Research Projects

Structure Learning for Bayesian Networks

A Machine Learning research paper which explored and compared different methods and algorithms to learn the structure of Bayesian Networks. It consists of the following sections,

• Discussion of the 3 broad categories of algorithms: constraint-based, score-based, and hybrid, including their mathematical significance and examples/simulations.
• An evaluation of the optimality of the algorithms in terms of space and time complexity
• Applying each algorithm to a music recommender system to observe and compare results on real-world data.

The Impact of Political Symbols on Electoral Success in Turkey

I collaborated with a professor and fellow students to contribute towards a research paper on political propaganda in Turkey, contributing to the technical development of large-scale image collection, preprocessing, and computer vision analysis.

• I helped build a Google Maps API–based pipeline to collect and geocode approximately 750,000 panoramas from Istanbul for political propaganda analysis.
• I assisted in the preprocessing and annotation of nearly 7000 images while exploring equirectangular undistortion, different sampling techniques, and duplicate filtering.
• I trained YOLOv8 models in Roboflow to detect party and national flags, and evaluated depth estimation models for symbol surface area analysis.
• I created district-level geospatial visualizations in Python, mapping propaganda density across Istanbul.

Metric Spaces: The Mathematics of Distance and Similarity

An intuitive exposition of metric spaces designed for a general audience, introducing the formal definition of a distance function and its core properties. Explores real-world and computer science applications (such as recommendation systems, search heuristics, and edit distance) to illustrate how these abstract mathematical structures are all around us.