I am a first-year Ph.D. student at ALIN-LAB, advised by Prof. Jinwoo Shin at Korea Advanced Institute of Science and Technology (KAIST).

My research interests focus on trustworthy machine learning for real-world deployment. I mainly focus on the interpretability & transparency of deep neural networks. However, I also have a broad interest in the robust machine learning (e.g., distributional shifts, adversarial examples and label shifts).

Contact: jihoontack at kaist.ac.kr

Publications

(C: Conference, W: Workshop, P: Preprint, *: Equal contribution)

  • [C2] CSI: Novelty Detection via Contrastive Learning on Distributionally Shifted Instances [paper], [code], [slide]
    Jihoon Tack*, Sangwoo Mo*, Jongheon Jeong, and Jinwoo Shin
    Neural Information Processing Systems (NeurIPS) 2020

  • [C1] Adversarial Self-Supervised Contrastive Learning [paper], [code], [site]
    Minseon Kim, Jihoon Tack, and Sung Ju Hwang
    Neural Information Processing Systems (NeurIPS) 2020

Education

  • M.S. & Ph.D. integrated course in Artificial Intelligence, Korea Advanced Institute of Science and Technology (KAIST), 2020 - present (advisor: Prof. Jinwoo Shin)
  • M.S. in Electrical Engineering, Korea Advanced Institute of Science and Technology (KAIST), 2019 - 2020 (advisor: Prof. Jinwoo Shin)
  • B.S. in Electrical Engineering and Mathematics (minored), Korea Advanced Institute of Science and Technology (KAIST), 2014 - 2019

Teaching

  • TA, AI504: Programming for AI, KAIST Fall 2020
  • TA, AI-Expert Program, Samsung-DS Summer 2020
  • TA, AI703: Systems and Applications of AI and ML, KAIST Spring 2020
  • TA, EE209: Programming Structure for Electrical Engineering, KAIST Fall 2019

Academic Services

  • Conference reviewer: NeurIPS 2020

Honor & Award

  • Top reviewer award (top 10%), NeurIPS 2020

Project

  • Used car project, KB Capital, 2017 - 2020
    Building an AI system that predicts price, depreciation, and sales duration of the used car.
    Currently, my system is deployed at KBchachacha service (only Korean available).
  • Interpretable ML for object detection, 2020 - present