Expert Workshop on Advancing Application of ML Tools for NASA EO Data: Full Schedule

Radiant Earth Foundation is hosting an international expert workshop to discuss how best to use machine learning (ML) techniques on NASA’s Earth Observation (EO) data and address environmental challenges. In particular, generation and usage of training datasets for ML applications using EO will be discussed. Participants of the workshop will evaluate recent advancements, identify existing obstacles and develop a best practices guideline to enhance the adoption of these techniques.

This workshop is sponsored by the NASA Earth Science Data Systems (ESDS) program.

8:00am EST

Registration and Meet & Greet

Join us bright and early to grab something to eat, a coffee or tea, get your name badge, and network with new colleagues and old friends!

If you are staying at the Cosmos Club, please note that your rate includes breakfast. We ask that you take advantage of that complimentary breakfast before heading to the Crentz Room (the main meeting room.)

Tuesday January 21, 2020 8:00am - 9:00am EST
Cosmos Club (Crentz Room)

Networking, Breakfast

9:00am EST

Welcome & Introductions

Welcome remarks, participant introductions, and workshop logistics.

Speakers

Anne Miglarese

CEO, Radiant Earth Foundation

Anne Hale Miglarese is the Founder and CEO of Radiant.Earth, a non-profit organization working to aggregate the world’s open Earth imagery and providing access and education on its use to the global development community. Prior to launching Radiant.Earth, Ms Miglarese served as... Read More →

Kevin Murphy

Programme Executive, ESDS Program, NASA

Kevin Murphy is the Program Executive for Earth Science Data Systems within NASA’s Earth Science Division.

Tuesday January 21, 2020 9:00am - 9:20am EST
Cosmos Club (Crentz Room)

Plenary

9:20am EST

Morning Plenary - Presentations (1)

Workshop introductions, objectives and logistics.

Speakers

Manil Maskey

Senior Research Scientist/Data Science Lead OCSDO, NASA

Hamed Alemohammad

Executive Director and Chief Data Scientist, Radiant Earth Foundation

Subit Chakrabarti

Data Scientist, Indigo Agriculture

I develop novel spatio-temporal machine learning methods applicable for large-scale earth imagery. I work in the Geoinnovation team at Indigo Agriculture and am the chair of Young Professionals activities in the IEEE Geoscience and Remote Sensing Society.

Lyndon Estes

Associate Professor, Clark University

Pierre Gentine

Associate Professor, Columbia University

Tuesday January 21, 2020 9:20am - 10:30am EST
Cosmos Club (Crentz Room)

Plenary

10:30am EST

Break

Tuesday January 21, 2020 10:30am - 11:00am EST
Cosmos Club (Crentz Room)

Networking, Coffee and Snacks

11:00am EST

Lightning Presentations (1)

Speakers

Dalton Lunga

Lead Machine Learning Scientist, Oak Ridge National Laboratory

May Casterline

Senior Data Scientist, NVIDIA

Dr. May Casterline is a data scientist/image scientist/software developer with a background in satellite and airborne imaging systems. Her research interests include deep learning, hyperspectral and multispectral imaging, innovative applications of machine learning approaches to remote... Read More →

Ilkay Altintas

Chief Data Science Officer, UC San Diego, San Diego Supercomputer Center

Andi Gros

Data Scientist, Facebook

I am a scientist with a background in complex systems on Facebook’s core data science team. I currently work on machine learning problems to tackle large quantities of textual data (topic modeling). I also work on spatial demographic questions for internet.org. For my PhD I studied... Read More →

Steven Brumby

Senior Director Geographic Visualization, National Geographic Society

Zhuangfang NaNa Yi

Machine Learning Engineer, Development Seed

Gencer Sumbul

Research Associate & PhD Candidate, Technische Universität Berlin

Daniel Hogan

Data Scientist, IQT CosmiQ Works

Tuesday January 21, 2020 11:00am - 12:30pm EST
Cosmos Club (Crentz Room)

Plenary, Coffee and Snacks

12:30pm EST

Lunch

Tuesday January 21, 2020 12:30pm - 1:30pm EST
Cosmos Club (Crentz Room)

Networking, Lunch

1:30pm EST

Lightning Presentations (2)

