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

Verified Expert  in Engineering

Machine Learning Developer

Campbell, CA, United States
Toptal 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.


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




Preferred Environment

Spyder, Linux, Git, Jupyter Notebook, Windows, Data Modeling, Visual Studio Code (VS Code)

The most amazing...

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

Work Experience

Staff Software Engineer

2019 - PRESENT
  • 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
  • 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), GPT, Generative Pre-trained Transformers (GPT), 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

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.


Python, R, C, C++, SQL, Bash


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


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


Data Science, REST


Windows, Ubuntu Linux, Eclipse, Jupyter Notebook, Docker, Amazon Web Services (AWS), Linux, Visual Studio Code (VS Code)


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, Deep Reinforcement Learning, Object Detection, Convolutional Neural Networks, Time Series Analysis, Research, GPT, Generative Pre-trained Transformers (GPT), Reinforcement Learning


Flask, Hadoop, LightGBM, RStudio Shiny


MySQL, MongoDB, PostgreSQL

2011 - 2014

PhD in Electrical and Computer Engineering

Virginia Tech - Blacksburg, VA

2008 - 2011

Master's Degree in Electrical and Computer Engineering

Chalmers University of Technology - Gothenburg, Sweden