Nathan Parrish
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Nathan Parrish Email & Phone Number

Location: Laurel, Maryland, United States 4 work roles 3 schools
1 work email found @jhuapl.edu LinkedIn matched
✓ Verified May 2026 4 data sources Profile completeness 100%

Contact Signals · 1 work email

Work email n****@jhuapl.edu
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Role
Chief Scientist
Location
Laurel, Maryland, United States
Company size

Who is Nathan Parrish? Overview

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Quick answer

Nathan Parrish is listed as Chief Scientist at Johns Hopkins University Applied Physics Laboratory, a company with 5538 employees, based in Laurel, Maryland, United States. AeroLeads shows a work email signal at jhuapl.edu and a matched LinkedIn profile for Nathan Parrish.

Nathan Parrish previously worked as Graduate Student, Electrical Engineering at University Of Washington and Research Intern at Johns Hopkins University Applied Physics Laboratory. Nathan Parrish holds Phd, Electrical Engineering from University Of Washington.

Company email context

Email format at Johns Hopkins University Applied Physics Laboratory

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{first}.{last}@jhuapl.edu
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AeroLeads found 1 current-domain work email signal for Nathan Parrish. Compare company email patterns before reaching out.

Profile bio

About Nathan Parrish

Nathan Parrish is a Chief Scientist at Johns Hopkins University Applied Physics Laboratory. He possess expertise in matlab, leadership, simulations, statistical signal, digital signal processors and 11 more skills.

Listed skills include Matlab, Leadership, Simulations, Statistical Signal, and 12 others.

Current workplace

Nathan Parrish's current company

Company context helps verify the profile and gives searchers a useful next step.

Johns Hopkins University Applied Physics Laboratory
Johns Hopkins University Applied Physics Laboratory
Chief Scientist
laurel, maryland, united states
Website
Employees
5538
AeroLeads page
4 roles

Nathan Parrish work experience

A career timeline built from the work history available for this profile.

Us Army, Captain

Fort Lewis, WA

Jun 2001 - Apr 2006
Team & coworkers

Colleagues at Johns Hopkins University Applied Physics Laboratory

Other employees you can reach at jhuapl.edu. View company contacts for 5538 employees →

3 education records

Nathan Parrish education

Ms, Electrical Engineering

Innovator Fellow, University of Washington Electrical Engineering Department, 2006

FAQ

Frequently asked questions about Nathan Parrish

Quick answers generated from the profile data available on this page.

What company does Nathan Parrish work for?

Nathan Parrish works for Johns Hopkins University Applied Physics Laboratory.

What is Nathan Parrish's role at Johns Hopkins University Applied Physics Laboratory?

Nathan Parrish is listed as Chief Scientist at Johns Hopkins University Applied Physics Laboratory.

What is Nathan Parrish's email address?

AeroLeads has found 1 work email signal at @jhuapl.edu for Nathan Parrish at Johns Hopkins University Applied Physics Laboratory.

Where is Nathan Parrish based?

Nathan Parrish is based in Laurel, Maryland, United States while working with Johns Hopkins University Applied Physics Laboratory.

What companies has Nathan Parrish worked for?

Nathan Parrish has worked for Johns Hopkins University Applied Physics Laboratory, University Of Washington, and Us Army.

Who are Nathan Parrish's colleagues at Johns Hopkins University Applied Physics Laboratory?

Nathan Parrish's colleagues at Johns Hopkins University Applied Physics Laboratory include William Elgie, Pmp, Michael Tilghman, Tim Krueger, Timothy Herder, and Margaret Morris.

How can I contact Nathan Parrish?

You can use AeroLeads to view verified contact signals for Nathan Parrish at Johns Hopkins University Applied Physics Laboratory, including work email, phone, and LinkedIn data when available.

What schools did Nathan Parrish attend?

Nathan Parrish holds Phd, Electrical Engineering from University Of Washington.

What skills is Nathan Parrish known for?

Nathan Parrish is listed with skills including Matlab, Leadership, Simulations, Statistical Signal, Digital Signal Processors, Machine Learning, Image Processing, and Mathematical Modeling.

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