Speakers

Ryan Abernathey

Associate Professor, Columbia University

Ryan P. Abernathey, an Associate Professor of Earth And Environmental Science at Columbia University and Lamont Doherty Earth Observatory, is a physical oceanographer who studies large-scale ocean circulation and its relationship with Earth's climate. He received his Ph.D. from MIT... Read More →

Joe Hamman

Scientist, NCAR

Anu Swatantran

Remote Sensing Data Science R&D Lead, Corteva Agriscience

Sherrie Wang

PhD Student, Stanford University

Machine learning + food security

Hannah Kerner

Assistant Research Professor, NASA Harvest/University of Maryland

Olha Danylo

Research Scholar, International Institute for Applied Systems Analysis

Tuesday January 21, 2020 1:30pm - 3:00pm EST
Cosmos Club (Crentz Room)

Plenary

3:00pm EST

Break

Tuesday January 21, 2020 3:00pm - 3:30pm EST
Cosmos Club (Crentz Room)

Networking, Coffee and Snacks

3:30pm EST

Breakout Sessions Introductions

Speakers

Hamed Alemohammad

Executive Director and Chief Data Scientist, Radiant Earth Foundation

Tuesday January 21, 2020 3:30pm - 4:00pm EST
Cosmos Club (Crentz Room)

Plenary

4:00pm EST

Breakout Session 1 (WG 1): Training data generation and accounting for errors/uncertainties

Proposed Questions:

How to define, measure, and document uncertainty in training data?
How to treat sparsity in training data? If the data lacks spatial or temporal completeness, what are the ways to augment the data?
How to decide on the size of training data required for a problem? This question cannot be answered in advance of building a model. But what steps should be followed to understand if the sample size is reasonable?
How to understand and quantify representativeness and class balance/imbalance of training data? What are the metrics to assess geographical diversity and representativeness of training data?
What are the requirements for compiling “benchmark training datasets” to advance model developments in each science discipline? For example, if the community is building a pollution estimation model, which is going to be integrated into a larger climate model and be integrated with CMIP comparisons, how can we benchmark the training data for this model and progressively improve it over time?
How do we deal with class imbalance issues in training data for Earth science machine learning classifications?
From the Earth Science Data Systems (sponsor) perspective how can we leverage the wealth of data for training models? E.g. sometimes multiple data sources can be fused to create a labeled dataset without a manual process.
What are the recommendations to map gaps in training data catalogs (based on science discipline or application area)?

Moderator(s)

Lyndon Estes

Associate Professor, Clark University

Tuesday January 21, 2020 4:00pm - 5:00pm EST
Cosmos Club (Crentz Room)

Breakout Sessions, Working Group 1

4:00pm EST

Breakout Session 1 (WG2): Modeling approaches and best practices for building the best and computationally optimum model

Proposed Questions:

Unlike common ML problems, Earth systems have a probabilistic nature, therefore we need to incorporate this property in our modeling frameworks and develop uncertainty aware models. What are the latest advancements in this direction, and what are the recommendations for new research ideas in this domain?
Multispectral data has more than 3 bands (unlike typical computer vision data) and has a temporal dimension which is very informative for many modeling efforts. What are the best practices in incorporating these data features into ML modeling?
Projections, spatial grids, and temporal revisits are almost never the same across two datasets. How can we as a community enable better interoperability between these data (that sometimes need to be input to a model)? And what are the considerations in building models when doing re-projection or spatial/temporal interpolation?
What are the considerations for model development when transitioning from R&D to production:
1. How frequently should we re-train models?
2. Content drift?
3. Back-testing, Now-testing?
How can ML models be used to improve specifics of measurement instruments? What are the possibilities for using existing training data and modeling frameworks to inform new data collection strategies? Could that lead to improved accounting of uncertainties in ML applications?

Moderator(s)

Dalton Lunga

Lead Machine Learning Scientist, Oak Ridge National Laboratory

Tuesday January 21, 2020 4:00pm - 5:00pm EST
Cosmos Club (Board Room)

Breakout Sessions, Working Group 2

4:00pm EST

Breakout Session 1 (WG3): Best practices for sharing and publishing ML applications (model, training data, and results)

Proposed Questions:

What are the requirements and best practices for documenting and dissemination of training datasets to ensure reproducibility?
What are the resources/repositories that researchers and practitioners can use to register and document training data? This should enable them to assign a DOI to the dataset.
What are the recommended data formats for training data? e.g. traditionally earth system data are stored in NetCDF format, but they are not cloud-friendly and ML-ready.
How to share processed input data? In many cases, the input data are pre-processed and then used as features in developing an ML model. These data sometimes come from huge datasets like re-analysis products. What are the requirements to share the training data in these scenarios to not duplicate the storage of the input data but ensure reproducibility?
What are the tools (if any) for discovering and accessing training data on the cloud environments? If not enough, what are the requirements for such a tool?

