Reza Vaghefi, Machine Learning Developer in Campbell, CA, United States
Reza Vaghefi

Machine Learning Developer in Campbell, CA, United States

Member since February 3, 2022
Reza holds an MS and a PhD in electrical and computer engineering. As a professional with more than ten years of experience in machine learning and data analysis, he specializes in different programming languages such as Python, R, C, C++, and MATLAB. Reza has a strong background in software engineering, algorithms, and data structures.
Reza is now available for hire

Portfolio

  • Qualcomm
    C++, Algorithms, Data Analysis, Cloud, Pandas, NumPy, Perforce, Scikit-learn...
  • Self-employed
    Artificial Intelligence (AI), Machine Learning, PyTorch, TensorFlow...
  • Blue Danube Systems
    Python, MATLAB, Simulations, Random Forests, Reinforcement Learning...

Experience

Location

Campbell, CA, United States

Availability

Part-time

Preferred Environment

Spyder, Linux, Git, Jupyter Notebook, Windows, Data Modeling

The most amazing...

...thing I've done is leading a group of engineers, which resulted in a product used by many people.

Employment

  • Staff Software Engineer

    2019 - PRESENT
    Qualcomm
    • Developed an automation pipeline using Jenkins and Python to run continuous simulations, process and clean results, store them in SharePoint and MySQL using Python API, and visualize results using Plotly.
    • Created and adapted complex machine learning algorithms, models, and frameworks aligned with product proposals or roadmaps.
    • Tracked and fixed bugs using Jira as a reporting tool. Improved debugging and research skills by finding the root cause of complex issues.
    • Enabled and optimized state-of-the-art neural network models to meet the demands of customers' real-world use cases.
    • Developed innovative data analysis and visualization tools.
    Technologies: C++, Algorithms, Data Analysis, Cloud, Pandas, NumPy, Perforce, Scikit-learn, Ggplot2, Ubuntu Linux, C, Plotly, Tidyverse, Keras, Eclipse, Bash, Shell, Data Mining, Data Modeling, Data Analytics, Data Visualization
  • Machine Learning Consultant

    2015 - PRESENT
    Self-employed
    • Deployed machine learning code, models, and pipelines into production and troubleshot issues that arose.
    • Built a first-class machine learning platform from the ground up, which helps manage the entire model lifecycle, including feature engineering, model training, evaluation, versioning, deployment, online serving, and monitoring prediction quality.
    • Employed machine learning and statistical modeling techniques, such as decision trees, logistic regression, Bayesian analysis, and neural networks to develop and evaluate algorithms to improve product and system performance, quality, and accuracy.
    Technologies: Artificial Intelligence (AI), Machine Learning, PyTorch, TensorFlow, Natural Language Processing (NLP), SQL, Recommendation Systems, Data Science, SciPy, Deep Reinforcement Learning, Neural Networks, RStudio Shiny, Computer Vision, Data Visualization
  • Senior Software Engineer

    2015 - 2019
    Blue Danube Systems
    • Created a Flask-based web application to simulate the signal received by the user in a cellular system and visualize the data on Google map.
    • Designed and developed software for simulating complex wireless networks in Cpp and MATLAB.
    • Developed deep reinforcement learning models and deep neural networks, including Graph NN, CNN, RNN, and attention and transformer.
    • Designed and developed an automation pipeline to extract user and network KPIs, store data in a MySQL server, preprocess in Python, and visualize the results in Tableau.
    Technologies: Python, MATLAB, Simulations, Random Forests, Reinforcement Learning, Deep Learning, R, Docker, PySpark, Tableau
  • Research Assistant

    2011 - 2014
    Virginia Tech
    • Developed a feedforward neural network model to predict users' location using time-of-arrival data.
    • Compared the proposed model with the state-of-art solutions regarding running time and performance in terms of root mean square error (RMSE).
    • Developed a web application using Flask that compares the prediction of different machine learning models for Node.js localization based on user input data.
    Technologies: Artificial Intelligence (AI), Machine Learning, Optimization, Scraping, Web Scraping

Experience

  • ArityCode
    https://www.aritycode.com/

    The website provides a platform that helps people learn to code and solve problems.

    I created this web application based on Flask using Python. It has two databases with MySQL and MongoDB to store user information, and it captures the interaction between the user and the coding environment.

  • Indoor Location and Navigation

    A W-KNN model that predicts the location of cell phones in indoor areas using received signal strength measurements.

    I used different distance metrics such as Euclidean, correlation, and Bray-Curtis and created a quadratic optimization problem to improve location estimation accuracy using sensor data.

  • NFL 1st and Future—Impact Detection

    Developed a computer vision model that automatically detects helmet impacts on the field. Developed and compared two object detection models based on YOLO and Faster R-CNN to see helmet detection in images. Used the post-processing techniques to remove false positive detection and improve the F1 score.

  • LANL—Earthquake Project

    It predicts the time remaining before laboratory earthquakes occur from real-time seismic data. The data is a single, continuous segment of the experimental signal.

    I extracted many features from a time-series signal and developed a boosting tree to predict the time of the earthquake.

Skills

  • Languages

    Python, R, C, C++, SQL, Bash
  • Libraries/APIs

    PyTorch, TensorFlow, Pandas, NumPy, Scikit-learn, Ggplot2, Tidyverse, Keras, SciPy, PySpark
  • Tools

    Spyder, Perforce, MATLAB, Plotly, Tableau, Shell, Git
  • Paradigms

    Data Science, REST
  • Platforms

    Windows, Ubuntu Linux, Eclipse, Jupyter Notebook, Docker, Linux
  • Other

    Machine Learning, Data Analysis, Statistics, Probability Theory, Algorithms, Data Structures, Computer Vision, Simulations, Deep Learning, Optimization, Scraping, Web Scraping, Artificial Intelligence (AI), Neural Networks, Data Mining, Data Modeling, Data Analytics, Data Visualization, Software Engineering, System Design, Cloud, Natural Language Processing (NLP), Random Forests, Recommendation Systems, AWS, Deep Reinforcement Learning, Object Detection, Convolutional Neural Networks, Time Series Analysis, Research, Reinforcement Learning
  • Frameworks

    Flask, Hadoop, LightGBM, RStudio Shiny
  • Storage

    MySQL, MongoDB, PostgreSQL

Education

  • PhD in Electrical and Computer Engineering
    2011 - 2014
    Virginia Tech - Blacksburg, VA
  • Master's Degree in Electrical and Computer Engineering
    2008 - 2011
    Chalmers University of Technology - Gothenburg, Sweden

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