 – 9:30

(invited) Maria J. Molina (NCAR, Boulder, Colorado)
Machine Learning for the Geosciences

9:30 – 9:50

Simon Mackovjak (Slovak Academy of Sciences)
Towards explanation of airglow variation by ML techniques

9:50 – 10:10

Shanshan Bao (Rice University)
A gray-box approach in modeling atmospheric precipitation in global
geospace models

10:10 – 10:30

Daniel I Okoh (Centre for Atmospheric Research)
Results from a 3-D electron density model developed from COSMIC radio
occultation data using artificial neural networks

10:30 – 11:00

Coffee11:00 – 11:20

Delores J Knipp (University of Colorado)
Geophysical interpretations from machine learning superstorm signature
identification in satellite precipitating particle data

11:20 – 11:40

Ekaterina Verner (NASA)
Heliophysics AI/ML focused program, AWS case study and crowdsourcing
result11:40 – 13:00

Open discussion

13:00

Farewell lunch (bag lunch available)

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List of posters (v=virtual; p=in person)
Session A (Tuesday 22)
1) Amy Keesee (p)Methods to improve magnitude accuracy for machine learning
predictions of ground magnetic field perturbations

2) Jasmine R Kobayashi (v) Machine Learning Models as an Alternative to Standard Interpolation
Techniques for Estimating Gaps in OMNI Data
3) Dattaraj B Dhuri (v)A deep learning model of proton auroras on Mars

4) Charles Topliff (p)A domain transfer approach to forecasting the Solar Wind at L1

5) Talha Siddique (v)A Bayesian Ensemble Machine Learning Approach For Prediction ofGeomagnetically Induced Currents (GICs) With UncertaintyQuantification

6) Adam T Michael (v)Radiation Belt Variability due to Wave-Particle Interactions: A
Multiscale Modeling Approach7) Spiridon Kasapis (p)Machine Learning-Based Forecasting of SEP Events Using theRecently Published MDI Data

8) Pete Riley (v)What Machine Learning Algorithms Teach us about Which
Explanatory Variables Matter Most in Predicting Bz within Coronal
Mass Ejections

9) Rainer Arlt (v)Machine learning for digitization of historical records of solar activity10) Gonzalo A Cucho-Padin (p) A machine learning framework for the reconstruction of the 3-D
ion density distributions and energetic fluxes in the Earth's cusp11) Jiahui Shan (v)Transfer learning for the three-dimensional reconstruction of CMEs

12) Xiukuan Zhao (v)Ionospheric scintillation prediction using gradient boosting algorithm

13) Kimberly D Moreland (p) A machine-learning oriented remote and in-situ database forforecasting SEP occurrence and properties
14) Anna L Morozova (v)Comparison of the performance of PCA-NN models for daily mean
TEC over the Iberian Peninsula: performance of different neuralnetworks configuration

15) Daniel T S Wrench (v)Exploring the potential of neural networks to predict statistics of solar
wind turbulence16) Raman Mukundan (p)Optimizing a Neural Network for Regional Forecasting of Ground
Magnetic Perturbations Using Spherical Elementary Current Systems17) Rukundo Wellen (v)Forecasting of ionospheric electron content (TEC) using a time series
neural network

18) Xiangning Chu (v)Relativistic Electron Model in the Outer Radiation Belt Using a
Neural Network Approach19) Luiz F Guedes dos Santos(p) Exploring the ability of Convolutional Neural Networks topredict Solar wind quantities at 1 AU20) Hannah T Rüdisser (v)Automatic Detection of Interplanetary Coronal Mass Ejections
721) Luisa Capannolo (v)Investigating the Relativistic Electron Precipitation using Deep
Learning Techniques22) Alexander Boyd (v)SHELLS Model: Specifying High-altitude Electrons using Low-altitude
LEO Systems

23) Kamen Kozarev (p)Towards Lucky Imaging for Quiet-Time Low-Frequency Radio SolarObservations24) Michael K Coughlan (v) Using a Convolutional Neural Network with Uncertainty to Forecast
GIC Risk of Occurrence at Mid-Latitudes.
25) Victor A Pinto (v)Developing near real-time ground magnetic field perturbations
predictions with machine learning models

