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Artificial Intelligence Engineer Resume

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Santa Clara, CA

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TECHNICAL SKILLS

Knowledge: image and data analysis, statistical analysis, data interpretation/visualization, machine learning

Programming Languages: Matlab, Python, SQL, C, C++, R, SAS, C#, HTML/XML, CSS, LaTex

Software/Tools: Keras, Tensorflow, Caffe, Theano, Scikit - learn, Microsoft Visual Studio, Definiens Tissue Studio, Spotfire, OpenCV, ITK, VTK, ImageJ, ITK-SNAP, Slicer3D, Seg3D, AutoCAD, Xilinx ISE, PSpice

PROFESSIONAL EXPERIENCE

Artificial Intelligence Engineer

Confidential, Santa Clara, CA

Responsibilities:

  • Investigating network behavior and building network anomaly detection models
  • Built predictive maintenance models using machine learning/deep learning techniques
  • Performed driver behavior analysis and developed machine learning models for driver identification
  • Developed models for time series smart meter prediction and anomaly detection using Neural Network and Long Short - Term Memory (LSTM)
  • Designed experiments for smart home user behavior analysis using multi-modal sensors and developed models to automatically detect user activities

Image Analysis Scientist

Confidential, Seattle, WA

Responsibilities:

  • Designed and developed image analysis tools to automatically analyze large, high content microscopy, histological images and MRI images
  • Built quantitative features of tumor response to drug exposure for the selection of optimal drugs
  • Used SQL queries to store and extract data from MySQL database
  • Analyzed drug response data and performed statistical analysis using Matlab, Python, R, and Spotfire
  • Developed solutions to optimize image formats and improve image quality

Research Intern

Confidential

Responsibilities:

  • Designed bench-top study protocols and experiments for optical shape sensing system characterization
  • Performed CT and X-ray scanning of experimental setups and acquired reconstructed optical fiber shapes
  • Applied statistical metrics to evaluate the performance of reconstructed optical fiber shapes Fault detection and diagnosis (FDD) of light fixtures in lighting systems
  • Built a linear model relating light fixtures and sensors
  • Proposed a combination of model-based and classification methods to detect and locate light fixture failures
  • Designed indoor lighting failure experiments and acquired light intensity data

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