Moderator(s)

Hamed Alemohammad

Executive Director and Chief Data Scientist, Radiant Earth Foundation

Tuesday January 21, 2020 4:00pm - 5:00pm EST
Cosmos Club (Taft Room) 2121 Massachusetts Ave NW, Washington, DC 20008, USA

Breakout Sessions, Working Group 3

5:00pm EST

Reception

Tuesday January 21, 2020 5:00pm - 8:00pm EST
Members Dining Room

Networking

8:00am EST

Meet & Greet

Join us bright and early to grab something to eat, a coffee or tea, get your name badge, and network with new colleagues and old friends!

If you are staying at the Cosmos Club, please note that your rate includes breakfast. We ask that you take advantage of that complimentary breakfast before heading to the Crentz Room (the main meeting room.)

Wednesday January 22, 2020 8:00am - 9:00am EST
Cosmos Club (Crentz Room)

Networking, Breakfast

9:00am EST

Report of Day 1

Speakers

Hamed Alemohammad

Executive Director and Chief Data Scientist, Radiant Earth Foundation

Lyndon Estes

Associate Professor, Clark University

Dalton Lunga

Lead Machine Learning Scientist, Oak Ridge National Laboratory

Wednesday January 22, 2020 9:00am - 9:30am EST
Cosmos Club (Crentz Room)

Plenary

9:30am EST

Lightning Presentations (3)

Participants presentations.

Speakers

Patrick Grimont

Head of Copernicus GS services, ESA

Joe Flasher

Open Geospatial Data Lead, Amazon Web Services

Joe Flasher is the Open Geospatial Data Lead at Amazon Web Services helping organizations most effectively make data available for analysis in the cloud. The AWS open data program has democratized access to petabytes of data, including satellite imagery, genomic data, and data used... Read More →

Alex Leith

Assistant Director, Geoscience Australia

Peter J. Doucette

Lead, Science and Applications, USGS / EROS

Benjamin Goldenberg

Engineering Manager, ML Infrastructure, Planet

Richard Choularton

Director, Agriculture and Economic Growth, Tetra Tech

Wednesday January 22, 2020 9:30am - 10:30am EST
Cosmos Club (Crentz Room)

Plenary

10:30am EST

Break

Wednesday January 22, 2020 10:30am - 11:00am EST
Cosmos Club (Crentz Room)

Networking, Coffee and Snacks

11:00am EST

Lightning Presentations (4)

Participants presentations.

Speakers

Murali Gumma

Head – Remote Sensing / GIS Lab, ISD, ICRISAT

Murali Gumma leads the RS-GIS unit, and conducts research in spatial aspects of agricultural research providing spatial dimension to almost all the components of agriculture. Prior to this he was a Remote Sensing Specialist and Post-doctoral fellow at IRRI. Before that he was a project... Read More →

Tasso Azevedo

General Coordinator, MapBiomas

Christopher Brown

Engineer, Google

Brookie Guzder-Williams

Director of Data Science, World Resources Institute

Konrad Wessels

Assistant Professor, George Mason University

Remote Sensing expert.

Caleb Robinson

PhD Student, Georgia Institute of Technology

Lewis Fishgold

Software Engineer, Azavea

I am a software engineer at Azavea where I build tools for applying machine learning to geospatial imagery such as Raster Vision (https://github.com/azavea/raster-vision), and develop custom solutions using these tools for a variety of clients.

