Milad Mirzaei 

- Information and Computer Science Student -

Projects:

connected vehicles

             

computer vision

Connected and autonomous vehicles:
 

I utilize Python programming to explore various aspects of connected and autonomous vehicles, including deep learning for visual perception, wheeled robot kinematics, localization, motion planning, and robot control. Technologies used include pytorch, sklearn, CUDA, numpy, pandas, PID algorithm, YOLO classification, and HPC (High-Performance Computing) with GPU acceleration.

Reinforcement Learning-Based Maze Solver:

I compared the efficacy of RL implemented with Keras and gymnasium against a modified A* navigation model, assessing performance in evading dynamic and static obstacles within a 2D maze environment.

LLM

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Reinforcement Learning

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Automated CI/CD Pipeline for Web Deployment:


I set up a fully automated pipeline for deploying a website, covering CI/CD, testing, and software updates. I used Vagrant, VirtualBox, Maven, and Ansible for environment provisioning and project management. I built an automated CI-server process with GitLab, Docker, and Unicorn for seamless deployment. I also integrated testing with npm, Maven, Liquibase, TestNG, and Mockito to optimize workflows and ensure reliability.

Big Data & Machine Learning Projects:

I worked on a Big Data project using Spark-Scala, Spark SQL, and Apache Spark for tasks like Modified Word Counting and Linear Regression. I also applied Geo-Spatial & Temporal Data Analysis and built Recommender Systems with Matrix Factorization. Additionally, I utilized machine learning models with sklearn, joblib, pandas, and seaborn to analyze datasets in banking, medical, and social activity domain

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