I’m a Data Science student at IT University of Copenhagen with an interest in machine learning, data analysis, finance and applied AI.

• 🔭 Currently working on: Cancerous lesion detection via image processing, feature extraction, and machine learning
• 📫 Contact: russudum@gmail.com
• 🌍 Based in: Copenhagen, Denmark

Using the PAD-UFES-20 dataset, we cleaned and analyzed the data, extracted relevant features, and trained machine learning models capable of identifying malignant skin lesions.

Tech stack: Python

Highlights:

• Implemented Logistic Regression, Random Forest, kNN, and SVM classifiers
• Developed and tested two custom AUC-boosting techniques to improve model performance
• Performed feature engineering and model evaluation using multiple performance metrics

🔗 GitHub Repo: https://github.com/LauritsRich/2026-PDS-Tigers

Analyzed bias in two sentiment analysis models using a dataset provided by Novo Nordisk containing patient notes and sentiment predictions from SieBERT and DistilBERT models.

Tech stack: R

Highlights:

• Cleaned and prepared the dataset for statistical analysis
• Used data visualization techniques to identify potential patterns and biases
• Applied statistical tests including Chi-square and Mann–Whitney tests to validate findings

🔗 GitHub Repo: https://github.com/Dumitru-dev-44/Bias-in_LLMs

Developed a blog publishing platform with user authentication and database integration as part of a Flask web development project.

Tech stack: Python, Flask, SQL, HTML, CSS

Highlights:

• Built a dynamic website using Flask, HTML, and CSS
• Implemented user authentication with salted and hashed passwords
• Connected the application to a database for secure credential storage and blog management

🔗 GitHub Repo: https://github.com/Dumitru-dev-44/day-71-final-blogpost-publishing

Languages:

• Python
• R

Tools & Technologies:

• Git
• Flask
• REST APIs
• Machine Learning
• Data Analysis

• LinkedIn: https://www.linkedin.com/in/russu-dumitru-4a76b8380/
• Email: russudum@gmail.com