Parsa Abbasi is a research-focused machine learning engineer and doctoral candidate at Paderborn University with eight years of experience spanning graph neural networks, NLP, and applied data science. He develops explainable ML methods for knowledge graphs under Dr. Stefan Heindorf and previously improved relational GNN architectures by reducing parameters and addressing static attention issues. As a data scientist and course author at Quera, he designed scalable online ML coursework, enhanced automated grading, and answered 450+ student queries while maintaining a 4.9/5 rating. His early work includes a peer-reviewed Persian sentiment analysis system using novel text augmentation techniques, and practical experience building MPI-based distributed systems. Comfortable bridging research and teaching, he blends rigorous academic inquiry with hands-on engineering to produce efficient, interpretable models for real-world graph and language tasks.
8 years of coding experience
3 years of employment as a software developer
Bachelor of Engineering - BE, Computer Engineering, Bachelor of Engineering - BE, Computer Engineering at Guilan University
Master of Science - MS, Artificial Intelligence, Master of Science - MS, Artificial Intelligence at Iran University of Science and Technology
Doctor of Philosophy - PhD, Computer Science, Doctor of Philosophy - PhD, Computer Science at Universität Paderborn - Paderborn University
Web scraper implementations for a variety of websites.
Contributions:50 commits, 48 PRs, 45 pushes in 21 days
implementationsdata-analysispythonscrapybs4
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