Hamidreza Yaghoubi
College Park, Maryland, United States
Summary
🤩
Rockstar🎓
Top SchoolHamidreza Yaghoubi is a Ph.D. student in Computer Engineering at the University of Maryland with six years of experience applying machine learning and data science to real-world problems. He has worked on robustness, generalizability, and interpretability of deep neural networks in research roles and delivered production-focused solutions for traffic forecasting, ETA, and location search at TAPSI. As a teaching assistant for graduate-level deep learning and biomedical imaging courses, he combines strong academic foundations with hands-on mentoring. His GitHub contributions include practical ML notebooks—like a decision tree implementation for a car evaluation dataset—demonstrating attention to reproducible analysis and visualization. Based in College Park, MD, he brings a blend of research rigor and product-minded engineering informed by both industry and academic projects.
6 years of coding experience
2 years of employment as a software developer
Bachelor of Engineering - BE, Computer Engineering, Bachelor of Engineering - BE, Computer Engineering at CE @ Sharif University of TechnologyAstronomy and Astrophysics, Astronomy and Astrophysics at Young Scholars ClubDoctor of Philosophy - PhD, Computer Engineering, Doctor of Philosophy - PhD, Computer Engineering at University of MarylandHigh School Diploma, Mathematics, High School Diploma, Mathematics at Allameh Helli school
Github Skills (12)
scikit10
data-analysis10
jupyter-notebook10
machine-learning10
python10
scikit-learn10
ai9
artificial-intelligence9
data-visualization9
data-visualisation9
pandas9
data-visualizations9
Programming languages (4)
Github contributions (5)
SharifiZarchi/Introduction_to_Machine_Learning
Sep 2022 - Dec 2022
Machine Learning Course, Sharif University of Technology
Role in this project:
Data Scientist
Data ScientistContributions:9 commits, 7 pushes in 2 months
Contributions summary:Hamidreza added a Jupyter Notebook detailing the implementation and exploration of a Decision Tree classifier for a car evaluation dataset. They focused on data loading, preprocessing, model training, accuracy evaluation, and visualization. The commits demonstrate a clear understanding of machine learning model development, including the application of relevant libraries and techniques.
pythonmachine-learningdepartmentengineeringsharif-university
deepmancer/full-stack-fastapi-ftgo
Jul 2024 - Aug 2024
Contributions:22 PRs, 88 pushes, 10 branches in 23 days
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