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Nathan D Ratliff, Age 43Seattle, WA

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Known as:
Nathan Dratliff Ratliff Nathan
Related to:
Donald E Ratliff

Nathan Ratliff Phones & Addresses

Seattle, WA

Sugar Land, TX

Bellaire, TX

Pittsburgh, PA

Roseburg, OR

Beaverton, OR

Chicago, IL

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Nathan Ratliff resumes & CV records

Resumes

Nathan Ratliff Photo 28

Distinguished Research Scientist

Location:
6522 34Th St, Seattle, WA 98103
Industry:
Computer Software
Work:
Nvidia
Distinguished Research Scientist Lula Robotics
Chief Executive Officer and Co-Founder Max Planck Institute For Intelligent Systems and the University of Stuttgart Sep 1, 2013 - Oct 2015
Research Scientist Google Apr 1, 2011 - Jun 1, 2013
Software Engineer Intel Labs Jun 2010 - Feb 2011
Research Scientist Toyota Technological Institute at Chicago (Ttic) Sep 2009 - Jun 2010
Research Assistant Professor Amazon 2003 - 2004
Software Engineer
Education:
Carnegie Mellon University 2004 - 2009
Doctorates, Doctor of Philosophy, Robotics, Philosophy University of Washington 1999 - 2003
Bachelors, Bachelor of Science, Mathematics, Computer Engineering Roseburg High School
Skills:
Machine Learning, Robotics, Computer Science, Artificial Intelligence, Algorithms, Mathematics, Optimization, Computer Vision, Software Development, C++, Physics, Dynamics, Ros, Latex, Scientific Writing, Mapreduce
Nathan Ratliff Photo 29

Project Manager

Location:
45461 Bluemont Junction Sq, Sterling, VA 20164
Industry:
Defense & Space
Work:
Lynxnet, Llc Aug 2018 - Nov 2018
Senior Logistician and Deputy Program Manager Netservices, Llc Aug 2018 - Nov 2018
Project Manager Caci International Inc Jan 2018 - Aug 2018
Siam Pmo Insight Global Sep 2017 - Jan 2018
Contractor -Service Integration and Management Pmo Us Army Jun 2016 - May 2017
Company Commander Us Army Sep 2013 - Aug 2015
Aide-De-Camp Us Army May 2013 - Sep 2013
Executive Officer Us Army Sep 2012 - May 2013
Platoon Leader Us Army Oct 2011 - Sep 2012
Public Affairs Officer
Education:
American Military University 2012 - 2016
Masters, Master of Arts, Logistics, Management Embry - Riddle Aeronautical University 2007 - 2012
Bachelors, Bachelor of Science, Management Radford University 2007 - 2011
Bachelors, Bachelor of Science, Psychology Universal Technical Institute, Inc 2005 - 2006
Skills:
Leadership, Army, Operational Planning, Military Experience, Dod, Microsoft Office, Program Management, Military, Military Operations, Supply Chain Management, Security Clearance, Force Protection, Defense, Command, Government, Logistics Management, National Security, Intelligence, Project Management, Business Process Improvement, Customer Relationship Management, Teamwork, Training, Management, Team Building, Sap Products, Military Aviation, Helicopters, Operations Management, Satellite Communications, Automotive Technology, Team Leadership, Personnel Management, Automotive Repair, Electrical Troubleshooting, Maintenance and Repair, Maintenance, Aviation, Electronic Warfare, Defense Sector, Military Logistics, Military Training
Languages:
English
German
Certifications:
Demonstrated Senior Logistician
Active Ts/Sci Clearance
Lean Six Sigma Black Belt
Master of Arts, Transportation and Logistics Management
Certified Scrummaster® (Csm®)
U.s. Army Flight School, Ft Rucker, Alabama.
Faa Rated Commercial Helicopter Pilot Ifr/Vfr.
Nathan Ratliff Photo 30

Assistant Manager

Work:

Assistant Manager
Nathan Ratliff Photo 31

Nathan Ratliff

Nathan Ratliff Photo 32

Nathan Ratliff

Nathan Ratliff Photo 33

Nathan Ratliff

Nathan Ratliff Photo 34

Nathan Ratliff

Skills:
Management
Nathan Ratliff Photo 35

Nathan Ratliff

Publications & IP owners

Us Patents

Simulation Of Tasks Using Neural Networks

US Patent:
2020030, Oct 1, 2020
Filed:
Apr 1, 2019
Appl. No.:
16/372274
Inventors:
- Santa Clara CA, US
Miles Macklin - Auckland, NZ
Nathan Ratliff - Seattle WA, US
Dieter Fox - Seattle WA, US
Yevgen Chebotar - Los Angeles CA, US
Jan Issac - Seattle WA, US
International Classification:
B25J 9/16
G05B 13/02
G05B 13/04
G05D 1/00
Abstract:
A machine-learning control system is trained to perform a task using a simulation. The simulation is governed by parameters that, in various embodiments, are not precisely known. In an embodiment, the parameters are specified with an initial value and expected range. After training on the simulation, the machine-learning control system attempts to perform the task in the real world. In an embodiment, the results of the attempt are compared to the expected results of the simulation, and the parameters that govern the simulation are adjusted so that the simulated result matches the real-world attempt. In an embodiment, the machine-learning control system is retrained on the updated simulation. In an embodiment, as additional real-world attempts are made, the simulation parameters are refined and the control system is retrained until the simulation is accurate and the control system is able to successfully perform the task in the real world.

Force Estimation Using Deep Learning

US Patent:
2020030, Sep 24, 2020
Filed:
Mar 19, 2019
Appl. No.:
16/358485
Inventors:
- Santa Clara CA, US
Byron Boots - Seattle WA, US
Dieter Fox - Seattle WA, US
Ankur Handa - Seattle WA, US
Nathan Ratliff - Seattle WA, US
Balakumar Sundaralingam - Seattle WA, US
Alexander Lambert - Atlanta GA, US
International Classification:
G06F 3/01
G01L 5/22
G06N 3/08
G06T 11/00
Abstract:
A computer system generates a tactile force model for a tactile force sensor by performing a number of calibration tasks. In various embodiments, the calibration tasks include pressing the tactile force sensor while the tactile force sensor is attached to a pressure gauge, interacting with a ball, and pushing an object along a planar surface. Data collected from these calibration tasks is used to train a neural network. The resulting tactile force model allows the computer system to convert signals received from the tactile force sensor into a force magnitude and direction with greater accuracy than conventional methods. In an embodiment, force on the tactile force sensor is inferred by interacting with an object, determining the motion of the object, and estimating the forces on the object based on a physical model of the object.

Possible Relatives

Cynthia L Ratliff  David Ratliff  Dayna M Ratliff  Deanna G Ratliff  Dale J Ratliff  Deborah A Ratliff  Dan T Ratliff  Nathan Johnson  Nathan R Stamps  Nathan Johnston 

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