Principal Scientist, USRA, NASA Ames Research Center
There is an increasing interest in applying methods based on Machine Learning Techniques (MLT) to problems in aviation operations. The current interest is based on developments in Cloud Computing, the availability of open software and the success of MLT in automation, consumer behavior and finance involving large database. Historically aviation operations have been analyzed using physics-based models and provide information for making operational decisions. This talk describes issues to be addressed in applying either model-driven or data driven methods. Aviation operations involving many decision makers, multiple objectives, poor or unavailable physics-based models and a rich historical database are prime candidates for analysis using data-driven methods. The issues relating to data, feature selection and validation of the models are illustrated by examining case studies of the application of MLT to problems in air traffic management at NASA. Further research is needed in the application of MLT to critical aviation operations. As always, the best approach depends on the task, the physical understanding of the problem and the quality and quantity of the available data.
Dr. Banavar Sridhar is Principal Engineer, University Space Research Association (USRA) at NASA Ames Research Center, Moffett Field, CA. He was formerly NASA Senior Scientist for Air Transportation Systems. His research interests are in the application of modeling and optimization techniques to aerospace systems. Dr. Sridhar received the 2004 IEEE Control System Technology Award for his contributions to the development of modeling and simulation techniques. He led the development of traffic flow management software, Future ATM Concepts Evaluation Tool (FACET), which received the NASA Software of the Year Award in 2006 and the FAA Excellence in Aviation Research in 2010. Dr. Sridhar has served on the Editorial Board of the International Federation of Automatic Control (IFAC) Journal Control Engineering Practice, International Journal of Machine Vision and Applications, IEEE Control Systems Magazine, IEEE Transactions on Automatic Control, Journal of Robotics Systems Special Issue on Passive Ranging and IEEE Transactions on Robotics and Automation Special Issue on Perception-Based Real-World Navigation. He is a Fellow of the IEEE and the AIAA.
Director, NASA Aeronautics Research Institute (NARI)
NASA’s Ames Research Center
Aviation is growing and many new entrants are emerging. They include drones, urban air mobility vehicles, commercial space crafts, and high altitude platforms. Current airspace operations and air traffic management will have to evolve to accommodate them. At the same time, improvements in the current manned air traffic management operations are desired to reduce delays and increase capacity. The talk addresses how we can enable the future, while maintaining the safety operations that we enjoy. It will specifically discuss the needs for airspace access, scalability, safety, and efficiency of all airspace users. The talk begins with an anecdote on how the speaker got involved in air traffic management research and development, and ends with a discussion of why is it an exciting area for a new career.
Dr. Parimal Kopardekar (PK) serves as the Director of NASA Aeronautics Research Institute (NARI). In that capacity, he is responsible for exploring new trends and needs related to aeronautics. He also serves as principal investigator for the Unmanned Aircraft Systems Traffic Management (UTM). For UTM, he and the team won the Service to America Medals (known as Oscars of Federal Workforce) in Promising Innovation Category in 2018. He was named as one of the top 25 most influential people in commercial UAS industry in 2017. He also won NASA exceptional technology medal in 2016. He is a co-author of over 50 publications with 3 best paper awards. He is passionate about airspace operations, autonomy, advanced air mobility, and digital manufacturing and supply chains in aeronautics. He is co-editor-in-chief of the Journal of Aerospace Operations and a fellow of American Institute of Aeronautics and Astronautics.
Associate Professor of City & Regional Planning, UC Berkeley
Dr. Gonzalez uses data science to characterize how humans interact with the built and the natural environment seeking to plan for more sustainable and livable cities. Given the increasing ubiquity of plug-in electric vehicles (PEVs) in the Bay Area, she presents a study that aims to assist in planning decisions by providing timing recommendations and assigning monetary values to modulations of PEV start and end charging times.
According to the US Energy Information Administration, the number of PEVs in the United States doubled between 2013 and 2015 and are expected to reach 20 million by 2020.
In the second part, Dr. Gonzalez presents DeepAir, a convolutional neural network platform that combines satellite imagery and urban maps with weather and air monitoring stations datasets. The goal is to enable science-informed policy by understanding various inter-dependencies in the quality of the air, we breathe. These methodologies are aimed to be fully scalable and open source. The presented methods can be extended to other domains that involve human and environmental interactions.
Marta Gonzalez, Ph.D. is an associate professor at UC Berkeley with appointments both in Civil and Environmental Engineering and City and Regional Planning. She is also a Research Scientist in the Energy Analysis and Environmental Impacts Division of the LBNL. Marta’s research analyzes and combines spatial data on various complex systems, with applications to transportation networks, energy efficiency planning, and characterization of disease proliferation. Prior to joining UC Berkeley, Marta worked as an Associate Professor of Civil and Environmental Engineering at MIT.
