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Suggested Citation:"Appendix B: Workshop Agenda." National Academies of Sciences, Engineering, and Medicine. 2017. Challenges in Machine Generation of Analytic Products from Multi-Source Data: Proceedings of a Workshop. Washington, DC: The National Academies Press. doi: 10.17226/24900.
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B

Workshop Agenda

AUGUST 9-10, 2017

The Keck Center of the National Academies of
Sciences, Engineering, and Medicine
Washington, D.C.

Objectives

Research Challenges:

  1. Machine-based methods for generating analytic products.
  2. Machine-based methods for automating the evaluation of analytic products.

Research Questions:

  1. What are the technical objectives and metrics needed for success?
  2. What are the primary issues?
  3. What are the current and “next level” key performance metrics?
  4. What is the “level after next” of expected research and development performance?
  5. What is the research knowledge base?
  6. How can the government best prepare the scientific workforce to enhance discovery in this area?
  7. What are the requisite enabling technologies?

Day 1: August 9, 2017

7:30 A.M. Registration and breakfast (on your own)

SESSION 1: Plenary

8:00 Sponsor Remarks and Expectations of the Workshop
Dr. David M. Isaacson, ODNI
8:15 Generation of Capability Technology Matrix
Dr. Rama Chellappa, UMCP, Planning Committee Chair
Dr. George Coyle, RSO, AFSB/ICSB
Suggested Citation:"Appendix B: Workshop Agenda." National Academies of Sciences, Engineering, and Medicine. 2017. Challenges in Machine Generation of Analytic Products from Multi-Source Data: Proceedings of a Workshop. Washington, DC: The National Academies Press. doi: 10.17226/24900.
×
8:30 Progress in Machine Learning
Dr. Tom Dietterich, Oregon State University
9:05 Industry Perspective
Dr. Josyula R. Rao, Watson IBM Fellow
9:45 Operational Perspective—Project MAVEN
Dr. Travis W. Axtell, OSD OUSD (I)
10:25 Break

SESSION 2: Machine Learning from Image/Video/Map Data

10:45 Learning from Overhead Imagery
Dr. Joe Mundy, Vision Systems, Inc.
11:20 Deep Learning for Learning from Images and Videos: Is It Real?
Dr. Rama Chellappa, UMCP
11:55 Learning about Human Activities from Images and Videos
Dr. Anthony Hoogs, Kitware, Inc.
12:30 P.M. Lunch

SESSION 3: Machine Learning from Natural Languages (ML-NLP)

1:15 Machine Learning from Text: Applications
Dr. Kathy McKeown, Columbia University
1:50 Deep Learning for NLP
Dr. Dragomir Radev, Yale University
2:25 Machine Learning from Conversational Speech
Dr. Amanda Stent, Bloomberg
3:00 Break

SESSION 4: Learning from Multi-Source Data

3:15 Situational Awareness from Multiple Unstructured Sources
Dr. Boyan Onyshkevych, DARPA
3:50 Discussion on Preparing the Capability Matrix Compile enabling technologies from 1st Day
5:30 Adjourn
Suggested Citation:"Appendix B: Workshop Agenda." National Academies of Sciences, Engineering, and Medicine. 2017. Challenges in Machine Generation of Analytic Products from Multi-Source Data: Proceedings of a Workshop. Washington, DC: The National Academies Press. doi: 10.17226/24900.
×

Day 2: August 10, 2017

7:30 A.M. Breakfast in cafeteria (on your own)
8:00 Sponsor Remarks
Dr. David Honey, Director of Science & Technology, ODNI

SESSION 5: Learning from Noisy, Adversarial Inputs

8:15 Harnessing Machine Learning for Global Discovery at Scale
Dr. Mikel Rodriguez, MITRE

SESSION 6: Learning from Social Media

8:50 Large-Scale Multi-Modal Deep Learning
Dr. Rob Fergus, NYU
9:25 What Can We Learn from Social Media Posts?
(Presentation withdrawn)
10:00 Break

SESSION 7: Humans and Machines Working Together with Big Data

10:15 Sensemaking Systems and Models
Dr. Peter Pirolli, Institute for Human and Machine Cognition
10:50 Crowd Sourcing for Natural Language Processing
Dr. Chris Callison-Burch, University of Pennsylvania

SESSION 8: Use of Machine Learning for Privacy Ethics

11:25 Toward Socio-Cultural Machine Learning
Dr. Mark Riedl, Georgia Institute of Technology
12:00 P.M. Lunch

SESSION 9: Panel on Evaluation of Machine-Generated Products

1:00 Dr. Anthony Hoogs, Kitware
Dr. Jason Duncan, MITRE
Mr. Jonathan Fiscus, NIST
Dr. Rob Fergus, NYU
2:00 Break
Suggested Citation:"Appendix B: Workshop Agenda." National Academies of Sciences, Engineering, and Medicine. 2017. Challenges in Machine Generation of Analytic Products from Multi-Source Data: Proceedings of a Workshop. Washington, DC: The National Academies Press. doi: 10.17226/24900.
×

SESSION 10: Capability Technology Matrix Panel: NSF, DoD, NIST, DoE

2:10 Machine Learning for Energy Applications
Dr. Devanand Shenoy, DOE
2:30 Using Metrology to Improve Access to “Unstructured” Data
Dr. Ellen Voorhees, NIST
2:50 Challenge Problems for Multi-Source Insights
Dr. Travis W. Axtell, OSD OUSD (I)
3:10 An Overview of NSF Research in Data Analytics
Mr. James Donlon, NSF
3:30 Discussion on Preparing the Capability Matrix
Compile enabling technologies from 2nd day
Complete matrix
5:00 Adjourn
Suggested Citation:"Appendix B: Workshop Agenda." National Academies of Sciences, Engineering, and Medicine. 2017. Challenges in Machine Generation of Analytic Products from Multi-Source Data: Proceedings of a Workshop. Washington, DC: The National Academies Press. doi: 10.17226/24900.
×
Page 48
Suggested Citation:"Appendix B: Workshop Agenda." National Academies of Sciences, Engineering, and Medicine. 2017. Challenges in Machine Generation of Analytic Products from Multi-Source Data: Proceedings of a Workshop. Washington, DC: The National Academies Press. doi: 10.17226/24900.
×
Page 49
Suggested Citation:"Appendix B: Workshop Agenda." National Academies of Sciences, Engineering, and Medicine. 2017. Challenges in Machine Generation of Analytic Products from Multi-Source Data: Proceedings of a Workshop. Washington, DC: The National Academies Press. doi: 10.17226/24900.
×
Page 50
Suggested Citation:"Appendix B: Workshop Agenda." National Academies of Sciences, Engineering, and Medicine. 2017. Challenges in Machine Generation of Analytic Products from Multi-Source Data: Proceedings of a Workshop. Washington, DC: The National Academies Press. doi: 10.17226/24900.
×
Page 51
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The Intelligence Community Studies Board of the National Academies of Sciences, Engineering, and Medicine convened a workshop on August 9-10, 2017 to examine challenges in machine generation of analytic products from multi-source data. Workshop speakers and participants discussed research challenges related to machine-based methods for generating analytic products and for automating the evaluation of these products, with special attention to learning from small data, using multi-source data, adversarial learning, and understanding the human-machine relationship. This publication summarizes the presentations and discussions from the workshop.

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