Jeesung Ahn

About Me

Welcome! My name is Jeesung Ahn, and I am a 5th-year Ph.D. candidate in Psychology at the University of Pennsylvania, working with Dr. Emily Falk at the Communication Neuroscience Lab (expected graduation in May 2024).

My doctoral research focuses on the domain of health psychology and neuroscience. I am passionate about making data-driven predictions on how health interventions can effectively improve physical and mental well-being at the individual- and population-level.

More specifically, my current research mainly revolves around two topics:

  • developing brain-based computational models to evaluate the effectiveness of persuasive health message interventions in promoting health behaviors (e.g., physical activity)
  • explaining individual differences in mental health (e.g., loneliness) based on brain network and social network characteristics

All in all, I am a curious and collaborative scholar who is ready to solve real-world problems with data and science!


Research Methods

Over the course of 9+ years leading end-to-end psychology and neuroscience research projects, I have primarily worked with human behavioral and neuroimaging data.

My research methods are multi-disciplinary and include the followings:

  • experimental and survey design (including A/B tests, usability tests)
  • data collection (recruiting, screening, survey, experimental, behavioral, neuroimaging, experience sampling, in-depth interview)
  • descriptive / parametric / non-parametric / multivariate statistics
  • regression (e.g., general linear modeling)
  • multilevel modeling
  • time-series analysis
  • signal processing (e.g., denoising, despiking, temporal filtering of fMRI signals)
  • computational network analysis
  • machine-learning classification (supervised / unsupervised learning)
  • meta-analysis of multidimensional datasets
  • qualitative content analysis (including text analysis)

Selected Projects

Below is the selected overview of my research projects. If you have any questions, please shoot me an email to jeesung@sas.upenn.edu. Always happy to chat!

Data-Driven Promotion of Healthy Lifestyles

Predicting Physical Activity Behaviors after Health Message Exposure
SANS 2022 Poster (top poster award winner)    SANS 2022 Talk

In this mega-analysis of neuroimaging (fMRI) and physical activity behavioral datasets (N=366), I found the effectiveness of health message interventions in promoting physical activity can be predicted by interactions between message-level (i.e., gain vs. loss framing) and individual-level (i.e., baseline levels of physical activity) characteristics.


Explaining Alcohol Craving-Drinking Links using Brain Network Dynamics
OSF Preregistration   Study Protocol

I ran multi-level models to examine whether the modular organization of functional connectivity networks in the brain (measured by fMRI) can explain real-world associations between alcohol craving and drinking in non-dependent social drinkers (measured by daily experience sampling for one month).


Using Machine Learning to Promote Healthy Eating
Data+Code in GitHub   Wharton Data Science Live


My goal was to create a data-driven algorithm that provides personalized food recommendations based on an individual’s dietary needs. I performed exploratory data analyses and unsupervised machine learning (PCA, K-means clustering using R) on a dataset with 5690 (food) x 154 (nutrients) dimensions. As a result, I was able to cluster 5690 food items into 8 categories and simulate real-world examples where I give food recommendations to individuals who are in need of dietary restrictions.


Connecting Mental Well-Being with Networks in the Brain and Society

Neural Responses to Peers’ Faces Predict Vulnerability to Loneliness during COVID-19
2 upcoming conference presentations in 2023

I tested whether neural responses to peers’ faces predict the likelihood of becoming lonelier from before to after the COVID-19 pandemic. Findings suggested that lonelier individuals may assign greater emotional salience to self-relevant social stimuli. Furthermore, individuals who showed greater reward sensitivity to peers’ faces were more likely to become lonely in response to social stressors such as social isolation during the pandemic.


Self- vs. Peer- Evaluated Social Status, Mental Health and Social Brains


In this large-scale multi-disciplinary study, we adopted Round Robin design where entire members of campus social groups were recruited. Participants rated themselves and their peers’ social status such as likability, attractiveness, and influence over others. Here, I asked three questions: 1) is there a discrepancy between social status that is evaluated by self vs. peers?; 2) would under (or over)-evaluating one’s social status be associated with poorer mental health?; 3) would under (or over)-evaluating one’s social status be associated with thinking more about others’ thoughts and feelings?


