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Seongsil Heo

Research Assistant

Languages

  • Korean (Native)
  • English (Professional)

Interests

  • Short-track speed skating, Piano, Cycling, Swimming, Drawing

About Me

I am a Ph.D. Candidate, advised by professor Roberto Manduchi in Computer Science and Engineering at University of California, Santa Cruz. I’m passionate about leveraging innovative AI technologies for positive societal impact, promoting equitable opportunities, and driving meaningful change towards societal well-being. My main passion lies in the understanding of human body movements, where I have notably contributed to eye gaze movement, diverse time series data analysis, and multi-modal approaches etc.

Education

Ph.D. in Computer Science and Engineering

University of California, Santa Cruz (2021 - )

Master's in Computer Science

Kookmin University (Graduated in 2021)

Bachelor's in Computer Science

Kookmin University (Graduated in 2017)

Experiences

Research Scientist Intern

2024.6 -

Meta, Redmond, WA

Reality Labs - Audio team

Applied Scientist Intern

2022.6 - 2022.9

Amazon, Seattle, WA

Computer Vision and NLP team
Early-fusion multi-modal model for product type classification

Publications

  • Reading with Screen Magnification; Eye Movement Analysis Using Compensated Gaze Tracks
    • Seongsil Heo, Roberto Manduchi, and Suzana Chung, In ACM Symposium on Eye Tracking Research & Applications(ETRA), MULTIPLEYE, April 2024
  • Bimo; Bootstrap Inter-Intra Modality at Once Unsupervised Learning for Multivariate Time Series
    • Seongsil Heo, Jaekoo Lee, In Applied Sciences, April 2024
  • Eye Movement Analysis for Low Vision Readers Using a Full Screen Magnifier
    • Roberto Manduchi, Seongsil Heo, Suzana Chung, February 2024
  • Sage; A Multimodal Knowledge Graph-based Conversational Agent for Complex Task Guidance
    • Kaizhi Z., Jeshwanth B., Bhrigu G., Seongsil Heo, Vignesh, Dhananjay S., Winson C., Shree V., Wang E., 2023
  • 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