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Meet Joseph Raetano, The Data Science Virtual Academy's New Instructor

ENTITY and Woz U are proud to have Joe Raetano on the team along with our wonderful mentors - Sherlin, Sarah, Margaret, and Darrell.

Joseph Raetano is a highly trained veteran Naval Officer, DoE, and Federal Government researcher with highly specialized operational R&D innovation experience. He has been consistently selected and trusted throughout his career to develop special technical programs that required keen intellect, and very focused leadership. He has over 7+ years of developing new programs that focus on integration or designing Artificial Intelligence, Machine Learning, Deep Learning, and Natural Language understanding. He has over 7+ years involving various transportation-based initiatives at both the national laboratory and commercial sectors.

He has over 15+ years of experience in offensive and defensive cyberspace research, development, testing, and operations at the National Level. This extensive experience has been utilized in concept development and implementation that has set the cyber vision, strategy and implementation at the national and service level. He is experienced with most topics and technologies relating to cyber security specifically Computer Network Exploitation, Attack and Defense (CNE/A/D); cloud infrastructures, operating system kernels; mobile and embedded devices; wireless and RF; fuzzing, taint tracking, static and dynamic analysis of binary code for multiple platforms; reverse engineering; intrusion tolerant systems; agents/bots, rootkits and related malware; network infrastructure; estimation and control theory; situation assessment; C2; and pen-test tools.

Joseph has designed and constructed a Vehicle Security Laboratory (VSL) located at the National Transportation Research Center that conducts vehicle focused data acquisition, vulnerability analysis research and development. Focuses on cyber physical and cloud security from the point of front end of collection in electronic/embedded systems, sensors, signals, communications, smart-grid power systems, wearable devices and Internet of Things (IoT) to back end systems in cloud environments. Some portion of the front end is operated in hazardous environments to include radiological, biological, chemical and explosive. OPM Tier 5 / TS/SCI: 6/23/2020, DoE Q: 2/26/2013, CI Poly: 11/1/2013. Career supported by a MS Computer Science and Ph.D. (ABD) Computer Science focusing on Machine Learning/Artificial Intelligence.

More About Joseph:

Drone Video - Kymeta is proud to support aid efforts in Puerto Rico by providing much-needed connectivity to citizens and relief agencies with Liberty Global, Intelsat, and Erwin Hymer Group. You can also see Joseph's blog Restore Puerto Rico following Hurricane Maria relief.

You can also check out his work with the Mobile Research Laboratory and Mobile Mission Ground Center: Designed and built for high-tech mobile edge node machine learning. It allows a person or persons to stay on station for long periods of time to assist in everything from humanitarian assistance to real-time smart city tests and analysis providing very focused onsite research platform support.

Interviews with Jospeh:

Mike Wendland’s interview during humanitarian effort in Puerto Rico while supporting Whitehouse/DHS/FEMA:

Latest Automotive and Connected Vehicle awards:

PhD Dissertation Research:

"Mobile Edge Node Machine Learning, Precognitive Lambda Function, Neural Inference Model Bonding"

Abstract: A research project focused on solving automated precognitive decision making during travel in a variety of transportation environments. The aim is to make it possible for a system to make decisions based on both the personality of the occupant/s and the personality of the vehicle to create meaningful, memorable, and safe travel. In this research, we explore various scenarios where decisions can be made before the occupant needs to ask or search for solutions to travel tasks. This project provides the initial exploration of combined neural inference model bonding of both vehicle and occupants.


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