profile
Seunghyuk Oh
CV
profile
Seunghyuk Oh
Master's Student in Artificial Intelligence

I am a Master's student at KAIST ALIN Lab, specializing in AI under Prof. Jinwoo Shin.

Previously, as a co-founder of a startup, I honed my skills as a software engineer, using technology to tackle complex challenges. This entrepreneurial journey ignited my passion for artificial intelligence, leading me to transition into AI research. Now, I’m dedicated to contributing to the advancement of AI by merging my practical experience with academic research.

Publication

  1. [W1] Sparsified State-Space Models are Efficient Highway Networks
    NeurIPS 2024 ENLSP (Oral)
  2. [C2] Optimized Feature Generation for Tabular Data via LLMs with Decision Tree Reasoning
    NeurIPS 2024
  3. [C1] Hierarchical Context Merging: Better Long Context Understanding for Pre-trained LLMs
    ICLR 2024
C: Conference, W: Workshop, P: Pre-print, *: Equal contribution

Work Experience

  1. Chief Technology Officer
    Weebut
    2021.01 ~ 2022.12
    - As the tech team leader, I was responsible for overseeing all technical aspects of our products.
    - I developed various business solutions, such as MEDIAR and iN!T, by implementing them as software through JavaScript-based frontend and backend development.
  2. Undergrad. Research Intern
    KAIST ALIN Lab
    2020.06 ~ 2020.12
    - For my undergraduate graduation research, I studied MirrorGAN, a model that learns the relationship between images and captions by converting a caption into an image and then back into a caption.
    - While MirrorGAN is typically trained using fully supervised learning, we applied semi-supervised learning in our research.
    - Our findings demonstrated that the semi-supervised model could achieve comparable performance to models trained on fully labeled datasets, even when the labeled portion was significantly smaller.
  3. Undergrad. Research Intern
    KAIST AIPR Lab
    2019.12 ~ 2020.01
    - I contributed to the OpenXAIProject/stable-baseline-tf2
    - Upgraded the Deep Q-Network codebase from Tensorflow 1.0 to 2.0.
  4. Undergrad. Research Intern
    KAIST Data Mining Lab
    2019.07 ~ 2019.08
    - I studied matrix factorization, a classical recommendation system technique.
    - I focused on dividing the list of items into categories and making recommendations by category.
    - I measured how low performance and computational costs are compared to the recommendations made across the entire dataset.

Education

  1. M.S. in Artificial Intelligence
    Korea Advanced Institute of Science & Technology
    2023.9 ~  On Going
    Advisor: Jinwoo Shin
  2. B.S. in Electrical Engineering
    Korea Advanced Institute of Science & Technology
    2017.02 ~ 2023.08
    Minor in Computer Science and Industrial & Systems Engineering

Projects

  1. iN!T
    A senior developer evaluation-based recruitment solution.
    2021.12 ~ 2022.12
    - Utilized no-code tools to develop a prototype for initial hypothesis validation rapidly and focused on building internal tools to support service operations.
    - Developed three front-end applications using NextJS, catering to juniors, seniors, and companies.
    - Built a backend server with NestJS to automate tasks previously handled manually by the operation team.
  2. MEDIAR
    A computer vision-based lung cancer diagnosis solution.
    2021.02 ~ 2021.06
    - Designed a computer vision solution to assist in the lung cancer diagnosis process, transitioning from traditional methods reliant on doctors' direct microscopic observation to an AI-supported approach.
    - Developed an AI system capable of analyzing high-capacity, high-resolution Whole Slide Images to identify lung cancer regions, classify the type of lung cancer in specimens, and estimate probabilities for accurate diagnosis.