Developing a Mobile Application for Emotionally and Socially Distressed Individuals
Brief Summary of Related Projects  SfN 2018 Poster 1  SfN 2018 Poster 2

I designed and conducted A/B tests for a mobile application that provides cognitive training to modify attentional bias towards negative social stimuli (Attentional Bias Modification, ABM). I recruited healthy controls, individuals who have social anxiety, and service workers who were undergoing extreme emotional load at workplaces. Participants performed the ABM task inside the fMRI scanner as well as on their smartphones. We conducted machine-learning classification of brain functional connectivity networks when participants were performing the ABM task and found socially distressed individuals show attentional bias to negative faces (vs. neutral faces) both at the behavioral and neural levels.


Physical Activity and Loneliness: A Systematic Review of Intervention Studies

I qualitatively reviewed literature that examined the effects of interventions that are designed to tackle two important public health matters: physical inactivity and loneliness. My aim was to identify which aspects of intervention most effectively improve physical inactivity and/or loneliness and whether these two variables are bi-directionally related. I proposed a psychological framework that suggests how positive social experience may underlie the relationship between loneliness and physical activity.


Unraveling the Negative Loop of Perseverance Thoughts in Depression and Anxiety

(I was in charge of documenting, wrangling, and engineering behavioral and fMRI datasets.)
Repetitive negative thoughts (perseverance thought, PT) are a prominent feature of many mental disorders and a robust predictor of poor clinical outcomes. Although the importance of PT as a source of impairment and an intervention target is well-recognized, it remains a difficult problem to treat. A major obstacle is our very limited understanding of what happens when people perseverate. In this study, we adopted the network control theory (NCT) analysis to examine whether clinical perseverators exhibit more temporally persistent brain states when they have to “turn off” negative thoughts in order to perform a basic cognitive task.

Other Neuroimaging Projects

Effects of Neurostimulation on Intrinsic Functional Networks and Cognition
Publication   SfN 2017 Poster

I conducted A/B testing of a novel neurostimulation device (transcranial direct current stimulation, tDCS) and examined its effects and safety on enhancing younger adults’ cognitive abilities, with a particular focus on attentional allocation abilities. More specifically, I examined whether an electric intensity lower than 0.5mA can influence the activity of the default mode network (DMN) in the brain. I applied a group independent component analysis (ICA) and found the low-intensity tDCS altered the intrinsic co-activation of brain regions within DMN.

Deep Learning Approach to Enhance fMRI Data Resolution
Yonsei Interdisciplinary Research Award Winner (won $5K in research funding)

In collaboration with electrical engineers, we applied a deep-learning algorithm (convolutional neural network, CNN) to conventional low-resolution fMRI data (i.e., 3.75 × 3.75 × 4 mm voxel size) and successfully converted them into high-resolution (i.e., 1.5 mm isotropic voxel size) images.


Publications

(† deonotes co-first authorship)


Working Papers

  • Ahn, J., Cooper, N., Kang, Y., O’Donnell, M., Green, M., Samanez‐Larkin, G., & Falk, E. B. (2023). Effects of framing on neural responses to persuasive messaging and physical activity. Under Review

  • Ahn, J., Cosme, D., Kang, Y., Zachary, B., Ochsner, K., Mucha, P., Lydon‐Staley, D., Bassett, D. S., & Falk, E. B. (2022). Segregation and integration of brain functional connectivity networks moderate craving‐drinking relationships in daily life. Data analysis completed

  • Ahn, J., Mwilambwe‐Tshilobo, L., Kang, Y., Cosme, D., Bassett, D. S., Zachary, B., Lydon‐Staley, D., Mucha, P., Ochsner, K., & Falk, E. B. (2022). Inaccurate self‐evaluation is associated with mental well‐being and mentalizing activity in the brain. Data analysis completed

  • Ahn, J., Mwilambwe‐Tshilobo, L., Kang, Y., Cosme, D., Bassett, D. S., Zachary, B., Lydon‐Staley, D., Mucha, P., Ochsner, K., & Falk, E. B. (2022). Connectome‐based predictive modeling of loneliness during COVID‐19. Data analysis in progress