Wednesday January 22, 2020 11:00am - 12:30pm EST
Cosmos Club (Crentz Room)

Plenary

12:30pm EST

Lunch

Wednesday January 22, 2020 12:30pm - 1:30pm EST
Cosmos Club (Crentz Room)

Networking, Lunch

1:30pm EST

Breakout Session 2 (WG1): Training data generation and accounting for errors/uncertainties

Proposed Questions:

How to define, measure, and document uncertainty in training data?
How to treat sparsity in training data? If the data lacks spatial or temporal completeness, what are the ways to augment the data?
How to decide on the size of training data required for a problem? This question cannot be answered in advance of building a model. But what steps should be followed to understand if the sample size is reasonable?
How to understand and quantify representativeness and class balance/imbalance of training data? What are the metrics to assess geographical diversity and representativeness of training data?
What are the requirements for compiling “benchmark training datasets” to advance model developments in each science discipline? For example, if the community is building a pollution estimation model, which is going to be integrated into a larger climate model and be integrated with CMIP comparisons, how can we benchmark the training data for this model and progressively improve it over time?
How do we deal with class imbalance issues in training data for Earth science machine learning classifications?
From the Earth Science Data Systems (sponsor) perspective how can we leverage the wealth of data for training models? E.g. sometimes multiple data sources can be fused to create a labeled dataset without a manual process.
What are the recommendations to map gaps in training data catalogs (based on science discipline or application area)?

Moderator(s)

Lyndon Estes

Associate Professor, Clark University

Wednesday January 22, 2020 1:30pm - 3:00pm EST
Cosmos Club (Crentz Room)

Breakout Sessions, Working Group 1

1:30pm EST

Breakout Session 2 (WG2): Modeling approaches and best practices for building the best and computationally optimum model

Proposed Questions:

Unlike common ML problems, Earth systems have a probabilistic nature, therefore we need to incorporate this property in our modeling frameworks and develop uncertainty aware models. What are the latest advancements in this direction, and what are the recommendations for new research ideas in this domain?
Multispectral data has more than 3 bands (unlike typical computer vision data) and has a temporal dimension which is very informative for many modeling efforts. What are the best practices in incorporating these data features into ML modeling?
Projections, spatial grids, and temporal revisits are almost never the same across two datasets. How can we as a community enable better interoperability between these data (that sometimes need to be input to a model)? And what are the considerations in building models when doing re-projection or spatial/temporal interpolation?
What are the considerations for model development when transitioning from R&D to production:
1. How frequently should we re-train models?
2. Content drift?
3. Back-testing, Now-testing?
How can ML models be used to improve specifics of measurement instruments? What are the possibilities for using existing training data and modeling frameworks to inform new data collection strategies? Could that lead to improved accounting of uncertainties in ML applications?

Moderator(s)

Dalton Lunga

Lead Machine Learning Scientist, Oak Ridge National Laboratory

Wednesday January 22, 2020 1:30pm - 3:00pm EST
Cosmos Club (Board Room)

Breakout Sessions, Working Group 2

1:30pm EST

Breakout Session 2 (WG3): Best practices for sharing and publishing ML applications (model, training data, and results)

Proposed Questions:

What are the requirements and best practices for documenting and dissemination of training datasets to ensure reproducibility?
What are the resources/repositories that researchers and practitioners can use to register and document training data? This should enable them to assign a DOI to the dataset.
What are the recommended data formats for training data? e.g. traditionally earth system data are stored in NetCDF format, but they are not cloud-friendly and ML-ready.
How to share processed input data? In many cases, the input data are pre-processed and then used as features in developing an ML model. These data sometimes come from huge datasets like re-analysis products. What are the requirements to share the training data in these scenarios to not duplicate the storage of the input data but ensure reproducibility?
What are the tools (if any) for discovering and accessing training data on the cloud environments? If not enough, what are the requirements for such a tool?

Moderator(s)

Hamed Alemohammad

Executive Director and Chief Data Scientist, Radiant Earth Foundation

Wednesday January 22, 2020 1:30pm - 3:00pm EST
Cosmos Club (Taft Room) 2121 Massachusetts Ave NW, Washington, DC 20008, USA

Breakout Sessions, Working Group 3

3:00pm EST

Break

Wednesday January 22, 2020 3:00pm - 3:30pm EST
Cosmos Club (Crentz Room)

Networking, Coffee and Snacks

3:30pm EST

Breakout Sessions Reporting

Speakers

Hamed Alemohammad

Executive Director and Chief Data Scientist, Radiant Earth Foundation

Lyndon Estes

Associate Professor, Clark University

Dalton Lunga

Lead Machine Learning Scientist, Oak Ridge National Laboratory

Wednesday January 22, 2020 3:30pm - 4:00pm EST
Cosmos Club (Crentz Room)

Plenary

4:00pm EST

Breakout Session 3 (WG 1): Training data generation and accounting for errors/uncertainties

Proposed Questions:

