SeungYoung OH

163 Seoulsiripdae-ro, Dongdaemun-gu, Seoul 02504, Republic of Korea

Hello, my name is Seung Young OH and I am interested in artificial intelligence, with a particular focus on datasets and model similarity. As an AI enthusiast, I am fascinated by the endless possibilities that this field holds for the future and am dedicated to contributing to its growth and development.

I have a strong academic background in computer science and have honed my skills in machine learning, data analysis, and programming through coursework, personal projects and Lab experience. I am adept at utilizing programming languages such as Python and C, as well as tools such as TensorFlow, PyTorch and Scikit-learn, to develop and implement innovative AI solutions.

Thank you for considering my introduction, and I look forward to discussing my interests and experiences in further detail.

selected publications

WATAA: Web Alternative Text Authoring Assistant for Improving Web Content Accessibility

Jeong, Hyeonhak, Chun, Minki, Lee, Hyunmin, Oh, Seung Young, and Jung, Hyunggu

In Companion Proceedings of the 28th International Conference on Intelligent User Interfaces 2023
Visualizing the Carbon Intensity of Machine Learning Inference for Image Analysis on TensorFlow Hub

Yoo, Taewon, Lee, Hyunmin, Oh, SeungYoung, Kwon, Hyosun, and Jung, Hyunggu

In Companion Publication of the 2023 Conference on Computer Supported Cooperative Work and Social Computing 2023

Abs

The increasing performance of machine learning (ML) models necessitates greater computing resources, contributing to rising carbon intensity in ML computing and raising concerns about computational equity. Previous studies focused on developing tools that enable model developers to view the carbon intensity of the ML models in the training process. Still, little is known about how to support ML developers in online communities to explore the carbon intensity of ML models during inference. We developed MIEV, a model inference emission visualizer, that supports ML developers on TensorFlow Hub to explore the carbon intensity of image domain models during the model Inference phase. We also provide insights into designing technologies that promote collaborative work among ML developers to drive sustainable AI development processes. To the best of our knowledge, this is the first attempt to interactively visualize the carbon intensity of ML models in online communities during the Inference phase.