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Ames Machine Learning Workshop Agenda

Moffett Conference Center, Building 3

29~31 August 2017

Goal: The goal of the workshop is to give NASA scientists, technologists, and program managers an understanding of the current state of the art in machine learning, and what machine learning can do for NASA science and engineering. The workshop will also explore what NASA’s role should be in advancing machine learning for its missions, and how NASA can engage with academia, industry, and other agencies in advancing machine learning. Breakout sessions will develop both specific ideas and programmatic recommendations, to be presented Thursday afternoon.

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August 29 Tuesday

8:30 am. Welcome and Introduction. Workshop Overview. (Steven Zornetzer and Michael Lowry) (Ballroom)

Workshop Presentation

Opening Keynote Session (Plenary - Ballroom)

9:00 - 10:00. Keynote. Industry Keynote. Peter Norvig, Director of Research, Google, Inc.

Peter Norvig Presentation

10:00 am - 10:15 am Break

10:15 am - 10:30 am. Academic Keynote. Vipin Kumar, Professor of Computer Science, University of Minnesota

Vipin Kumar Presentation

11:15 - 12:15 am. Nikunj Oza, Data Sciences Group Leader, NASA Ames Research Center

Nikunj Oza Presentation

12:15 pm - 1:30 pm . Lunch (On your own, Spacebar Café located in building)

1:30~4:00 pm. Machine Learning for Aeronautics (Ballroom)

  • 1:30 - 2:00 Deepak Kulkarni: Models of Weather Impact on Airspace Operations
  • 2:00 - 2:30 Heather Arneson: Analysis of convective weather impact on pre-departure routing of flights from Fort Worth Center to New York Center
    Heather Arneson Presentation
  • 2:30 - 3:00 Bryan Matthews: Approach to Assessing RNAV STAR Adherence
    Bryan Matthews Presentation
  • 3:00 - 3:30 Break (Snacks and Beverages in SpaceBar Café)
  • 3:30 - 4:00 Vijay Janakiraman: Discovering precursors to aviation safety incidents: Algorithms and Applications
    Vijay Janakiraman Presentation

1:30~4:00 pm. Machine Learning for Human Space Exploration (Showroom)

  • 1:30 - 2:00 Shawn Wolfe: The Inductive and Meta Monitoring Systems (IMS+MMS): Automated Monitoring Techniques for Complex and Autonomous Missions Operations Systems
    Shawn Wolfe Presentation
  • 2:00 - 2:30 David Thompson: Advances in Medical Analytics Solutions for Autonomous Medical Operations on Long-Duration Missions
    David Thompson Presentation
  • 2:30 - 3:00 Rodney Martin: Integrated Systems Health Management for Sustainable Habitats
    Rodney Martin Presentation
  • 3:00 - 3:30 Break (Snacks and Beverages in SpaceBar Café)
  • 3:30 - 4:00 Adrian Agogino: Machine Learning for Slow but Steady Interplanetary Construction
    Adrian Agogino Presentation

4:00 – 5:30 pm Breakout Groups Session 1. The objective of the breakout sessions is to explore and refine ideas for applying machine learning to NASA missions, and priorities for technology advances. Programmatic recommendations are encouraged. Breakout groups meet each day, and will be facilitated by NASA domain and technology experts. The recommendations will be presented Thursday afternoon. Aeronautics (Northwing), Astrophysics (Ballroom 1), Earth Science (Ballroom 2), Human Space (Showroom), Technology and Human Interaction (Mezzanine)

August 30 Wednesday

8:30 am ~9:30 am. HPC Keynote Piyush Mehrotra: HPC and Data Analysis at NASA Ames (Ballroom Plenary)
Piyush Mehrotra Presentation

9:30 am ~9:45 am. Break

9:45 am ~12:30 pm. Hardware, Program Synthesis, and V&V for Machine Learning (Ballroom)

9:45 am-12 pm. Learning for Human Machine Interaction (Showroom)

  • 9:45 - 10:45 Milind Tambe: AI for Social Good: The Role of Human-Machine Partnership
    Milind Tambe Presentation
  • 10:45 - 11:15 Karen Myers: Learning to Help Human Problem Solvers
    Karen Myers Presentation
  • 11:15 - 11:30 Break (Snacks and Beverages in SpaceBar Café)
  • 11:30 - 12:00 Kamalika Das: ASK-the-Expert: Active learning based knowledge discovery using the expert
    Kamalika Das Presentation
  • 12:00 - 12:30 Alonso Vera: What Machines Need to Learn to Support Human Problem-Solving

12:30 - 1:30 pm. Lunch (On your own, Spacebar Café located in building)

1:30 pm-4:30 pm. Machine Learning for Earth Science (Ballroom)

  • 1:30 - 2:00 Kamalika Das: Using Machine Learning to Study the Effects of Climate on the Amazon Rainforests
  • 2:00 - 2:30 Sangram Ganguly: Scaling Deep Learning Models to High Resolution Satellite Image Classification on the NASA Earth Exchange Platform
    Sangram Ganguly Presentation
  • 2:30 - 3:00 Grey Nearing: Toward Confirmatory Data Analytics for Process-Based Hydrology Models
    Grey Nearing Presentation
  • 3:00 - 3:30 Break (Snacks and Beverages in SpaceBar Café)
  • 3:30 - 4:00 James MacKinnon: Detecting Wildfires in MODIS data using Deep Neural Networks
    James MacKinnon Presentation
  • 4:00 - 4:30 Stefano Ermon: Artificial Intelligence for Sustainability
    Stefano Ermon Presentation

1:30 pm-4:30 pm. Machine Learning for Astrophysics/Planetary Science

  • 1:30 - 2:00 Hamed Valizadegan: Machine learning For Space Projects: Example Engineering and Science Case Studies
    Hamed Valizadegan Presentation
  • 2:00 - 2:30 Nick Kern: Surrogate Modeling Solutions for Cosmological Parameter Estimation of Future HI Radio Intensity Mapping Surveys
    Nick Kern Presentation
  • 2:30 - 3:00 Sean McGregor: Interdisciplinary Collaboration: Finding new Ways of Extracting Scientific Insights from Solar Observations
  • 3:00 - 3:30 Break (Snacks and Beverages in SpaceBar Café)
  • 3:30 - 4:00 Mark Cheung: Heliophysics: Data and Science
    Mark Cheung Presentation
  • 4:00 - 4:30 Madhulika Guhathakurta: Frontier Development Laboratory 2017
    Madhulika Guhathakurta Presentation

4:30 – 5:30 pm Breakout Groups Session 2. Same breakout group locations.


August 31 Thursday

8:30 am-9:30 am. Program Keynote.
Michael Little: "AIST: Investing in the Future of Earth Science; 20 Years Out”
Michael Little Presentation

9:30 am-10:00 am. Break

10:00 am-12:00 pm. Breakout Groups Session 3.
Same breakout group locations. Preparation of brief-out material.

12:00 pm - 1:30 pm. LUNCH

1:30-3:00 pm. Session brief-outs and recommendations towards future work in machine learning for NASA.
Session Brief-Outs Presentation

3:00 pm. Kai Goebel – Path Forward for Machine Learning at NASA
Kai Goebel Presentation