How to define, measure, and document uncertainty in training data?
How to treat sparsity in training data? If the data lacks spatial or temporal completeness, what are the ways to augment the data?
How to decide on the size of training data required for a problem? This question cannot be answered in advance of building a model. But what steps should be followed to understand if the sample size is reasonable?
How to understand and quantify representativeness and class balance/imbalance of training data? What are the metrics to assess geographical diversity and representativeness of training data?
What are the requirements for compiling “benchmark training datasets” to advance model developments in each science discipline? For example, if the community is building a pollution estimation model, which is going to be integrated into a larger climate model and be integrated with CMIP comparisons, how can we benchmark the training data for this model and progressively improve it over time?
How do we deal with class imbalance issues in training data for Earth science machine learning classifications?
From the Earth Science Data Systems (sponsor) perspective how can we leverage the wealth of data for training models? E.g. sometimes multiple data sources can be fused to create a labeled dataset without a manual process.
What are the recommendations to map gaps in training data catalogs (based on science discipline or application area)?

Moderator(s)

Lyndon Estes

Associate Professor, Clark University

Wednesday January 22, 2020 4:00pm - 5:00pm EST
Cosmos Club (Crentz Room)

Breakout Sessions, Working Group 1

4:00pm EST

Breakout Session 3 (WG2): Modeling approaches and best practices for building the best and computationally optimum model

Proposed Questions:

Unlike common ML problems, Earth systems have a probabilistic nature, therefore we need to incorporate this property in our modeling frameworks and develop uncertainty aware models. What are the latest advancements in this direction, and what are the recommendations for new research ideas in this domain?
Multispectral data has more than 3 bands (unlike typical computer vision data) and has a temporal dimension which is very informative for many modeling efforts. What are the best practices in incorporating these data features into ML modeling?
Projections, spatial grids, and temporal revisits are almost never the same across two datasets. How can we as a community enable better interoperability between these data (that sometimes need to be input to a model)? And what are the considerations in building models when doing re-projection or spatial/temporal interpolation?
What are the considerations for model development when transitioning from R&D to production:
1. How frequently should we re-train models?
2. Content drift?
3. Back-testing, Now-testing?
How can ML models be used to improve specifics of measurement instruments? What are the possibilities for using existing training data and modeling frameworks to inform new data collection strategies? Could that lead to improved accounting of uncertainties in ML applications?

Moderator(s)

Dalton Lunga

Lead Machine Learning Scientist, Oak Ridge National Laboratory

Wednesday January 22, 2020 4:00pm - 5:00pm EST
Cosmos Club (Board Room)

Breakout Sessions, Working Group 2

4:00pm EST

Breakout Session 3 (WG3): Best practices for sharing and publishing ML applications (model, training data, and results)

Proposed Questions:

What are the requirements and best practices for documenting and dissemination of training datasets to ensure reproducibility?
What are the resources/repositories that researchers and practitioners can use to register and document training data? This should enable them to assign a DOI to the dataset.
What are the recommended data formats for training data? e.g. traditionally earth system data are stored in NetCDF format, but they are not cloud-friendly and ML-ready.
How to share processed input data? In many cases, the input data are pre-processed and then used as features in developing an ML model. These data sometimes come from huge datasets like re-analysis products. What are the requirements to share the training data in these scenarios to not duplicate the storage of the input data but ensure reproducibility?
What are the tools (if any) for discovering and accessing training data on the cloud environments? If not enough, what are the requirements for such a tool?

Moderator(s)

Hamed Alemohammad

Executive Director and Chief Data Scientist, Radiant Earth Foundation

Wednesday January 22, 2020 4:00pm - 5:00pm EST
Cosmos Club (Taft Room) 2121 Massachusetts Ave NW, Washington, DC 20008, USA

Breakout Sessions, Working Group 3

8:00am EST

Meet & Greet

Join us bright and early to grab something to eat, a coffee or tea, get your name badge, and network with new colleagues and old friends!

If you are staying at the Cosmos Club, please note that your rate includes breakfast. We ask that you take advantage of that complimentary breakfast before heading to Crentz Room (the main meeting room.)

