I headed to the University of Utah right after the 2023 AAAI Conference. Prof. Ji was very hospitable. I am sincerely grateful to him. The weather in Salt Lake City was gloomy; in fact, there was heavy snowfall, forcing me to stay home. At the C³ Lab, I introduced the quantum machine learning techniques I had worked on and discussed how to apply them in wireless communication and communication network environments for two months. Our main topic was a Contents Recommendation System, similar to Netflix. We planned to apply quantum machine learning with projection value measurement. Prof. Ji and Dr. Xiang provided many comments, and I am grateful for their help. In particular, discussing the machine learning optimization problem within the Lyapunov framework with the professor was truly innovative. It's a wonderful memory.

When I first joined the lab, I knocked on Prof. Kim's office to address a delay. AIMLab collaborates with various professors, which offers the advantage of learning a lot from them. This environment significantly contributed to my skill development. Initially, from Prof. Park at Deakin University, I learned many AI analysis methods and how to refine experiments. Prof. Choi from Kyung Hee University taught me how to refine mathematical formulas. I delved deep into wireless communication channel modeling with Prof. Ko at Korea University and Prof. Lim from Pukyong National University. From Prof. Lee at the University of Wisconsin-Madison, I gained a deep understanding of how to prove concepts through experimentation, and from Prof. Mohaisen at the University of Central Florida, I learned about presentation techniques and paper writing. Additionally, I was fortunate enough to work with esteemed professionals like Prof. Molisch from the University of Southern California and Prof. Bennis from the University of Oulu. 

I undertook numerous tasks for industrial-academic research (Samsung, Hyundai, Cipherome) and national research projects, such as setting up quantum computing-related codes and multi-agent reinforcement learning libraries. Also, as one of the early members of the lab, I was responsible for setting up most of the equipment servers and building almost all of the code used in our research. It was a very arduous and demanding task, but seeing the fruitful results brought me great joy. Furthermore, generously contributing to ensure the growth of my juniors in the lab felt rewarding.  

After finishing the second semester of my sophomore year in 2018, I pondered, "What meaningful activity can yield results with a low opportunity cost?" As a result of this reflection, I decided to start developing systems using a computer. From January 2019, I began web crawling and server construction, during which I carried out a project for Namdong Power Development. Later, I developed an interest in deep learning and took a course in computer vision, allowing me to accumulate the necessary major knowledge. During the summer, I was able to implement the theories I learned through hand-written digit recognition and provided it as an API. Additionally, I developed a student learning support platform. After the outbreak of COVID-19, I deployed the learning support platform I had developed to an academy, demonstrating its real-world application.