About Me

I am a first year Ph.D. student in Computer Science and Engineering at University of California, Santa Cruz. I am a member of Computer Vision Lab advised by professor Roberto Manduchi. I am currently working on Gaze-contingent Screen Magnification project for visually impaired people. My research interests lie broadly in artificial intelligence, machine learning, deep learning, time series analysis, object detection, computer vision, IoT healthcare, assistive technology, and philosophy of science.

I have received my Master’s degree in Computer Science from Kookmin University, Seoul, South Korea. I was a member of Machine Intelligence Lab advised by Jaekoo Lee. My master’s thesis was about Learning from time Series. I also have a Bachelor’s degree in Computer Science from Kookmin University, Seoul, South Korea.

Education

Ph.D. in Computer Science and Engineering

University of California, Santa Cruz (2021 - )

Master's in Computer Science

Kookmin University (2019 - 2021)

Bachelor's in Computer Science

Kookmin University (2013 - 2017)

Publications

  • Learning from Time Series
  • Seongsil Heo, In Kookmin University, Seoul, Korea, August 2021
  • Unsupervised Representation Learning for ECG-based Stress Detection (Best Paper Award)
  • Seongsil Heo, Jaekoo Lee, In IEMEK Symposium on Embedded Technology, May 2021
  • Stress Detection with Single Sensor PPG by Orchestrating Multiple Denoising and Peak Detection Method
  • Seongsil Heo, Sunyoung Kwon, and Jaekoo Lee, In IEEE Access, February 2021
  • PPG signal processing and comparison study with learning-based model for stress detection (Best Paper Award)
  • Seongsil Heo, Inkyung Kim, Sunyoung Kwon, Hyejin Lee and Jaekoo Lee, In Proceedings of Symposium of the Korean Institute of communications and Information Sciences, August 2020
  • A variation on Loss function of Deep Neural Networks for Facial Recognition
  • Seongsil Heo, Daehee Kim, Jaebin Lee and Jaekoo Lee, In Proceedings of Symposium of the Korean Institute of communications and Information Sciences, February 2020
  • Real-time Face De-identification in Visual Media using a Deep Neural Network for Object Detection
  • Seongsil Heo, Daehee Kim, Yonguk Kim and Jaekoo Lee, In Proceedings of Symposium of the Korean Institute of communications and Information Sciences, June 2019