Scientometric mapping of Türkiye's computer engineering graduate research landscape

This study examines 12,778 master's and PhD theses in Computer Engineering from universities in Türkiye. The theses were completed between 1984 and 2024. The main goal is to show how postgraduate research has grown, changed, and improved over the last 40 years. All theses I used came from the YÖK National Thesis Center, since that is where Türkiye keeps its graduate work. I first cleaned the texts myself with simple steps like joining title+abstract, doing basic lemmatization, and catching a few n-grams. After that, I tried several modelling tools in no fixed order. BERTopic was the main one because it uses neural embeddings, but I also checked LDA and even TF-IDF just to compare what they highlight. They did not produce the same structure, yet together they helped me understand the overall shape of the data. In the end, the models showed nearly ninety topic groups. The number of theses also rose a lot after 2015 as more graduate programs opened. Some themes suddenly became popular too — for example cybersecurity, blockchain, agricultural AI, and medical image work. There was also a clear difference between degree levels. PhD theses usually went toward theory related topics like optimization, privacy, or advanced modelling, while master's work stayed closer to practical tasks. Master's theses are usually more practical and focus on things such as face recognition, mobile apps, or smart home systems. The study also found that 33.6% of the theses do not match the main topic groups very well. These studies can be seen as "unconventional" or outliers. Overall, this study is one of the first large-scale studies of computer engineering research in Türkiye. The results show both the growth in the number of theses and the increasing variety of topics.