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Eric Freiling, Algorithms Developer in San Diego, CA, United States
Eric Freiling

Algorithms Developer in San Diego, CA, United States

Member since August 4, 2018
Eric is a senior data scientist based in San Diego, and prior to his current career, spent six years in the defense industry. He has a strong academic background culminating in a master's degree in mathematics and a PhD in electrical engineering. Some of the areas that he's worked in are algorithm development, signal/image processing, and machine learning.
Eric is now available for hire



  • Algorithms, 8 years
  • Clustering, 6 years
  • Digital Signal Processing, 6 years
  • Python, 3 years
  • Image Processing, 3 years
  • Machine Learning, 3 years
  • Data Science, 3 years
  • Keras, 1 year
San Diego, CA, United States



Preferred Environment


The most amazing...

...project I've worked on was fingerprinting radios.


  • Senior Data Scientist

    2018 - PRESENT
    Sotera Wireless
    • Created new SpO2 calibration techniques, SpO2 feature calculations, and a PPG beat classification for signal quality metrics.
    • Developed unsupervised learning and clustering techniques in Python.
    Technologies: Python, Jupyter, PyCharm
  • Data Scientist

    2017 - 2018
    • Created event detection algorithms for several business use cases on signals regarding database health; developed those algorithms in Python and SQL.
    • Explored root cause data. Given a scenario of poor database performance or unusual behavior, explored massive data sets to find root cause with an accompanying explanation for customers (Python, SQL, and Tableau).
    Technologies: Teradata, SQL, Python, PyCharm, Tableau
  • Software Engineer - Scientist III

    2011 - 2017
    KAB Laboratories
    • Worked with a team of software engineers and algorithm developers to create handheld push-to-talk radio fingerprinting algorithms.
    • Worked on feature extraction and pattern detection on radio signals to uniquely identify radio devices, make, and model independent of the speaker.
    • Developed algorithms in MATLAB and collaborated with engineers to convert the code to C for production and helped deploy the product.
    • Created unique digital signal filtering techniques for radio fingerprinting. The project had been successful with moderate noise, but real-world applications had more noise than expected. These unique filtering techniques led to the success of the project in real-world noisy environments.
    • Developed an algorithm in MATLAB and converted it to C for production; the product was deployed and deemed successful.
    • Wrote signal modulation detection algorithms, pattern detection in the Fourier domain. Satellites can be saturated by unwanted signals. Detection of signal modulation and frequency was a way to classify wanted versus unwanted signals.
    • Developed algorithms in MATLAB and converted the code to C for production.
    • Designed and trained deep learning techniques and other machine learning algorithms to identify ship vessel type based on transit patterns; this project was short term.
    Technologies: MATLAB, C
  • Teaching Assistant

    2011 - 2014
    UCSD | University of California, San Diego
    • Assisted several professors in supplementing course material, hosting discussion sections, holding office hours, grading homework and exams for the following courses: Graduate Level Digital Image Processing, Graduate Level Random Processes, and Undergraduate Probability.
    • Received the "Best TA Award" in the Electrical Engineering department for a graduate class in digital image processing.
    Technologies: MATLAB
  • Teaching Assistant - Instructor

    2008 - 2010
    SDSU | San Diego State University
    • Acted as an instructor for two classes per semester: Statistics and Business Calculus.
    • Designed lesson plans, course material, and graded homework and exams.
    • Worked with the professors to assure that the lesson plans aligned with his teaching materials.
    Technologies: MATLAB


  • Transfer Learning on Small Datasets (Development)

    I compared the benefits of transfer learning on a small dataset to deep learning with data augmentation.


  • Other

    Algorithms, Principal Component Analysis (PCA), Clustering, Digital Signal Processing, Image Processing, Deep Learning, Machine Learning
  • Languages

    Python, SQL, C
  • Libraries/APIs

    Keras, SciPy, NumPy
  • Tools

    PyCharm, MATLAB
  • Paradigms

    Data Science


  • PhD degree in Electrical Engineering
    2010 - 2014
    University of California, San Diego - San Diego, CA, USA
  • Master's degree in Applied Mathematics
    2008 - 2010
    San Diego State University - San Diego, CA, USA
  • Bachelor's degree in Mathematics
    2006 - 2008
    University of California, San Diego - San Diego CA, USA
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