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:
- Machine-based methods for generating analytic products.
- Machine-based methods for automating the evaluation of analytic products.
Research Questions:
- What are the technical objectives and metrics needed for success?
- What are the primary issues?
- What are the current and “next level” key performance metrics?
- What is the “level after next” of expected research and development performance?
- What is the research knowledge base?
- How can the government best prepare the scientific workforce to enhance discovery in this area?
- 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.
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| 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.
×
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.
×
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.
×
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.
×
