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Dr. Deepak Shyamkant Kulkarni is a senior computer scientist within the Intelligent Systems Division at NASA Ames Research Center. Since 1996, Dr. Kulkarni has managed NASA research and development projects in the areas of data mining, health monitoring systems and collaborative systems. He has been the principal investigator on several long-term projects with successful NASA deployments in engineering, and aviation domains. He was the leader of Aeronautical Assistant Systems group from 2011 to 2018. His contributions have been recognized by several NASA awards including NASA Ames Honor Awards (2010, 2013), NASA Honor Award for ADIS work (2005), NASA Group Achievement Award for Exploration Systems work (2005), Space Act Award for ADIS work (2004), NASA Tech Briefs Award (2004), Semantic Organizer Space Act Award (2003), NASA Ames Group Achievement Awards (1998, 1999). He was a member of the editorial board of International Journal of Operations Research and Information Systems from 2010 to 2018. In 2013, SAE International recognized his work with Arch T. Colwell Merit Award. In his spare time, Deepak Kulkarni is also involved in educational outreach activities including coaching students for national math competitions including math olympiads. He was the chair of NASA Ames Combined Federal Campaign in 1999.

Some of his projects are listed below.

Aviation Data Integration System : Many airlines routinely review flight data and seek voluntary safety reports to identify anomalous events and ways to improve flight safety. These events may pinpoint potential problems in flight operations that could grow to cause an accident. Apart from human performance and mechanical factors, environmental conditions contribute to understanding what happened. Knowing the weather allows safety analysts to determine whether an event needs more detailed study. Aviation Data Integration System (ADIS) is a Web-based archive that integrates environmental data from more than 100 airports, giving flight safety analysts access to weather data previously unavailable to them. This system was commercialized as a Flight Operation Quality Assurance (FOQA) product used by airlines. System was also made available airlines as a web based system by FAA Safety Office.
Development of Models of Impact of Weather on Aviation Delays and Throughput : This work models relating national delay, center level delays, airspace capacity and weather. The methodology used traffic data during the spring and summer over a three-year period in the United States. The results indicate: (a) the method for estimating the delay at the national level can be extended to estimate delay at the regional level, (b) the estimation of national delay as the sum of regional delays produces a national delay estimate of comparable accuracy, while providing insights into differential impacts of regional weather on delays, and (c) the national delay can be estimated accurately by observing the behavior of 5 or 6 prominent centers.

Networked ATM: As the lead of NASA's Networked Air Traffic Management effort from 2012-14, Deepak Kulkarni was involved in an effort aimed at formulation and demonstration of concepts of cloud computing and other networking technologies in NextGEN airspace operations. He led two successful demonstration projects and organization of two industry-Government Networked ATM workshops attended by about 100 participants.

JPDO: Deepak Kulkarni served as NASA representative on Shared Situation Awareness Team of Joint Planning and Development Office. This team with representatives from multiple Government agencies addressed one of the most critical challenges in NextGen that is to develop a net-centric capability for the national airspace system that will provide advanced exchange of operational data in national airspace operations.

Reusable Launch Vehicle Condition Based Maintenance System: Deepak Kulkarni used machine learning methods to develop and demonstrate a condition based maintenance system for a X-33 Reusable Launch Vehicle system technology demonstration developed by Rockwell under NASA funding.

Space Shuttle Reaction Control System Monitoring System Deepak Kulkarni used Bayesian machine learning methods to develop Space Shuttle Reaction Control System monitoring system that was deployed in Space Shuttle operations. The system used machine learning methods on historical archive of data to learn rules for detecting conditions that needed further inspection and analysis. Monitoring system was successfully used in Space Shuttle Mission Control Center.

Semantic Organizer Semantic Organizer is a collaborative knowledge management system designed to support distributed NASA projects, including multidisciplinary teams of scientists, engineers, and accident investigators. The system provides a customizable, semantically structured information repository that stores work
products relevant to multiple projects of differing types. SemanticOrganizer is one of the earliest and largest semantic web applications deployed at NASA, and has been used in varying contexts ranging from the investigation of Space Shuttle Columbia’s accident to the search for life on other planets. Semantic Organizer project has been recognized by NASA Space Act as well as Turning Goals into Reality awards. It was a runner-up for NASA Software of the Year Award in 2003.

KEKADA : Deepak Kulkarni developed KEKADA discovery system as a part of his doctorate thesis. KEKADA is constructed to simulate the sequence of experiments carried out by Hans Krebs and his colleague, Kurt Henseleit, between July 1931 and April 1932, which produced the elucidation of the chemical pathways for synthesis of urea in the liver. This discovery of the ornithine cycle was the first demonstration of the existence of a cycle in the metabolic biochemistry. Simon and Kulkarni's source for this episode is "Hans Krebs and the Discovery of the Ornithine Cycle" in Federation Proceedings (1980) by Frederic L. Holmes of Yale University. Holmes also made himself available to Simon and Kulkarni for consultation in 1986 when their study was in progress. The organization of KEKADA is based on a two-space model of learning.

From 2010 to 2017, Deepak Kulkarni served as the leader of Aeronautical Assistant Systems Group . The group included projects in machine learning, ontology based search tools and simulation capability in support of Aeronautical Research Mission Directorate in NASA. Deepak has also led organization of workshops and conference sessions on information technology applications in aeronautics.



Deepak S. Kulkarni

Intelligent Systems Division
Ames Research Center
Mail Stop 269-2
Moffett Field, CA 94035

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