About me

I have always been the kind of person who wants to understand how things work from the inside. Even as a child, I was fascinated by toys, games, and anything mechanical or electronic. I was not satisfied with simply using things the way they were meant to be used. I wanted to take them apart, explore their pieces, and see whether I could put them back together in a different way.

Most of the time, the result was not exactly successful. Many toys ended up broken, demolished, or impossible to rebuild. But every once in a while, I managed to take something apart and create something new from it. Those moments were incredibly exciting for me. They gave me a feeling of discovery and possibility, as if I had unlocked a small secret about how the world worked. Looking back, I think that curiosity was one of the earliest signs of the path I would eventually follow.

As I grew older, my interest shifted naturally toward video games, electronics, and computers. I spent a lot of time in front of my computer, not only playing games but also experimenting, learning, and sometimes destroying the operating system more than once in the same day. It was frustrating at times, but it was also fun. Every mistake taught me something. Every problem became a puzzle. That period of trial and error was where my love for computer science, computer engineering, and technology truly began.

During my childhood and teenage years, I dreamed of becoming a developer, a computer engineer, or someone who worked deeply with computers. I did not yet know the exact title or direction I wanted, but I knew that I wanted my future to be connected to computer science and engineering. When the time came to choose a university path, I entered the field of Information Technology, specializing in networking.

Networking gave me a valuable foundation. It helped me understand many important basics of computing, systems, and communication technologies. However, I quickly realized that it was not the field that excited me most. I respected it, and I learned from it, but I did not feel that it was where I truly belonged. It did not give me the same sense of curiosity, creativity, or joy that I felt when I was exploring computers, programming, and intelligent systems.

After finishing college, I decided to give computer science and computer engineering a second chance in a way that felt closer to my real interests. I began a master’s degree, and this time I had more freedom to choose the area that inspired me. I focused on computer engineering, and my thesis was related to deep learning and machine learning. That decision changed everything for me.

I loved the field immediately. I enjoyed learning the theory, understanding the algorithms, and applying them to real problems. Deep learning felt almost magical because it combined mathematics, engineering, and intelligence in a way that could produce powerful results. For the first time, I felt that I was not only studying a subject, but also building a future around something I truly cared about.

I also feel very lucky because my growing interest in artificial intelligence happened during a historic moment in the field. Around the time of my master’s studies, the paper Attention Is All You Need had recently been published in 2017. That paper introduced the Transformer architecture, which changed the direction of deep learning and natural language processing. Its attention mechanism offered a new way for models to understand relationships between words and sequences, allowing them to consider context more effectively than many previous approaches.

Before Transformers, recurrent neural networks and related architectures were widely used for sequence modeling. They were important and influential, but they also had limitations, especially when dealing with long-range dependencies and large-scale training. The Transformer architecture opened a new door. When I read about it, I was fascinated. It felt like a major breakthrough, and I could sense that it would shape the future of artificial intelligence.

A few years later, systems such as ChatGPT showed the world how powerful these ideas could become when scaled and developed further. Suddenly, artificial intelligence was no longer only a research topic discussed by specialists. It became something everyone was talking about, using, and trying to understand. For me, that was deeply motivating. The field I loved was becoming one of the most important technological movements of our time.

That is why I consider myself fortunate. The area that naturally captured my curiosity artificial intelligence, machine learning, deep learning, and intelligent algorithms became the center of global attention. It made me even more determined to continue on this path.

Today, I am pursuing my PhD, and my research is focused on artificial intelligence, particularly Transformer-based models and multimodal fusion. I am interested in how different types of data and modalities can be combined more effectively, including early fusion, late fusion, adaptive fusion, attention-based fusion, and other multimodal learning strategies. My goal is to explore how these models can better learn from multiple sources of information and produce stronger, more meaningful results.

From taking apart toys as a child to studying advanced AI models as a researcher, my journey has always been driven by curiosity. I have always wanted to understand systems, break them down, rebuild them, and discover new possibilities. That same excitement I felt when I managed to create something new from a broken toy is still with me today only now, it guides my work in computer engineering and artificial intelligence.