  • Ahn, J., Kang, Y., Mwilambwe‐Tshilobo, L., Cosme, D., Bassett, D. S., Zachary, B., Lydon‐Staley, D., Mucha, P., Ochsner, K., & Falk, E. B. (2022). Neural responses to peer faces predict loneliness in college students. Data analysis in progress

  • Ahn, J.†, Zhou, D.†, Falk, E. B., Bassett, D. S., & Ruscio, A. (2022). Brain network underpinnings of perseverance thought in clinical populations. Data analysis in progress

  • Cosme, D., Helion, C., Kang, Y., Lydon‐Staley, D. M., Doré, B. P., Stanoi, O., Ahn, J., Jovanova, M., McGowan, A. L., Kober, H., Mucha, P. J., Bassett, D. S., Ochsner, K. N., & Falk, E. B. (2023). Mindful attention to alcohol can reduce cravings in the moment and consumption in daily life, PsyArXiv

(† deonotes co-first authorship)


Conference Presentations

Invited Talks

Poster Presentations

  • Ahn, J., Kang, Y., Mwilambwe‐Tshilobo, L., Bassett, D., Boyd, Z., Lydon‐Staley, D., Mucha, P., Ochsner, K., & Falk, E. (2023, May). Neural responses to peers’ faces predict vulnerability to loneliness during COVID‐19. Annual International Communication Association Conference 2023, Toronto, Canada.

  • Kang, Y., Ahn, J., Cosme, D., McGowan, A., Mwilambwe‐Tshilobo, L., Zhou, D., Jovanova, M., Stanoi, O., Mucha, P., Ochsner, K., Bassett, D., Lydon‐Staley, D., & Falk, E. (2023, May). Frontoparietal system functional connectivity moderates the within‐day associations between increases in time spent on social media and subsequent negative affect. Annual International Communication Association Conference 2023, Toronto, Canada.

  • Jovanova, M., Boyd, Z., Schwarze, A., Christensen, T., Cosme, D., Katch, K., Ahn, J., Resnick, A., Cooper, N., Xie, H., Kang, Y., Lomax, S., McGowan, A., Mwilambwe‐Tshilobo, L., Stanoi, O., Srivastava, P., Ochsner, K., Bassett, D., Lydon‐Staley, D., … Mucha, P. (2023, May). Integrating multimodal data and machine learning to predict individual differences in health behavior change. Annual International Communication Association Conference 2023, Toronto, Canada.

  • Ahn, J., Cooper, N., Kang, Y., O’Donnell, M., Green, M., Samanez-Larkin, G., & Falk, E. (May, 2022). Brain responses to gain- and loss-framed messages differ, and interact with baseline physical activity, to predict later behaviors, Annual International Communication Association Conference 2022, Paris, France.

  • Kang, Y., O’Cosme, D., Ahn. J., Strecher, V., Lydon-Staley, D., Corbani, F., Jovanova, M., Stanoi, O., Lomax, S., Ochsner, K., Mucha, P., Bassett, D., & Falk., E. (May, 2022). Alcohol cue reactivity in the ventral striatum and daily purpose in life moderate the relationship between alcohol craving and consumption in college students, Annual International Communication Association Conference 2022, Paris, France.

  • Cosme, D., Scholz, C., Chan, HY., Martin, R., Benitez, C., Cooper, N., Paul, A., Ahn, J., Dore, B., Resnick, A., Carreras-Tartak, J., & Falk, E. (May, 2022). Does focusing on self or social relevance during news article exposure increase motivation to share content?, Annual International Communication Association Conference 2022, Paris, France.

  • Chan, HY., Scholz, C., Cosme, D., Martin, R., Benitez, C., Cooper, N., Paul, A., Ahn, J., Dore, B., Resnick, A., Carreras-Tartak, J., & Falk, E. (May, 2022). Brain-based prediction of information virality: Evidence of cross-cultural validity from a pre-registered neuroimaging study, Annual International Communication Association Conference 2022, Paris, France.