26) Xudong Sun (v)SpIn4D: Spectropolarimetric Inversion in Four Dimensions with DeepLearning27) Kendra Bergstedt (p)Machine Learning Algorithms for the Detection of Plasmoids in
Multiple-X-Line Collisionless Reconnection Regions

28) Rong Lin (v)Predicting Ambient Solar Wind Speed at L1-point based on
Convolutional Neural Network and PFSS Magnetogram29) Mario Cobos Maestre (v) Stability of loss functions for solar wind forecasting using DeepLearning30) Armando Collado-Villaverde(v) Deep Neural Networks With Convolutional and LSTM Layers
for SYM-H and ASY-H Forecasting.
31) Andong Hu (p)A Multi-Hour-Ahead global geospace model using Gated Recurrent
Unit (GRU) networks and SuperMAG data

32) Suvadip Sinha (v)A comparative study of supervised machine learning algorithms toforecast solar flares

33) Tommaso Alberti (v)Chaos and spontaneous stochasticity: two sides of (un)predictability34) Juliana Vievering (p)Real-Time Solar Flare Predictions using Machine Learning35) Verena Heidrich-Meisner (v) Neural network reconstruction of in-situ solar wind parameters
36) Ute V Amerstorfer (v)Machine Learning Solutions for Data Analysis and Exploitation in
Planetary Science - A Work Package in Europlanet 2024 ResearchInfrastructure37) Ajay Kumar Tiwari (v)CME-learn: An interactive playground to benchmark CME databases
for the time of arrival (ToA) prediction for using machine learning
methods

38) Subhamoy Chatterjee (p) Forecasting the Occurrence Probability and Properties of Solar
Energetic Particle Events using a Multivariate Ensemble ofConvolutional Neural Networks
39) Donglai Ma (v)Machine learning based reconstruction and prediction of radiation
belt flux840) Enrico Camporeale (p)Space Weather with Quantified Uncertainty: Optimizing Ensembles for
Probabilistic Predictions41) Liam Smith (v)Machine Learning for Ionospheric Extrapolation and Forecasting in a
Data-Model Fusion Approach42) Kyle Domico (v)A Machine Learning and Computer Vision Approach to Geomagnetic
Storm Forecasting43) Dominique L Stumbaugh (p) Predicting Equatorial Electron Flux Measurements from Low
Earth Orbit
44) Yong Ji (v)Composite model for predicting sym-H index45) Matthew G Lennard (v) Machine Learning in Heliophysics46) Simon Bouriat (p)Forecasting low-energy particle flux in LEO using DMSP satellites:
data analysis and first results47) Emmanuel De Leon (v) Automatic detection of the electron density from the WHISPER
instrument onboard CLUSTER
Session B (Thursday 24)48) Luiz F Guedes dos Santos (p) Forecasting flux rope's orientation using CNNs
49) Henrik Eklund (v)Image refinement and estimation of intensity contrast degradation atsmall scales events of Solar observations.

50) Adeline T M Paiement (v) Cloud removal from ground-based images of the Sun51) Elizabeth P O'Dwyer (v) Machine Learning for the Classification of Low Frequency Extensionsof Saturn Kilometric Radiation
52) Carlos J Diaz Baso (p)Bayesian Stokes inversion with Normalizing flows53) Harry Arnold (v)Using Effective Resistivity Maps Derived From Data Mining for
Global MHD Simulations of the Magnetosphere54) Ajay Kumar Tiwari (v)Predicting Arrival Time and Arrival Speed for CMEs: Machine
Learning and Ensemble Methods55) Laura Simms (v)A comparison of ARMAX (autoregressive moving average transferfunction) and RNN (recurrent neural network) models to predictgeostationary keV electrons56) Yigit Aytac (p)A Computer Vision Approach for Real-time Solar Event Detection

57) Dogacan S Ozturk (v)A predictive model for the high-latitude ionospheric convection58) Aliaa A. M. Afify (v)Development of a forecasting technique for ionospheric plasma
irregularities by applying a supervised machine learning regression
technique to spaceborne GPS measurements59) Matthew Blandin (v)Predicting Geomagnetically Induced Currents across Alaska utilizing
Multi-Variate LSTM models

960) Robert Jarolim (p)ITI for the Sun: Improved intercalibration of multi-instrument
heliophysics data series with Instrument-To-Instrument translation

