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

Verified Expert  in Engineering

Bio

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.

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

  • Machine Learning - 10 years
  • Data Analysis - 10 years
  • Python - 10 years
  • Deep Learning - 8 years
  • Algorithms - 7 years
  • Software Engineering - 7 years
  • C++ - 7 years
  • Data Structures - 7 years

Availability

Part-time

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

Senior 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, Python, Git, Model Development, GitHub API, GitLab CI/CD, APIs, Java, REST APIs, Architecture, Integration, GitHub, Amazon Web Services (AWS), Microservices, Databricks, Visualization, Back-end, Back-end Development, FastAPI, Data Pipelines, CSV, Excel 365, Reports, Metrics, Statistical Modeling, GNU Debugger (GDB), CMake, Data Transformation, SharePoint, ETL, Azure, Analytics, Selenium, Cron, Machine Learning Operations (MLOps), CI/CD Pipelines, Deep Neural Networks (DNNs)

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), Generative Pre-trained Transformers (GPT), SQL, Recommendation Systems, Data Science, SciPy, Deep Reinforcement Learning, Neural Networks, RStudio Shiny, Computer Vision, Data Visualization, Dashboards, Data Scraping, Model Development, GitHub API, Amazon EC2, Google Sheets, Google Cloud Platform (GCP), Image Generation, JavaScript, REST APIs, API Integration, Transformers, Recurrent Neural Networks (RNNs), Large Language Models (LLMs), ChatGPT, Generative Adversarial Networks (GANs), Architecture, Integration, GitHub, Amazon Web Services (AWS), Microservices, Databricks, Visualization, LSTM, BERT, FastAPI, Django, Node.js, Data Pipelines, CSV, Excel 365, Reports, Metrics, Statistical Modeling, Generative Artificial Intelligence (GenAI), Data Transformation, SharePoint, Language Models, Prompt Engineering, ETL, OpenAI, Azure, Time Series, OpenAI GPT-4 API, OpenAI GPT-3 API, AI Programming, Software Architecture, Analytics, Reporting, Selenium, LangChain, LoRa, Natural Language Toolkit (NLTK), PEFT, SpaCy, Beautiful Soup, Cron, Machine Learning Operations (MLOps), CI/CD Pipelines, Web Development, Deep Neural Networks (DNNs), Bots, Fine-tuning, Open-source LLMs

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, GitHub API, GitLab CI/CD, Google Sheets, APIs, HTML, CSS, API Integration, Large Language Models (LLMs), GitHub, Amazon Web Services (AWS), Visualization, LSTM, Back-end Development, CSV, Excel 365, Reports, Metrics, Statistical Modeling, Data Transformation, ETL, Beautiful Soup, Cron

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, Large Language Models (LLMs), GitHub

Experience

Object Detection and Classification Using Satellite Images

I created an end-to-end computer vision model to detect and classify satellite images. Also, I implemented specialized pre-processing and post-processing techniques to handle large satellite images. The entire pipeline was developed in Python using PyTorch and TensorFlow library.

RStudio Shiny App for Pooling Data

I created the RStudio Shiny app for visualizing and analyzing poll data. The application involves intensive data processing and analysis. The pipeline also includes extracting survey data from SurveyMonkey APIs and loading data from SPSS.

ArityCode

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.

3D Object Detection for Autonomous Vehicles

I developed a 3D object detection algorithm for vehicles and pedestrians using 2D images and a 3D LIDAR point cloud.
I then used mmdetection3d and PyTorch library to evaluate and compare different models.
The prediction outputs include localization, classification, and direction.

Education

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

Skills

Libraries/APIs

PyTorch, TensorFlow, Pandas, NumPy, Scikit-learn, Ggplot2, Tidyverse, Keras, SciPy, GitHub API, LSTM, Natural Language Toolkit (NLTK), SpaCy, Beautiful Soup, PySpark, REST APIs, Node.js

Tools

Spyder, Git, Perforce, MATLAB, Plotly, Tableau, Google Sheets, GitHub, Cron, Shell, GitLab CI/CD, ChatGPT, GNU Debugger (GDB), CMake, MATLAB Statistics & Machine Learning Toolbox

Languages

Python, R, C++, SQL, C, Bash, HTML, CSS, Java, JavaScript, SPS

Frameworks

Flask, RStudio Shiny, Selenium, Hadoop, LightGBM, Django

Paradigms

ETL, REST, Microservices

Platforms

Windows, Ubuntu Linux, Amazon Web Services (AWS), Eclipse, Jupyter Notebook, Amazon EC2, SharePoint, Docker, Databricks, Linux, Visual Studio Code (VS Code), Google Cloud Platform (GCP), Azure

Storage

Data Pipelines, MySQL, MongoDB, PostgreSQL

Other

Machine Learning, Data Analysis, Statistics, Probability Theory, Algorithms, Data Structures, Natural Language Processing (NLP), Computer Vision, Simulations, Data Science, Deep Learning, Optimization, Scraping, Web Scraping, Artificial Intelligence (AI), Neural Networks, Data Mining, Data Modeling, Data Analytics, Data Visualization, Dashboards, Data Scraping, Model Development, APIs, Architecture, Integration, Visualization, BERT, Back-end, Back-end Development, CSV, Excel 365, Reports, Metrics, Statistical Modeling, Data Transformation, Language Models, Analytics, CI/CD Pipelines, Deep Neural Networks (DNNs), Fine-tuning, Open-source LLMs, Software Engineering, System Design, Cloud, Random Forests, Recommendation Systems, Deep Reinforcement Learning, Object Detection, Convolutional Neural Networks (CNNs), Time Series Analysis, Research, Generative Pre-trained Transformers (GPT), Image Generation, API Integration, Transformers, Recurrent Neural Networks (RNNs), Large Language Models (LLMs), Generative Adversarial Networks (GANs), FastAPI, Generative Artificial Intelligence (GenAI), Prompt Engineering, OpenAI, Time Series, OpenAI GPT-4 API, OpenAI GPT-3 API, AI Programming, Software Architecture, Reporting, LangChain, LoRa, PEFT, Machine Learning Operations (MLOps), Web Development, Bots, Reinforcement Learning, Classification

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