Da-eun Ellen Lee
Postdoctoral Associate in the Child Study CenterAbout
Titles
Postdoctoral Associate in the Child Study Center
Biography
Dr. Da-eun (Ellen Lee is a Postdoctoral Associate at Yale School of Medicine's Child Study Center, collaborating with Dr. Christine Cha. She earned her Ph.D. in Applied Artificial Intelligence from Sungkyunkwan University (SKKU) under the supervision of Dr. Jinyoung Han in Seoul, Republic of Korea, in August 2024.
Dr. Lee's primary research focuses on Affective Computing, specializing in Natural Language Processing (NLP) and Multi-Modal Learning to create accessible AI-driven healthcare solutions for real-world challenges. Her work emphasizes interdisciplinary studies, particularly in developing deep learning models for suicide prevention by combining artificial intelligence and psychological insights.
Appointments
Child Study Center
Postdoctoral AssociatePrimary
Other Departments & Organizations
- All Institutions
- Cha Lab
- Child Study Center
Education & Training
- PhD
- Sungkyunkwan University, Applied Artificial Intelligence (2024)
- BA
- Sookmyung Women's University, Social Psychology (2017)
Research
Publications
2024
Detecting Bipolar Disorder from Misdiagnosed Major Depressive Disorder with Mood-Aware Multi-Task Learning
Lee, D., Jeon, H., Son, S., Park, C., hyun An, J., Kim, S., & Han, J. (2024, June). Detecting Bipolar Disorder from Misdiagnosed Major Depressive Disorder with Mood-Aware Multi-Task Learning. In Proceedings of the 2024 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (Volume 1: Long Papers) (pp. 4954-4970).Peer-Reviewed Original ResearchFighting against Fake News on Newly-Emerging Crisis: A Case Study of COVID-19
Yang M, Park C, Kang J, Lee D, Choi D, Han J. Fighting against Fake News on Newly-Emerging Crisis: A Case Study of COVID-19. 2024, 718-721. DOI: 10.1145/3589335.3651506.Peer-Reviewed Original Research
2023
Detecting depression on video logs using audiovisual features
Min K, Yoon J, Kang M, Lee D, Park E, Han J. Detecting depression on video logs using audiovisual features. Humanities And Social Sciences Communications 2023, 10: 788. DOI: 10.1057/s41599-023-02313-6.Peer-Reviewed Original ResearchTowards Suicide Prevention from Bipolar Disorder with Temporal Symptom-Aware Multitask Learning
Lee D, Son S, Jeon H, Kim S, Han J. Towards Suicide Prevention from Bipolar Disorder with Temporal Symptom-Aware Multitask Learning. 2023, 4357-4369. DOI: 10.1145/3580305.3599917.Peer-Reviewed Original ResearchBipolar disorderBD patientsPredicting future suicideIncreased risk of suicideRisk of suicideFuture suicideMulti-task learning modelBD symptomsState-of-the-art modelsCurrent symptomsState-of-the-artSuicideSuicide preventionMultitask learningAttention weightsSymptom identificationAttention mechanismPrediction taskDisordersSymptomsLearning modelsPsychiatristsBipolarSocial media
2021
Authors’ Reply to: Bibliometric Studies and the Discipline of Social Media Mental Health Research. Comment on “Machine Learning for Mental Health in Social Media: Bibliometric Study”
Kim J, Lee D, Park E. Authors’ Reply to: Bibliometric Studies and the Discipline of Social Media Mental Health Research. Comment on “Machine Learning for Mental Health in Social Media: Bibliometric Study”. Journal Of Medical Internet Research 2021, 23: e29549. PMID: 34137721, PMCID: PMC8277311, DOI: 10.2196/29549.Peer-Reviewed Original ResearchMachine Learning for Mental Health in Social Media: Bibliometric Study
Kim J, Lee D, Park E. Machine Learning for Mental Health in Social Media: Bibliometric Study. Journal Of Medical Internet Research 2021, 23: e24870. PMID: 33683209, PMCID: PMC7985801, DOI: 10.2196/24870.Peer-Reviewed Original ResearchConceptsLarge-scale social media dataKeyword co-occurrence networkWeb of Science recordsSocial media dataSocial mediaBibliometric studyMental healthSocial media platformsBibliometric analysisMedia dataData resourcesResearch trendsMedia platformsResearch areaWebWeb of Science databasesCo-occurrence networkPublic distributionContinuous growthFace-to-face meetingsMachineSignificant attentionFace-to-faceMedical providersBibliometrics
News
Get In Touch
Contacts
Email
Locations
The Child Study Center
Lab
350 George Street, Fl 3
New Haven, CT 06511