I am mainly interested in clinical psychology and decision neuroscience. Particularly, I seek to understand the roles of stress in decision making and how they are implicated in certain psychiatric conditions using neurocomputational and machine learning approaches. I aim to become a scientist who builds tools that help people make better decisions in the realm of psychiatric issues. To this end, I am eager to combine computational modeling and neuroscience with clinical psychology.


Memory bias and greater preference for smoking-associated contexts in smokers under acute stress

Like most addictive substances, nicotine dependence and smoking addiction is also known for its high relapse rate. Here, stress is a huge risk factor of relapse and increased craving to smoke. Referring to the phrase of “chasing the first high”, this project aimed to test the role of acute stress bringing up the drug-related memory, especially the rewarding ones and that would contribute to increased craving. By investigating the process of which context memories are retrieved and preferred in episodes associated with cigarettes, we hope the findings to provide a deeper understanding of stress-induced relapse. image-center

  • Manuscript in preparation
  • Poster presented at the Society of Biological Psychiatry (2022, April) link to the abstract

Identifying phenotypes for body-image satisfaction and healthy outcome for obestiy: using machine-learning approach

We recruited over 1,000 participants from 16 branches nationwide with 365mc clinic, which is a liposuction hospital located in South Korea. This project aimed to investigate the significant variables that predict successful weight loss or maintenance. The battery we applied includes computational tasks and models on mobile applications and traditional psychometric measures.


Contribution of Early-Life Stress and Genetic factors to the young brains: a machine-learning approach with ABCD dataset

There has been increasing evidence highlighting the gene x environment interaction in understanding the individual difference that mediates associations between early life stress (ELS) and later development of psychopathology. In this project, we used elastic net to distinguish significant predictors of psychopathology development in different ELS groups. Different data from the ABCD dataset including fMRI task results, clinical diagnosis, and interview answers were included as predictors.

Acute stress alters social discounting rate: a model-based fMRI study


  • Project led with Kunil Kim (LSD lab)
  • This project mainly aimed to investigate:
    • if cortisol is indeed a prosocial hormone.
    • whether the prosocial behaviors would be amplified among the stressed males, even toward the distant others.
  • Poster at the SfN Annual meeting (Upcoming, 2022)

From image to emotion: Multi-label image classification based on the emotions represented in images

Expressing oneself with images is prevalent online these days, perhaps even more than texts Millions of new images are posted on social network services everyday. Enterprises are eager to collect information from such posts and try to follow the preference trend. As a part of 2021 Machine Learning for Visual Understanding course (SNU, Instructor: Joonseok Lee), our team developed a multi-label image classification utilizing triplet loss embeddings on the Pittsburgh advertisement image database.