Thursday January 23, 2020 8:00am - 9:00am EST
Cosmos Club (Crentz Room)

Networking, Breakfast

9:00am EST

Report of Day 2

Thursday January 23, 2020 9:00am - 9:30am EST
Cosmos Club (Crentz Room)

Plenary

9:30am EST

Breakout Session 4 (WG 1): Training data generation and accounting for errors/uncertainties

Proposed Questions:

How to define, measure, and document uncertainty in training data?
How to treat sparsity in training data? If the data lacks spatial or temporal completeness, what are the ways to augment the data?
How to decide on the size of training data required for a problem? This question cannot be answered in advance of building a model. But what steps should be followed to understand if the sample size is reasonable?
How to understand and quantify representativeness and class balance/imbalance of training data? What are the metrics to assess geographical diversity and representativeness of training data?
What are the requirements for compiling “benchmark training datasets” to advance model developments in each science discipline? For example, if the community is building a pollution estimation model, which is going to be integrated into a larger climate model and be integrated with CMIP comparisons, how can we benchmark the training data for this model and progressively improve it over time?
How do we deal with class imbalance issues in training data for Earth science machine learning classifications?
From the Earth Science Data Systems (sponsor) perspective how can we leverage the wealth of data for training models? E.g. sometimes multiple data sources can be fused to create a labeled dataset without a manual process.
What are the recommendations to map gaps in training data catalogs (based on science discipline or application area)?

Moderator(s)

Lyndon Estes

Associate Professor, Clark University

Thursday January 23, 2020 9:30am - 11:00am EST
Cosmos Club (Crentz Room)

Breakout Sessions, Working Group 1

9:30am EST

Breakout Session 4 (WG2): Modeling approaches and best practices for building the best and computationally optimum model

Proposed Questions:

Unlike common ML problems, Earth systems have a probabilistic nature, therefore we need to incorporate this property in our modeling frameworks and develop uncertainty aware models. What are the latest advancements in this direction, and what are the recommendations for new research ideas in this domain?
Multispectral data has more than 3 bands (unlike typical computer vision data) and has a temporal dimension which is very informative for many modeling efforts. What are the best practices in incorporating these data features into ML modeling?
Projections, spatial grids, and temporal revisits are almost never the same across two datasets. How can we as a community enable better interoperability between these data (that sometimes need to be input to a model)? And what are the considerations in building models when doing re-projection or spatial/temporal interpolation?
What are the considerations for model development when transitioning from R&D to production:
1. How frequently should we re-train models?
2. Content drift?
3. Back-testing, Now-testing?
How can ML models be used to improve specifics of measurement instruments? What are the possibilities for using existing training data and modeling frameworks to inform new data collection strategies? Could that lead to improved accounting of uncertainties in ML applications?

Moderator(s)

Dalton Lunga

Lead Machine Learning Scientist, Oak Ridge National Laboratory

Thursday January 23, 2020 9:30am - 11:00am EST
Cosmos Club (Board Room)

Breakout Sessions, Working Group 2

9:30am EST

Breakout Session 4 (WG3): Best practices for sharing and publishing ML applications (model, training data, and results)

Proposed Questions:

What are the requirements and best practices for documenting and dissemination of training datasets to ensure reproducibility?
What are the resources/repositories that researchers and practitioners can use to register and document training data? This should enable them to assign a DOI to the dataset.
What are the recommended data formats for training data? e.g. traditionally earth system data are stored in NetCDF format, but they are not cloud-friendly and ML-ready.
How to share processed input data? In many cases, the input data are pre-processed and then used as features in developing an ML model. These data sometimes come from huge datasets like re-analysis products. What are the requirements to share the training data in these scenarios to not duplicate the storage of the input data but ensure reproducibility?
What are the tools (if any) for discovering and accessing training data on the cloud environments? If not enough, what are the requirements for such a tool?

Moderator(s)

Hamed Alemohammad

Executive Director and Chief Data Scientist, Radiant Earth Foundation

Thursday January 23, 2020 9:30am - 11:00am EST
Cosmos Club (Taft Room) 2121 Massachusetts Ave NW, Washington, DC 20008, USA

Breakout Sessions, Working Group 3

11:00am EST

Break

Thursday January 23, 2020 11:00am - 11:30am EST
Cosmos Club (Crentz Room)

Networking, Coffee and Snacks

12:00pm EST

Wrap up & Adjourn

Thursday January 23, 2020 12:00pm - 12:30pm EST
Cosmos Club (Crentz Room)

Plenary

12:30pm EST

Lunch

Thursday January 23, 2020 12:30pm - 1:30pm EST
Cosmos Club (Crentz Room)

Networking, Lunch