Los Angeles-Las Vegas

Notional DIP Ecosystem

Dr. Swati Saxena

Swati Saxena is presently working as a Technical Lead at NASA Ames Research Center for the Digital Information Platform sub-project within NASA's Air Traffic Management eXploration (ATM-X) project. Her research interests include data science for aviation (ATM), computational fluid dynamics and HPC, engine icing, turbomachinery and aeroacoustics.

Before joining NASA, Swati has worked at Ansys Inc. and GE Global Research. She has held adjunct faculty position at Santa Clara University. She is a lifetime senior member of AIAA, has more than 25 technical publications and holds two patents. She completed her MS and PhD in Aerospace Engineering from the Pennsylvania State University and her undergraduate studies from IIT Kanpur in India. 

NASA's Digital Information Platform (DIP) to Accelerate NAS Transformation

NASA's Digital Information Platform (DIP) sub-project's objective is to accelerate the transformation of the National Airspace System (NAS) and airspace operations through the development of a platform for advanced data-driven digital services for both traditional and emergent aviation operations. DIP is one of the research and technology development initiatives within NASA's Air Traffic Management exploration (ATM-X) project. DIP sub-projects contributes to the NASA Aeronautics focus area of safe and efficient growth in global operations and the U.S. Aviation Climate Goal of Net-Zero GHG Emissions from the U.S. Aviation Sector by 2050.

The primary goal of DIP is to establish an easily accessible cloud-based digital platform that can be utilized by a community of data and service providers and flight operators. The users will have access to integrated real-time and historical flight data upon which the data-driven services for NAS users can be developed and operated. The DIP sub-project promotes and facilitates one-to-many shared services model rather than traditional one-to-one services model. Several NASA developed services will be hosted on the platform as reference implementations. An example of such data-driven NASA developed service is the Runway Configuration Prediction service where machine learning model is used to predict the optimal runway configuration for an airport based on the historical data.

In this presentation, an overview of the DIP sub-project, the technical approach, its timeline and the details about the partnership opportunities will be shared.  

Tentative Agenda: (All Time PDT (GMT-0800))

10:00 am - 10:20 am: Check-in, Networking

10:20 am - 10:30 am: Welcome and Introduction

10:30 am - 12:00 pm: Presentation and Q&A