NASA LANGLEY RESEARCH CENTER: CDT – BIG DATA ANALYTICS AND MACHINE INTELLIGENCE TEAM HOSTING
Machine Learning Technologies and Their Applications to Scientific and Engineering Domains Workshop
August 16-18, 2016Pearl Young and Reid 1Morning session: 9 AM – NoonAfternoon session: 1 PM – 4 PM
Learn and stay abreast of state-of-the-art technologies and their applications — NASA Langley Machine Learning Technologies and their Applications for Scientific and Engineering Domains Workshop.The workshop provided a forum for scientists and engineers to learn from experts across academia and industry on advances in machine learning techniques and cognitive technologies, and their applications to NASA domains such as computational aerosciences, computational materials and structures, next generation airspace, autonomy, aerospace systems analysis and design, and climate science. Participation in this workshop helped to further develop the important work NASA Langley is doing in this area through investigating and applying emerging technologies in data analytics and machine learning to address NASA’s technical challenges.
The workshop was organized and hosted by the Big Data Analytics and Machine Intelligence capability team that is part of NASA Langley’s Comprehensive Digital Transformation (CDT) initiative. CDT serves as a catalyst to create an integrated digital capability by leveraging and synergistically combining state-of-the-art advancements in modeling and simulation, high performance computing, big data analytics and machine intelligence, and IT infrastructure. NIA is assisting with logistics and coordination of the workshop.
Thank you for your participation and attendance at the NASA LaRC Machine Learning Workshop!
Speakers’ Topics and Presentations below – Click a Speaker’s name for Bio/Abstract. Click a presentation title to download.
- Dr. Sebastian Pokutta, Georgia Institute of Technology, Industrial Engineering Dept. – Machine Learning in Engineering: Applications and Trends
- Dr. Ella Atkins, University of Michigan, Aerospace Engineering Dept. – New Data Sources to Revolutionize UAS Situational Awareness and Minimize Risk
- Dr. Chris Codella, IBM, Watson Group – Cognitive Computing and IBM Watson in Research, Operations, and Medicine
- Dr. Tsengdar Lee, NASA Science Mission Directorate – NASA Earth Science Knowledge Network
- Dr. Barnabas Poczos, Carnegie Mellon University, Computer Science Dept. – Applied Machine Learning for Design Optimization in Cosmology, Neuroscience and Drug Discovery
- Dr. Lyle Long, Pennsylvania State University, Aerospace Engineering Dept. – Toward Human-Level (and Beyond) Artificial Intelligence
- Dr. Una-May O’Reilly, MIT, Computer Science and Artificial Intelligence Lab – Machine Learning: Data-Driven Artificial Intelligence with Machine Learning
- Dr. Matthias Scheutz, Tufts University, Cognitive and Computer Science Dept. – Intelligent Agents: One-Shot Learning through Task-Based Natural Language Dialogues
- Dr. Dimitri Mavris, Georgia Institute of Technology, Aerospace Engineering Dept. – Application of Machine Learning for Aircraft Design
- Dr. Karthik Duraisamy, University of Michigan Aerospace Engineering Dept. – Data-driven Turbulence Modeling: Current Advances and Future Challenges
- Dr. Heng Xiao, Virginia Polytechnic Institute and State University, Aerospace Engineering Dept. – A Physics-Informed Machine Learning Framework for RANS-Based Predictive Turbulence Modeling
- Dr. Krishna Rajan, University at Buffalo SUNY Materials Design and Innovation Dept. – Materials Informatics: Mining and Learning from Data for Accelerated Design and Discovery
- Dr. Jaime Carbonell, Carnegie Mellon University, Computer Science Dept.- Transfer Learning and Data Science for Aerospace: CMU and Boeing Partnership
- Dr.Vipin Kumar, University of Minnesota, Computer Science Dept.- Big Data in Climate: Opportunities and Challenges for Machine Learning and Data Mining
- Dr. Raju Vatsavai, North Carolina State University, Computer Science Department and Oak Ridge National Laboratory, USA – Global Earth Observations Based Machine Learning Framework for Monitoring Critical Natural and Man-Made Infrastructures