Jaemin is a student in Industrial and Systems Engineering at Texas A&M University. She has a strong interest in applying data science to real-world problems — especially those at the intersection of biomedical systems and engineering analytics.
Currently, she is part of the Hoyt Lab, where she works on biomedical data analysis, particularly involving high-frequency ultrasound signal data. Her goal is to apply industrial engineering methodologies and machine learning to biomedical datasets to uncover patterns, support diagnosis, and improve healthcare decision-making.
Experience
Graduate Research Assistant
- Texas A&M University (PI: Dr. Kenneth Hoyt) — Apr 2025 – Present
Research Assistant
- Hongik University (PI: Dr. Yong Oh Lee) — Feb 2024 – Aug 2024
Improving Laryngeal Cancer Detection: Comparing Octave-band Filtering & MFCC in Voice Data Machine Learning
Undergraduate Research Assistant
- Hongik University (PI: Dr. Yong Oh Lee) — Dec 2022 – Feb 2024
AI-based Diagnosis of Laryngeal Cancer using Voice Data for Enhanced Diagnosis and Treatment
Education
Texas A&M University
M.S. in Industrial Engineering (current)
Hongik University
B.S. in Industrial and Data Engineering
Publication
J. Song, H. Kim, and Y.O. Lee. “Laryngeal Disease Classification Using Voice Data: Octave-Band vs. Mel-Frequency Filters.”
Heliyon, 2024.
H.B. Kim, J. Song, S.H. Park, and Y.O. Lee. “Classification of Laryngeal Diseases including Laryngeal Cancer, Benign Mucosal
Disease, and Vocal Cord Paralysis by Artificial Intelligence using Voice Analysis.” Scientific Reports, 2023.
J. Song, et al. “Enhancing Vocal-Based Laryngeal Cancer Screening with Additional Patient Information and Voice Signal
Embedding.” Big Data Analytics for Health and Medicine (BDA4HM 2023), Workshop at 2023 IEEE International Conference
on Big Data (IEEE Big Data 2023), 2023.