Aerospace Engineer, NASA Ames Research Center
The modern day National Airspace System (NAS) is powered by System Wide Information Management (SWIM) which is a real-time digital data sharing infrastructure that provides a high fidelity view of the lifecycle of a flight. The newly available data within the SWIM feeds can be leveraged to help drive efficiencies in the NAS. In this talk, we investigate the gate conflict prediction problem as a concrete use case which could help drive efficiencies. We begin with a high level description of NASA's Airspace Technology Demonstration 2 which is built upon the real-time SWIM feeds and produces the data used in our investigation. We model gate conflicts as a regression problem and describe the iterative process of model building, model validation, and evaluation used to assess the efficacy of our approach. We quantify our predictive accuracy and identify paths for improvement. Through this iterative process we hope to evolve our models and methods in the development of a near real-time prediction service.
Jeremy Coupe is an Aerospace Engineer at NASA Ames Research Center and the analytics lead for NASA's Airspace Technology Demonstration 2. He received his BS degree in Mathematics from the University of San Francisco and both MS degree in Applied Mathematics and Statistics and Ph.D. degree in Computer Engineering from the University of California, Santa Cruz where he was a member of the Robotics and Control Lab.
Technologist, Business Finland
Sudip Mukhopadhyay, Ph.D. will discuss the drone application development, starting in 2007, of a select few customized end users applications. He will discuss lessons learned and innovative developments, which will augment the steps needed to make urban air mobility successful in the coming years. He will also describe the relevance and role of aerospace corporations versus today’s car manufacturers, and what we need to do to develop a new and successful alternative transportation ecosystem.
Dr. Mukhopadhyay currently works for Business Finland, the innovation instrument, helping the country with incubation, innovation, and investment strategy, and nurturing hundreds of government funded startups in Finland. Areas of focus are full autonomy, future mobility, new space, and circular economy. Sudip worked at Honeywell for 16 years as a corporate fellow and Director of innovation for Aerospace, bringing over $7B new products revenue through his own research. Sudip co-founded the Honeywell drone startup and wrote the Urban Air Mobility strategy for Honeywell Corporate. Sudip is trained as an engineer in India, Israel, and UC Berkeley. Sudip lives in Berkeley with his wife, a biophysicist, and their daughter.
Recording not available
Professor, Civil Engineering, UC Berkeley
Raja Sengupta, Ph.D. is Professor in the Systems Engineering Program, Civil Environmental Engineering at UC Berkeley. He holds a PhD in EECS from the University of Michigan. His research has spanned automated cars, drones, connected cars, smartphone apps for economics & transportation, wireless networking, and control theory. He likes to do research with industry and get it into the marketplace. He holds car-to-road networking patents with Toyota, a UAV patent with BAE Aerospace, and has car-to-car networking contributions standardized by the SAE into J2945. He created technology for the successful start-up automatic.com and is now the founder and CEO of responsiblerobotics.com. He has been an advisor to the World Bank, a recipient of U.S. DoT Connected Vehicle Technology award in 2011, UC Berkeley's Energy and Climate Lectures Innovation Award in 2014, and has authored over a hundred papers spanning control theory, networking, drones, and transportation.
The Aviation Data Science Seminar Series is a collaboration between USRA, NASA, and the University of California, Berkeley. For more information on the Aviation Data Science Lab at NASA Ames Research Center, please visit our web site at http://nams.usra.edu/nams-labs/aviation-data-science-lab-dsl.
This course teaches you fundamentals of reproducible data science and analytics, probabilistic reasoning & statistical inference, and machine learning to leverage data generated within the large scope of aviation and aeronautics.
As the number of air vehicles grows exponentially with unmanned aerial systems and the amount of data grows exponentially in civil aviation, the future holds more promise for a greater impact from advanced tools and techniques in data science and AI. This Spring, learn from experts in government, industry, and academia in a 12-seminar series, sponsored by USRA and UC Berkeley with NASA Ames Research Center.
USRA institutes and programs will celebrate USRA’s 50th anniversary on March 12, 2019. USRA was founded on March 12, 1969 in Washington, D.C. as a private, nonprofit corporation under the auspices of the National Academy of Sciences.
University Space Research Association NAMS staff received excellence awards at the 2018 Ames Contractor Council Excellence Award Ceremony. The award recipients were acknowledged at the ceremony by Ames Center Director Dr. Eugene Tu and were nominated by Dr. David Bell, Director of RIACS and NAMS Chief Technologist.
Dr. David Bell, Director, USRA Research Institute for Advanced Computer Science and Chief Technologist, NASA Academic Mission Services was a keynote speaker at the Quantum Summit held in San Francisco on September 19, 2018.