61) Reynaldo O Rojas Zelaya (v)Forecasting Spread F at Jicamarca62) Laura Simms (v)The use of differencing to remove spurious correlations in models ofgeostationary 2 MeV electron flux63) Naoto Nishizuka (v)Reliable Probability Forecast of Solar Flares using Deep NeuralNetworks64) Paul J Wright (p)SDOVIS: A Vision Transformer Model for Solar DynamicsObservatory (SDO) Data

65) Constantinos Papadimitriou (v) Application of information theoretical measures for improved
machine learning modelling of the electron radiation belt66) Hemapriya Raju (v)Deep learning analysis on CMEs assosciated with flares and filaments67) Ryan McGranaghan (v) A Next Generation Space Weather Particle Precipitation Model:
Mature machine learning approaches, multiscale mesoscaleprediction, and an open science framework for machine learning68) Andrea Diercke (p)Automatic Extraction of Solar Filaments Using Machine LearningTechniques69) Sumanth A.T. Rotti (v)Machine Learning Dataset of SEP Events from Solar Cycles 22, 23and 24.

70) Yana Shtyk (v)Solar flare prediction using a multi-channel model71) Pavithra G Srinivas (v)Development Of An Onboard Space Weather Module For SatelliteOperations

72) Denny Oliveira (p)Perspectives on the use of data assimilation for improving
thermospheric empirical models: Focus on extreme magnetic storms

73) Grant K Stephens (v)Global structure of magnetotail reconnection unveiled by mining
spaceborne magnetometer data74) Vanessa M Mercea (v)Detection of sunquakes in Egression Power Maps using Deep
Autoencoders75) Anthony Sciola (v)Ring current plasma pressure reconstructed from empirical magnetic
field distributions embedded within a global MHD model76) Ravindra T Desai (p)Using a neural network to model ultra-relativistic charged particles
and exploit sparse datasets

77) Sachin A Reddy (v)Predicting Equatorial Plasma Bubbles with Machine Learning andCubeSats78) Edward J E Brown (v)Attention-based machine vision models and techniques for solar windspeed forecasting using solar EUV images79) Stefano Bianco (v)A modification of the PINE model for real-time plasmasphericforecasts1080) Thomas Berger (p)Decreasing False Alarm Rates in ML-based Solar Flare Prediction
using SDO/HMI Data81) Shah Mohammad Bahauddin(v) Unboxing the Black Box: Learning to identify acoustic wave
sources on the Sun from Deep Learning82) Donglai Ma (v)Automatic discovery of the equations governing radiation beltdynamics

83) Saida Milena Diaz Castillo (v)Dense segmentation of solar granulation structures using deeplearning84) Harim Lee (p)Generation of Modern Satellite Data from Galileo Sunspot Drawings
by Deep Learning85) James Lende (v)Statistical Investigation of the Erosion and Refilling of thePlasmasphere - Neural Network Model Approach86) John C Dorelli (v)Vlasov Informed Super Resolution (VISR): A Deep Learning
Approach for De-Aliasing Particle Data

87) Xin Cao (v)Investigation of the response of equivalent ionospheric current toupstream solar wind and magnetospheric activity: a neural network
approach88) Subhamoy Chatterjee (p) Utilizing a Convolutional Neural Network to Efficiently Label a Solar
Flux Emergence Video Dataset89) Zeyu Sun(v)Predicting Solar Flares Using CNN and LSTM on Two Solar Cycles ofActive Region Data

90) Linnea Wolniewicz (v)SEARCH: SEgmentation of Active Regions and Coronal Holes91) Enrico Camporeale (p)The PRAISE Initiative: Promoting Research in Artificial Intelligence
for the Space Economy92) Kevin D Smith (v)Machine Learning Classification of Mercury Magnetospheric
Boundary Crossings93) Daniel E da Silva (p)Data

Semi-Empirical Data Compression for Heliophysics Space MissionSponsors

11Time difference calculator(Note: on this week USA is on Daylight Saving time, while Europe and Australia are still onwinter time)
Boulder
MDT (UTC -6)
8:00 am
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5:00 pmRome
CE T (UTC+1)
3:00 pm
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New Delhi
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7:30 pm
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12TokyoJST (UTC +9)
11:00 pm
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8:00 amSydneyAEDT (UTC +11)
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