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David Liebson Phones & Addresses

  • 2816 NE 12Th Ave, Portland, OR 97212
  • 6038 NE 26Th Ave, Portland, OR 97211
  • 2312 Schmidt Way, Beaverton, OR 97006
  • 2378 NW Schmidt Way, Beaverton, OR 97006
  • 2738 Schmidt Way, Beaverton, OR 97006
  • North East, PA
  • Somerville, MA
  • Wheaton, IL

Work

Company: Blackberry Feb 2019 Position: Software engineer staff

Education

Degree: Masters, Master of Engineering School / High School: Massachusetts Institute of Technology 1992 to 1997 Specialities: Electrical Engineering, Electrical Engineering and Computer Science, Computer Science, Engineering

Skills

Intel • Processors • Perl • Testing • Software Engineering • Simulations • C • X86 Assembly • Ia32 • Microprocessors • Microcode • Validation • Semiconductors • Debugging • Linux • Assembly Language • Embedded Systems • Software Development • Computer Hardware • Verilog • Computer Science • Design Verification Testing • Software Quality Assurance • Software Quality • Vlsi • Asic • Triage • Troubleshooting • Soc • Fpga • System Architecture • Computer Architecture • Hardware • Integration • Unix • Engineering • System on A Chip • Field Programmable Gate Arrays

Interests

Workaround Development • Technology • Debug Techniques • Validation • Photography • Space • Bug Hunting • Woodworking • Maker Movement

Industries

Semiconductors

Resumes

Resumes

David Liebson Photo 1

Software Engineer Staff

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Location:
2816 northeast 12Th Ave, Portland, OR 97212
Industry:
Semiconductors
Work:
Blackberry
Software Engineer Staff

Cylance Inc.
Research Engineer, Senior

Intel Corporation 2014 - 2016
System Validation Engineer

Intel Corporation 2004 - 2014
Component Design Engineer
Education:
Massachusetts Institute of Technology 1992 - 1997
Masters, Master of Engineering, Electrical Engineering, Electrical Engineering and Computer Science, Computer Science, Engineering
Massachusetts Institute of Technology 1992 - 1996
Bachelors, Bachelor of Science, Electrical Engineering, Electrical Engineering and Computer Science, Computer Science
Skills:
Intel
Processors
Perl
Testing
Software Engineering
Simulations
C
X86 Assembly
Ia32
Microprocessors
Microcode
Validation
Semiconductors
Debugging
Linux
Assembly Language
Embedded Systems
Software Development
Computer Hardware
Verilog
Computer Science
Design Verification Testing
Software Quality Assurance
Software Quality
Vlsi
Asic
Triage
Troubleshooting
Soc
Fpga
System Architecture
Computer Architecture
Hardware
Integration
Unix
Engineering
System on A Chip
Field Programmable Gate Arrays
Interests:
Workaround Development
Technology
Debug Techniques
Validation
Photography
Space
Bug Hunting
Woodworking
Maker Movement

Publications

Us Patents

Statistical Data Fingerprinting And Tracing Data Similarity Of Documents

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US Patent:
20220198189, Jun 23, 2022
Filed:
Dec 23, 2020
Appl. No.:
17/132767
Inventors:
- Irvine CA, US
David Michael LIEBSON - Portland OR, US
Yaroslav OLIINYK - Portland OR, US
International Classification:
G06K 9/00
G06K 9/62
G06F 40/279
G06F 17/18
Abstract:
A method and computing device for statistical data fingerprinting and tracing data similarity of documents. The method comprises applying a statistical function to a subset of text in a first document thereby generating a first fingerprint; applying the statistical function to a subset of text in a second document thereby generating a second fingerprint; comparing the first fingerprint to the second fingerprint; and determining that the subset of text in the first document matches the subset of text in the second document based on the first fingerprint threshold matching the second fingerprint, wherein the statistical function is a measure of randomness of a count of each character in a subset of text against an expected distribution of said characters.

Redaction Of Artificial Intelligence Training Documents

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US Patent:
20180260734, Sep 13, 2018
Filed:
Mar 7, 2017
Appl. No.:
15/452623
Inventors:
- Irvine CA, US
Yaroslav Oliinyk - Portland OR, US
David Michael Liebson - Portland OR, US
International Classification:
G06N 99/00
G06F 21/62
Abstract:
Systems and methods are provided herein for redaction of artificial intelligence (AI) training documents. Data comprising an unredacted document is received. The unredacted document comprises a plurality of objects arranged according to a first topology. The unredacted document is parsed to identify objects either directly or relationally containing user sensitive information using a predetermined rule set based on the first topology. The user sensitive information within the unredacted document is substituted with placeholder information to generate a redacted document having a second topology. The second topology is substantially identical to the first topology. In some variations, the redacted document is provided to an AI model for training.
David M Liebson from Portland, OR, age ~50 Get Report