Working Group C

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Working Group C

Intelligent Systems and Machine Learning for Space Ground Systems


Adaptive, reliable automation and intelligent decision making are essential for the success of our space ground systems. One big challenge is migrating capabilities out of the lab to supporting critical mission operations. In the past, these approaches were often misunderstood, misapplied, too complex or costly to sustain, or insufficient for mission needs. Applied intelligent systems and machine learning technologies have begun to address this challenge through self-evolving, efficient, and value-focused capabilities. In addition, un-realized opportunities exist for applying established, or rapidly emerging technologies, solutions and architectures to the area of ground system space control and mission processing.This year of the GSAW “Intelligent Systems / Machine Learning for Space Ground Systems” working group will explore deeper the themes of:

  • Where do intelligent systems and machine learning currently exist in space ground systems?
  • What underlying parts of the space ground systems, enterprise and operations are suited to intelligent systems and machine learning?
  • What emerging capabilities and technologies are being developed in the community?
  • What are real-world impediments for adoption in operations?
  • What capability and technology gaps exist and might seed further research and investment?

Format of the Working Group
Part 1: Use Case Presentations. This portion will include a brief recap of definitions, concepts and misconceptions, and summary material from the previous year’s GSAW Intelligent System – Machine
Learning Working Group. This will be followed by a series of thought-provoking presentations and case studies on where intelligent systems and machine learning are currently adopted in space ground systems for the focus areas listed above. The purpose will be to seed the Working Group with real world, current examples of successes, failures, challenges, opportunities.

Part 2: Panel Discussion. A group of panelists will address topics covered in the survey and answer questions from the audience.

Desired Outcomes
The primary goal of the Working Group is to compile the results from the presentations, survey and panel discussion, to be out-briefed at end of the GSAW Workshop. The outcome should inform adopters, as well as informing stakeholders who are able to support research and development and help integrate intelligent systems into operations. A secondary goal is to establish an enduring community for long-term collaboration.

Leads Jon Neff and Max Spolaor, The Aerospace Corporation


Jon Neff is a data scientist and system engineer in the Civil Systems Group Artificial Intelligence, Analytics and Innovation Department at The Aerospace Corporation. He has worked on development and operations of several NASA robotic space missions and also has experience in software startups and medical devices. Most recently, he was Director, Risk Analytics at Visa. He has a Ph.D. in aerospace engineering from the University of Texas at Austin and an MBA from Pepperdine University.

Max Spolaor is a Senior Engineering Specialist in the Data Science and Artificial Intelligence Department at The Aerospace Corporation. He has a Ph.D. in Astrophysics, in addition to 10+ years of hands-on experience engineering scientific and space systems software applications. Most recently, he worked at the NASA IV&V Facility where he applied artificial intelligence and statistical modeling tools to assure the safety and success of software on NASA’s highest-profile missions. For his contributions to the field, he was awarded the NASA Project Achievement Award 2018 and 2020, the NASA Excellence in Leadership Award 2018, the NASA Excellence in Values Award 2018, and the NASA Group Achievement Award 2016.


Working Group C Outbrief
Jon Neff and Max Spolaor, The Aerospace Corporation
Artificial Intelligence Solution Architecting for the Solar Gravity Lens Mission
Jon Neff and Henry Helvajian, The Aerospace Corporation
Efficient out-of-distribution detection for reliable deployment of DNNs
Apoorva Sharma, Autonomous Systems Lab
SatNet: A Benchmark for Satellite Scheduling Optimization
Edwin Goh, Hamsa Shwetha Venkataram, Bharathan Balaji, Mark Johnston,  and Brian Wilson, NASA Jet Propulsion Laboratory / California Institute of Technology
Application of Unsupervised Deep Learning for Smoke Plume and Active Fire Identification
Erik Linstead1, Nick LaHaye1,2 , and Mike Garay2,
1Fowler School of Engineering, Chapman University
2Jet Propulsion Laboratory
Space Vehicle Onboard Cyber Defense using AI/ML
Nicholas Cohen, The Aerospace Corporation
How we Innovate Data Science at ASRC
Philip Feldman, ASRC Federal