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parya shadbakhsh

@paryashadbakhsh

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Data Scientist at Biogenity who developed biomedical NLP pipelines to turn scientific literature into structured data.

Denmark

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At Biogenity, I developed reproducible Python data and machine learning workflows for an EU Horizon research project. I also built pipelines that combined biomedical sources with data-quality checks and provenance tracking.

I developed transformer-based NLP pipelines using BioBERT-related models, Hugging Face Transformers, PubTator, and biomedical text-mining tools to turn scientific literature into structured data.

At Aalborg University’s Wireless Communication Networks Section, I developed machine learning and computer vision pipelines for robotic systems. I integrated YOLOv5 models, OpenCV, and Intel RealSense cameras, and improved robotic task success from approximately 66% to 95%.

For my M.Sc. thesis, I trained graph neural networks and graph autoencoders to analyse clinical samples with pathway knowledge graphs. I also developed a PyTorch and scikit-learn pipeline for multivariate clinical time-series analysis.

Experience

Work history, roles, and key accomplishments

Data Scientist

Biogenity

Feb 2025 - Dec 2025 (10 months)

Developed reproducible Python-based data and machine learning workflows for an EU Horizon research project, including data ingestion, preprocessing, feature preparation, and validation. Built and maintained data pipelines integrating heterogeneous biomedical sources and developed transformer-based NLP pipelines using BioBERT-related models and Hugging Face Transformers.

Python Data Pipelines Data Quality NLP Hugging Face Transformers BioBERT PubTator MLFlow Git CI CD Docker

Research Assistant

Aalborg University

Jun 2022 - Jun 2024 (2 years)

Developed reproducible Python-based machine learning and computer vision pipelines for real-world robotic systems. Trained and tuned image recognition models using YOLOv5 and PyTorch, integrated them with OpenCV and Intel RealSense cameras, and improved robotic task success from approximately 66% to 95%.

Python Machine Learning Computer Vision YOLO V5 PyTorch OpenCV Intel RealSense MLFlow Git CI CD Docker

Front-end Developer

Faranegar

Jun 2018 - Sep 2018 (3 months)

Developed a React Native mobile application in a collaborative software engineering environment using version control, reusable components, debugging, and maintainable development practices.

React Native JavaScript Git Debugging Version Control

Education

Degrees, certifications, and relevant coursework

Aalborg University

M.Sc., Computer Science

2022 - 2024

Grade: 12/12

M.Sc. in Computer Science with a thesis on enriching clinical sample analysis with graph autoencoders and pathway knowledge graphs, achieving a grade of 12/12.