Beste Ekmen

- Built a web-scraping and data-processing pipeline (Python, Beautiful Soup, Requests) that collected large volumes of painting images and text metadata from auction websites. - Ran regression and cluster analysis (scikit-learn, XGBoost) and improved the previous model's accuracy by 20%. - Explained model predictions with SHAP (explainable AI) and visualised results for human interpretation.

Developed a Node.Js based UI for 4K STB (set-top-box) using HTML5, CSS, JS, and a Dojo-based framework. Conducted research on evaluating the usability of STB interfaces with heuristic checklists and user experience tests, created a usability guideline for TV and STB interfaces. Conducted research on evaluating the feature usage and access patterns for STB feature recommender systems.

Participated in the intensive Java Summer School organized with OBSS and Oracle collaboration.

Programmed iRobiQ robots for a preschool training system to provide reactions to acts and emotions of the user with the ROCOS platform, programmed Nao robots for demonstration purposes.

Developed and designed a “Database Management System”, internal and customer requested websites using PHP, MySQL, JavaScript, HTML5, and CSS. Designed UI and managed content of mobile applications.

IMPRS-CS Master's scholarship (Max Planck Institute for Informatics). Master's thesis "Inpainting Artworks with Style in Mind" (1.8): GAN-based reconstruction of damaged paintings that preserves artistic style, with style metrics defined from Gram matrices of pretrained CNN features. Advanced cou

Total Credits: 30 (ECTS) Courses: Computer Graphics, Operating Systems, Software Engineering, Project Management, Introduction to Java Programming, Introduction to Materials Science

(Double Major) Thesis: Usability heuristics for the set‐top box and TV interfaces. (Supervisor: Doç. Dr. Çiğdem Altın Gümüşsoy)

Total Credits: 154 (ITU Credit System) Thesis: From 2D to 3D real-time expression transfer for facial animation (Supervisor: Prof. Dr. Hazım EKENEL ITU Honor List and High Honor List (top 3% of graduates)