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Presented by NASA Scientists
Sponsored by Snowflake
Calling all data enthusiasts and/or space nerds in Portland, Oregon and beyond. Join us for an exciting, all-day virtual event with NASA scientists. Attendees will work with actual NASA data and have an opportunity to collaborate on matters of data analysis directly with our presenters.
Opening Remarks: 8:30 AM: DAMA PDX Board
Morning Session: 9AM – 11:30 AM: Eric Lyness and Victoria Da-Poian, NASA
Eric Lyness is the NASA software and operations lead at Goddard Space Flight Center for the Mars Organic Molecule Analyzer on the ExoMars 2020 rover and Victoria Da-Poian is an Aerospace Engineer. They will present their talk, Machine Learning to Find Life on Mars and Beyond.
In 2021 the European ExoMars rover will land on Mars with the Mars Organic Molecular Analyzer (MOMA) laboratory to analyze soil samples searching for past or present life. NASA is developing machine learning algorithms to help the scientists more quickly analyze the data when it arrives from Mars. In this talk, we will present the current work using MOMA mass spectrometer data acquired during ground testing. Using this data we are aiding the scientists by matching new spectra with the most similar spectra from past experiments. We will present the nuances of mass spectra, our limitations with respect to data, and our approach to the problem. We hope to elicit feedback from the attendees.
More details at https://phys.org/news/2020-06-nasa-life-mars.html.
30 minute Break
Lunch Session: 12:00 – 1:00 PM: Snowflake Presentation, Drew Swanson and Brian Whittington
30 minute Break
Afternoon Session: 1:30 PM – 3:30PM: Detecting Wildfires in MODIS data using Deep Neural Networks with James MacKinnon, NASA
James MacKinnon is a computer engineer at the NASA Goddard Space Flight Center in the Science Data Processing branch. He received both his B.S. and M.S. at the University of Florida. His most recent work includes developing the payload processing FPGA design for the NASA-developed CeREs CubeSat, and being a principal investigator on an internal R&D project with the goal of designing a neural network for detecting wildfires from multispectral imagery. His expertise includes FPGA development for high-performance, space-based data processing systems, machine learning for science data, and reliable software design.
Wildfires are destructive to both life and property, which necessitates an approach to quickly and autonomously detect these events from orbital observatories. This talk will introduce a neural network based approach for classifying wildfires in MODIS multispectral data, and will show how it could be applied to a constellation of low-cost CubeSats. The approach combines training a deep neural network on the ground using high performance consumer GPUs, with a highly optimized inference system running on a flight-proven embedded processor. Normally neural networks execute on hardware orders of magnitude more powerful than anything found in a space-based computer, therefore the inference system is designed to be performant even on the most modest of platforms. This implementation is able to be significantly more accurate than previous neural network implementations, while also approaching the accuracy of the state-of-the-art MODFIRE data products.
- What methods can be used for a decision making process (classical expert system approach, ML approach) and to understand how the process works?
- How do we choose new data to collect with an engineering test unit on Earth?
15 minute Break
Closing Remarks and Next Steps: 3:45 – 4:00 PM: DAMA PDX Board
Times above may be revised in the days before our event.
Event will be presented via a Zoom bridge. Analytical discussions and collaboration will be hosted via a Slack workspace that will begin on event day and last for a few weeks afterwards. This is a BYOAE (bring your own analytical environment). However, attendees will have access to the data via Snowflake and to notebook templates via Zepl.
Pre-event tasks for analysis setup will be shared on 10/19 via email.
Date – Thursday, Oct. 22nd
Time – 8:30 – 4:00pm
NOTE: This is an all day event |
→ |
Presented by NASA Scientists
Sponsored by Snowflake
Calling all data enthusiasts and/or space nerds in Portland, Oregon and beyond. Join us for an exciting, all-day virtual event with NASA scientists. Attendees will work with actual NASA data and have an opportunity to collaborate on matters of data analysis directly with our presenters.
Opening Remarks: 8:30 AM: DAMA PDX Board
Morning Session: 9AM – 11:30 AM: Eric Lyness and Victoria Da-Poian, NASA
Eric Lyness is the NASA software and operations lead at Goddard Space Flight Center for the Mars Organic Molecule Analyzer on the ExoMars 2020 rover and Victoria Da-Poian is an Aerospace Engineer. They will present their talk, Machine Learning to Find Life on Mars and Beyond.
In 2021 the European ExoMars rover will land on Mars with the Mars Organic Molecular Analyzer (MOMA) laboratory to analyze soil samples searching for past or present life. NASA is developing machine learning algorithms to help the scientists more quickly analyze the data when it arrives from Mars. In this talk, we will present the current work using MOMA mass spectrometer data acquired during ground testing. Using this data we are aiding the scientists by matching new spectra with the most similar spectra from past experiments. We will present the nuances of mass spectra, our limitations with respect to data, and our approach to the problem. We hope to elicit feedback from the attendees.
More details at https://phys.org/news/2020-06-nasa-life-mars.html.
30 minute Break
Lunch Session: 12:00 – 1:00 PM: Snowflake Presentation, Drew Swanson and Brian Whittington
30 minute Break
Afternoon Session: 1:30 PM – 3:30PM: With James MacKinnon, NASA
James MacKinnon is a computer engineer at the NASA Goddard Space Flight Center in the Science Data Processing branch. He received both his B.S. and M.S. at the University of Florida. His most recent work includes developing the payload processing FPGA design for the NASA-developed CeREs CubeSat, and being a principal investigator on an internal R&D project with the goal of designing a neural network for detecting wildfires from multispectral imagery. His expertise includes FPGA development for high-performance, space-based data processing systems, machine learning for science data, and reliable software design.
15 minute Break
Closing Remarks and Next Steps: 3:45 – 4:00 PM: DAMA PDX Board
Times above may be revised in the days before our event.
Event will be presented via a Zoom bridge. Analytical discussions and collaboration will be hosted via a Slack workspace that will begin on event day and last for a few weeks afterwards. This is a BYOAE (bring your own analytical environment). However, attendees will have access to the data via Snowflake and to notebook templates via Zepl.
Pre-event tasks for analysis setup will be shared on 10/19 via email.
Date – Thursday, Oct. 22nd
Time – 8:30 – 4:00pm
NOTE: This is an all day event |