Jongyeon Yoon

My name is Jongyeon Yoon, a Ph.D. student in Computer Science at Vanderbilt University and a member of Vanderbilt Institute for Surgery and Engineering(VISE).

Currently, I am doing my research at VINE Lab under the guidance of Professor Daniel Moyer. Additionally, I am an affiliated student at MASI Lab advised by Professor Bennett Landman. My research focus on the application of Deep Learning to medical imaging, with a particular emphasis on MRI, specifically dMRI.

Before pursuing Ph.D., I completed my undergraduate programs in Statistics and Software Convergence (double-major) at Dongguk University. During this time, I actively involved in DAMI Lab, working advise by Professor Woojin Lee. 　
　

Email: {firstname}.{lastname}@vanderbilt.edu

Office: Featheringill Jacobs Hall 360

news

Jan 03, 2024	Starting my graduate study as Ph.D. student at Vanderbilt University!
Oct 02, 2023	My first paper got accepted to IEEE Access!　Check it out!
Aug 24, 2023	Completed my Bachelor’s degree at Dongguk University, Seoul! (B.S. in Statistics & B.S. in Software Convergence)

selected publications

IEEE Access
Fairness-Aware Multimodal Learning in Automatic Video Interview Assessment

Changwoo Kim , Jinho Choi , Jongyeon Yoon , and 2 more authors

IEEE Access, 2023

Abs Bib HTML PDF

With the ever-growing reliance on Artificial Intelligence (AI) across diverse domains, there is an increasing concern surrounding the possibility of biases and unfairness inherent in AI systems. Fairness problems in automatic interview assessment systems, especially video-based automated interview assessments, have less been addressed despite their prevalence in the recruiting field. In this paper, we propose a method that resolves fairness problems in an automated interview assessment system that uses multimodal data as input. This is mainly done by minimizing the Wasserstein distance between two sensitive groups by introducing a regularization term. Subsequently, we employ a hyperparameter that can control the trade-off between fairness and accuracy. To test our method in various data settings, we suggest a preprocessing method that can manually adjust the underlying degree of unfairness in the training data. Experimental results show that our method presents state-of-the-art results in terms of fairness compared to previous methods.
@article{10287972, url = {https://ieeexplore.ieee.org/abstract/document/10287972}, author = {Kim, Changwoo and Choi, Jinho and Yoon, Jongyeon and Yoo, Daehun and Lee, Woojin}, journal = {IEEE Access}, title = {Fairness-Aware Multimodal Learning in Automatic Video Interview Assessment}, year = {2023}, volume = {11}, number = {}, pages = {122677-122693}, keywords = {Interviews;Recruitment;Predictive models;Training data;Machine learning algorithms;Data models;Prediction algorithms;Automatic interview assessment;multimodal;fairness;sensitive attribute;unfair assumption;Wasserstein distance;adversarial;representation learning}, doi = {10.1109/ACCESS.2023.3325891} }