Contact
[email protected]
Github
seungwoos
Linkedin
Seungwoo Kim
I’m a machine learning engineer interested in making our lives beneficial and productive. I had worked in medical imaging and computer vision, and currently digging on Large Language Models, but not limited to.
As of December 2023, I am working as an AI Engineer at 3billion. Previously, I worked as a (front-end) software engineer at Dancers Connect.
Experiences
Dec. 2023 - Present
- Working on large language models (LLMs) for diagnosis.
- From instruction dataset generation to fine-tuning PEFT-based open-source LLM model.
- Building Retrieval Augmented Generation (RAG) using knowledge using dify.
Oct. 2023 - Dec. 2023
- Worked as a front-end engineer, developed and maintained services at danceapply.com using ReactJS and Typescript.
Research Assistant @ UNIST
Mar. 2021 - Jun. 2023
- Implemented Neural Diffusion Processes architecture in PyTorch before the official code was released. [Code]
- Planned and conducted 2D image experiments using the proposed diffusion model in distributed setting. [Code]
- Submitted to Conference, codes and papers will be released soon.
- Conducted experiments on 2D cytology and CT image generation using proposed diffusion model with acceleration.
Research Intern @ Tomocube AI Team
Mar. 2021 - Jul. 2022
- Conducted experiments on thyroid malignancy classification and proposed the combination method of bright field and refractive index image. [Code]
- Submitted to Scientific Reports
- Implemented post-stenting stent prediction of Intravascular Ultrasound (IVUS) cardiovascular model based on vision transformer, and conducted experiments using different modalities (image and clinical information)
Collaborative Research with YUHS
Jan. 2022 - Jul. 2022
- Conducted experiments on brain malignancy differentiation of 2D CT image from YUHS using Deep Ensembles and Neural Bootstrapper
- Submitted to Journal, codes and papers will be released soon.
- Conducted experiments on tumor segmentation of 3D Medical Segmentation Decathlon with synthetically generated imbalanced and noisy label datasets
Teaching Assistant @ UNIST
Sep. 2022 - Dec. 2022
- [AI502] Principles of Deep Learning (Instructor: Sungbin Lim)