I'm an AI researcher who is interested in computer vision, natural language processing, and generative models.

You can find more about me on my Stack Overflow, DACON account, and LinkedIn.

Some things I wanna share:

• I earned a Master's degree from the Seoul National University of Science and Technology (SNUT). Yes, I know, it's not SNU, it's SNU-T.

• During my master's degree, my field of study was generative models with a main research focus on GANs. After StyleGAN3 was published, I wished I could work with Tero Karras to see how he came up with those ideas.

• Besides, I believe the diffusion architecture shows more promising results than the GANs approach, thanks to the fine-grained control in training instead of relying on true/false feedback from the discriminator. (no more mode collapse)

• I first discovered the word "machine learning" in 2019. When I was at the beginning of my third year of bachelor's, one of my best friends suggested we take this course (it used to be on Coursera). We spent 8 to 12 hours a day, continuously for 14 days, to finish this course, covering all lectures, videos, notes, and assignments (in Octave).

• Then I had my first intern job, which wasn't ideal, but still a valuable experience. My friend and I then completed a significant project on monitoring traffic for our graduation. Project details can be checked here.

• Currently, I'm diving into NLP to gain a better understanding and to participate in some competitions on Kaggle. Additionally, I believe that multi-modal learning is the future of machine learning, although it might not be prevalent in this era. I think models that focus on specific tasks are currently more valuable in the market.

Note: Since I noticed a lack of standard packages for GANs evaluation metrics such as IS, FID, Precision, and Recall, I implemented one myself. You can check the details at this pip link. Feel free to use it or raise any issues if you find any mistakes in my implementation.

I love joining competitions and reading solutions to learn how to handle machine learning models with real-world data. Since I don't have enough resources to deal with big data, platforms like DACON are more suitable for me at this time. Here are some of my achievements:

• Camera Image Quality Improvement AI Competition, hosted by LG AI Research and organized by DACON, I participated solo. Out of 228 teams, I reached 10th place, which placed me in the top 4%. The task involved developing an AI model to improve camera image quality degraded by light blur.

• Monthly Daycon Computer Vision Outlier Detection Algorithm Competition, organized by DACON, my labmate and I participated. We reached 13th place out of 480 teams, placing us in the top 3%. The task involved the development of a computer vision algorithm to classify the type and state of objects.

• The 2nd KRX Stock Investment Algorithm Competition, hosted by Korea Exchange (KRX), sponsored by Koscom, and organized by DACON, I participated solo. I reached 41st place out of 400 teams, placing me in the top 10%. The task involved using capital market data and public data to create a stock investment algorithm capable of expecting high stability and returns.

• Handling questions and answers about wallpaper defects: Hansol Deco Season 2 AI Contest, hosted by Hansol Deco (한솔데코) and organized by DACON, I participated solo. I reached 18th place out of 557 teams, placing me in the top 3%. The task involved developing an AI model with in-depth question-and-answer processing capabilities related to wallpaper.