Yunpyo An's Webpage

Email: anyunpyo at unist dot ac dot kr

Yunpyo An (안윤표, 安潤杓)

I'm currently pursuing Ph.D degree at Ulsan National Institute of Science and Technology (UNIST). I graduated Cum Laude with a B.Sc. in Computer Science and Engineering from UNIST in 2022. My research focuses on finding the boundaries of machine learning paradigms and exploring methods to transcend these boundaries.

ORCID iD icon https://orcid.org/0000-0002-7402-4297

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Research Statement

My research focuses on finding limitations of machine learning paradigms and exploring methods to transcend these limitations. The current research topic involves the concept of unknown in machine learning. A source of machine learning knowledge is training datasets that sample from real-world data. However, there is a gap between training datasets and real-world such as biased sampling in training data. Because of this difference, there are data points that remain unknown in the training dataset. If we give a task based on unknown information to a human, the human infers it as “unknown”. However, in the current machine learning paradigm, it does not answer unknowns that are not based on the training dataset. In the case of humans, they fill in gaps while performing new learning based on what they do not know. Can this method be applied equally to machine learning?

Current Projects

  • Autonomous Lab Project with Dept. of Chemistry at UNIST. (focus on estimate synthesis results and active learning on this field)

Vita

Education

Publication

  1. Yunpyo An, Suyeong Park, and Kwang in Kim. “Active Learning Guided by Efficient Surrogate Learners”, AAAI 2024.

Skills

I use tools related to machine learning and deep learning.

  • Python (usually with PyTorch library)
  • Matlab
  • Slurm (for scheduling and multi-processing)
  • Wandb (for monitoring and sweeping research project)

Academic Service

  • 2024 CVPR reviewer

Lab Experience

  • 2024.3. - current: IMIL at UNIST.
  • 2021.9. - 2024. 2.: MLV Group at UNIST. Research topic: attribute estimation, adversarial example, active learning, federated learning.

Teaching Activity

  • 2023 Fall: ITP112 Discrete Mathematics (TA, freshmen course)
  • 2023 Fall: ITP117 Introduction to AI Programming Ⅱ (TA, freshmen course)
  • 2023 Spring: AI502 Principles of Deep Learning (TA, graduate course)
  • 2022 Spring: CSE463 Machine Learning (TA, undergraduate course)
  • 2020-21 Winter, 2021-22 Winter: ABC: A Build CS skills winter school (Tutor, sophomore who enter major CSE)

Undergraduate Club Activity

  1. 2018.3. - 2022.3.: UNIST Computer Science and Engineering Club, HeXA, President in 2019.
  2. 2020.9. - 2022.3.: UNIST AI Club, brAIns, Promoter
  3. 2021.3. - 2022.3.: UNIST Problem Solving Club, Almight, Promoter

Last Update: 2025. 03. 05.

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