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Eric Sinzinger, Computer Vision Developer in Washington, DC, United States
Eric Sinzinger

Computer Vision Developer in Washington, DC, United States

Member since March 27, 2016
Eric has been a computer science professor, a researcher for the US government, and a consultant to several startup companies. He has excellent problem-solving and communication skills, believes in making the code base as small and simple as possible, and has worked across several different types of back-end stacks.
Eric is now available for hire

Portfolio

Experience

  • C, 20 years
  • C++, 20 years
  • Computer Vision, 20 years
  • OpenGL, 20 years
  • Python, 20 years
  • Machine Learning, 20 years
  • Management, 6 years
  • Robotics, 5 years
Washington, DC, United States

Availability

Part-time

Preferred Environment

Linux, Mac OS, Xcode, GitHub

The most amazing...

...project I've developed is a robot that can determine its location and identify a small set of objects.

Employment

  • Software Engineer

    2009 - PRESENT
    United States Government
    • Created an automated tool to detect and extract text from videos in real-time using C++, OpenCV, support vector machines, and a novel representation of the text space.
    • Built an organization-wide Linux file-system that allows all data to be tracked, properly categorized, and legally compliant—meeting government requirements for the data storage of critical information.
    • Developed an automated system for the machine language translation of small phrases that filled a need not satisfied by either simple word lookup or more advanced translation of full sentences.
    • Wrote Python scripts to support the automated processing of complex tasks for non-technical customers.
    • Served as the team lead for a vital tiger team that required understanding several different skill sets.
    • Received several team awards, two invention awards, and a patent.
    Technologies: Linux, C++, Python, SQL
  • Associate Professor of Computer Science

    1999 - 2009
    Texas Tech University
    • Worked as the founding director of the Data Representation and Intelligent Learning Lab and supervised twenty masters students and two PhD students.
    • Taught courses on programming, analysis of algorithms, operating systems, numerical methods, computer graphics, computer visualization, pattern recognition, and computer vision.
    • Developed a novel method for estimating livestock body mass from laser imaging that resulted in licensing and commercialization of the technology.
    • Created a novel method for improving the shape reconstruction from multiple images using an improved data analysis of the neighborhoods around critical points inside of an image. This technology was also licensed for commercialization.
    • Built a computer vision library to support complex mathematical operations on geometric objects that wrapped fast Fortran code underneath a clean object-oriented library in C/C++.
    Technologies: C/C++, Python, Computer Vision, Computer Graphics, Robotics, Scientific Computing, Pattern Matching
  • Research Engineer

    1998 - 1999
    Summus, Ltd
    • Worked as a developer on a video compression team that was designated as the leader in the video-compression quality in bandwidth limited environments.
    • Invented a new method that improved the accuracy of underwater mine detection by 95% by detecting surface slope of ocean waves and removing the effects of light refraction. This resulted in a technology that received the award of 1999 Advanced Imaging Solution of Year.
    • Wrote real-time, cinema-quality, video-compression code for applications with high bandwidth.
    • Optimized the code by detecting code bottlenecks and replacing expensive functions with numerical approximations implemented in x86 Assembly. This resulted in a 20-time speed-up of the specific functions, and a 3-time speed-up of the overall compressor.
    Technologies: C/C++, Assembly Language, Wavelet Theory

Experience

  • Flatland (Development)

    Created a system for the archaeological records of 3D scanned historical objects. Although the data was 3D, the archaeologists were more comfortable with 2D printings. Geo-rectified the 3D data to a cardinal 2D coordinate system and unwrapped 360 degree views into a flat representation allowing archaeological preservation of national park landmarks.

  • Object Recognition for Crew Exploration Vehicle (Development)

    While working as a visiting scientist at NASA, developed a system for real-time recognition of complex 3D shapes. Relied upon the crew exploration vehicle only having a small set of objects predetermined prior to launch, then developed shape classifiers for each object based upon surface characteristics of each shape.

Skills

  • Languages

    C, C++, Python, Fortran, Assembly
  • Libraries/APIs

    OpenGL, NumPy
  • Tools

    LabVIEW, Git, MATLAB, Bamboo
  • Paradigms

    Wavelets, Data Science, Management
  • Platforms

    Linux, AWS EC2, iOS, Docker
  • Storage

    SQLite, MySQL, AWS S3, MongoDB
  • Other

    Numerical Methods, Scientific Computing, Computer Vision, Computer Graphics, Machine Learning, Robotics

Education

  • Ph.D. in Computer Science
    1996 - 1999
    University of South Carolina - Columbia, SC, USA
  • M.S. in Computer Science
    1996 - 1998
    University of South Carolina - Columbia, SC, USA
  • M.S. in Mathematics
    1994 - 1996
    University of South Carolina - Columbia, SC, USA
  • B.A. in History
    1989 - 1993
    University of Texas at Austin - Austin, TX, USA
  • B.S. in Mathematics
    1989 - 1993
    University of Texas at Austin - Austin, TX, USA
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