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.
Portfolio
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
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
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.
Machine Learning Consultant
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.
Senior Software Engineer
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.
Research Assistant
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.
Experience
Object Detection and Classification Using Satellite Images
RStudio Shiny App for Pooling Data
ArityCode
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
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
LANL—Earthquake Project
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 then used mmdetection3d and PyTorch library to evaluate and compare different models.
The prediction outputs include localization, classification, and direction.
Education
PhD in Electrical and Computer Engineering
Virginia Tech - Blacksburg, VA
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), 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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