  • Ahn, J., Jun, S., Lee, J., Min, S., Lee, S-K., Park, S.H., & Han, S. (Nov, 2018) Altered emotional attention and brain functional connectivity networks of emotional laborers, Society for Neuroscience 2018 Annual Conference, San Diego, USA.

  • Min, S., Jun, S., Ahn, J., Lee, J., Lee, S-K., Park, S.H., & Han, S. (Nov, 2018) Intrinsic functional connectivity in emotion regulation network is altered in emotion laborers, Society for Neuroscience 2018 Annual Conference, San Diego, USA.

  • Lee, J., Lee, H-J., Ahn, J., Lee, S-K., Han, S. (June, 2018) Exploring the high-resolution EPI fMRI protocol to reduce susceptibility-related BOLD signal dropout, The Organization for Human Brain Mapping 2018 Annual Meeting, Singapore.

  • Ahn, J., Han, J.H., Kang, M.S., & Han, S. (Nov, 2017) Frontopolar transcranial direct current stimulation changes intrinsic functional connectivity networks during resting-state fMRI, Society for Neuroscience 2017 Annual Conference, Washington DC, USA.

  • Ahn, J., Nah, Y., & Han, S. (Nov, 2016) Voxel-wise Mapping of the Cingulate Cortex in Impression Formation, Society for Neuroscience 2016 Annual Conference, San Diego, USA.

  • Ahn, J., Nah, Y., & Han, S. (April, 2016) Patterns of Functional Connectivity during Preparation Periods Can Predict the Tendency to Give Up in Following Decision-Making, Cognitive Neuroscience Society 2016 Annual Conference, New York, USA.


Volunteer Activities

I am deeply committed to utilizing my research skills to translate scientific insights into actionable outcomes in complex decision-making scenarios. In pursuit of this goal, I have actively participated in various volunteer activities, where I have been able to work on making real-world impacts on issues that are important to me.

Vice President of Consulting at Penn Graduate Consulting Club (May 2023~):

  • Directed 6 project managers and 30+ analysts, successfully executing 6 consulting projects with 100% client retention
  • Cultivated strong relationships with 5+ clients, increasing the number of secured consulting projects by 200%
  • Revamped marketing strategies for effective talent recruitment, increasing member applications by 300%

Consultant at Penn Biotech Group Healthcare Consulting (Sept 2022~):

  • Presented weekly deliverables to a biotechnology start-up by analyzing the market landscape for a novel cancer therapy that has the potential to significantly impact 1M+ tumor patients
  • Led in-depth interviews with healthcare stakeholders and qualitatively evaluated more than 200 clinical trials and company profiles, which helped the client to make informed decisions regarding partnership opportunities, market sizing, and product pricing
  • Executed agile and meticulous research, complemented by extensive literature reviews, in order to fulfill the client’s needs and adhere to the established timeline

Data Scientist at Penn Mind Sciences Diversity and Equity Initiative (Mar 2022~):

  • Designed and administered online surveys (using Qualtrics) to assess participants’ experience with an outreach program that provides mentorship to underrepresented minority students in their careers in science
  • Wrangled and analyzed pre- vs. post- event data, including qualitatively reviewing participants’ written feedback
  • Visualized event outcomes using ggplot2, wordcloud, and R Markdown and presented actionable insights and recommendations to the program organizers to improve the program

Data Scientist at Penn Data Science Group (Feb 2022 – May 2022):

  • Synthesized air pollution and health data from 6 public sources (100K+ data points) into an interactive heat map, providing clients with a compelling visual aid to support informed policy-making decisions

UX Research Scientist at Team QuantumLabs (April 2015 – Mar 2019):

  • Provided consulting services to a start-up company on the efficacy of their novel neurostimulation technology in enhancing cognitive functions, such as attention capacity
  • Designed and conducted A/B tests and usability studies of their product, which led to the successful acquisition of $100K in funding
  • Led a team that presented findings to cross-functional stakeholders, including venture capital funders, designers, engineers, and clinicians, to inform and advocate the direction of product development

For Fun

My kaggle projects on predicting music and movie popularity.. Coming soon!


Contact

Richards Medical Research Laboratories, 3700 Hamilton Walk, 5F, Philadelphia, PA 19104

jeesung@sas.upenn.edu