Reza is available for hire

Reza Vaghefi

Verified Expert in Engineering

Machine Learning Developer

Location

Campbell, CA, United States

Toptal Member Since

February 3, 2022

Reza拥有电气和计算机工程硕士和博士学位. 作为一名在机器学习和数据分析方面拥有十多年经验的专业人士, 他擅长不同的编程语言，比如Python, R, C, C++, and MATLAB. Reza在软件工程、算法和数据结构方面有很强的背景.

Portfolio

Qualcomm

c++，算法，数据分析，云，Pandas, NumPy, Perforce, Scikit-learn...

Self-employed

人工智能，机器学习，PyTorch, TensorFlow...

Blue Danube Systems

Python, MATLAB，仿真，随机森林，强化学习...

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...

...我做过的一件事就是带领一群工程师，开发出了一个被很多人使用的产品.

Work Experience

Senior Staff Software Engineer

2019 - PRESENT

Qualcomm

使用Jenkins和Python开发自动化管道来运行连续模拟, 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. 通过找到复杂问题的根本原因，提高了调试和研究技能.
启用并优化了最先进的神经网络模型，以满足客户实际用例的需求.
开发创新的数据分析和可视化工具.

Technologies: c++，算法，数据分析，云，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, GDB, CMake, Data Transformation, SharePoint, ETL, Azure, Analytics, Selenium, Cron, Machine Learning Operations (MLOps), CI/CD Pipelines, Deep Neural Networks

Machine Learning Consultant

2015 - PRESENT

Self-employed

Deployed machine learning code, models, 管道投入生产，并解决出现的问题.
从头开始建立了一流的机器学习平台, which helps manage the entire model lifecycle, including feature engineering, model training, evaluation, versioning, deployment, online serving, and monitoring prediction quality.
采用机器学习和统计建模技术, such as decision trees, logistic regression, Bayesian analysis, 和神经网络开发和评估算法，以提高产品和系统的性能, quality, and accuracy.

Technologies: 人工智能，机器学习，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, 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

Senior Software Engineer

2015 - 2019

Blue Danube Systems

创建了一个基于flash的web应用程序来模拟用户在蜂窝系统中接收到的信号，并在谷歌地图上可视化数据.
在Cpp和MATLAB中设计并开发了复杂无线网络仿真软件.
开发了深度强化学习模型和深度神经网络, including Graph NN, CNN, RNN, and attention and transformer.
设计并开发了一个自动化管道来提取用户和网络kpi, store data in a MySQL server, preprocess in Python, and visualize the results in Tableau.

Technologies: Python, MATLAB，仿真，随机森林，强化学习, 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

开发了一个前馈神经网络模型，利用到达时间数据预测用户的位置.
将所提出的模型与基于均方根误差(RMSE)的运行时间和性能的最新解决方案进行比较。.
使用Flask开发了一个web应用程序，比较不同机器学习模型对Node的预测.js localization based on user input data.

Technologies: Artificial Intelligence (AI), Machine Learning, Optimization, Scraping, Web Scraping, Large Language Models (LLMs), GitHub

Experience

基于卫星图像的目标检测与分类

我创建了一个端到端的计算机视觉模型来检测和分类卫星图像. Also, 我实现了专门的预处理和后处理技术来处理大型卫星图像. 整个管道是使用PyTorch和TensorFlow库在Python中开发的.

RStudio Shiny App for Pooling Data

我创建了RStudio Shiny应用程序，用于可视化和分析投票数据. 该应用程序涉及密集的数据处理和分析. 该管道还包括从SurveyMonkey api提取调查数据和从SPSS加载数据.

ArityCode

http://www.aritycode.com/

该网站提供了一个平台，帮助人们学习编码和解决问题.

我使用Python创建了这个基于Flask的web应用程序. 它有MySQL和MongoDB两个数据库来存储用户信息, 它捕获了用户和编码环境之间的交互.

Indoor Location and Navigation

一种W-KNN模型，利用接收到的信号强度测量来预测手机在室内的位置.

I used different distance metrics such as Euclidean, correlation, 和布雷-柯蒂斯，并创建了一个二次优化问题，以提高利用传感器数据的位置估计精度.

NFL 1st and Future—Impact Detection

开发了一种计算机视觉模型，可以自动检测头盔对场地的影响. 开发并比较了基于YOLO和Faster R-CNN的两种目标检测模型，看图像中的头盔检测. 采用后处理技术去除假阳性检测，提高F1分数.

LANL—Earthquake Project

它根据实时地震数据预测实验室地震发生前的剩余时间. 数据是实验信号的单个连续片段.

我从时间序列信号中提取了许多特征，并开发了一个增强树来预测地震的时间.

3D Object Detection for Autonomous Vehicles

我使用2D图像和3D激光雷达点云开发了一种针对车辆和行人的3D物体检测算法.
然后我使用mmdetection3d和PyTorch库来评估和比较不同的模型.
预测输出包括定位、分类和方向.

Skills

Languages

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

Frameworks

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

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, GDB, CMake, MATLAB Statistics & Machine Learning Toolbox

Paradigms

Data Science, 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, 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, Software Engineering, System Design, Cloud, Random Forests, Recommendation Systems, Deep Reinforcement Learning, Object Detection, Convolutional Neural Networks (CNN), Time Series Analysis, Research, GPT, 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, Reinforcement Learning, Classification

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

Collaboration That Works

How to Work with Toptal

在数小时内，而不是数周或数月，我们的网络将为您直接匹配全球行业专家.

Share your needs

在与Toptal领域专家的电话中讨论您的需求并细化您的范围.

Choose your talent

在24小时内获得专业匹配人才的简短列表，以进行审查，面试和选择.

Start your risk-free talent trial

与你选择的人才一起工作，试用最多两周. Pay only if you decide to hire them.

Top talent is in high demand.

Start hiring