About Me
Hello, I am Youngeun Nam. I am a Ph.D. student in Data Mining Lab at KAIST. Data mining is a process of discovering patterns in large data sets involving methods at the intersection of machine learning, statistics, and database systems. Recently, I have been studying multi-modal data-centric deep learning in various domains. Furthermore, I am particularly interested in the sports domain, and I have worked as a data analyst at a sports data analysis company, named Fitogeter.
Education
KAIST
Ph.D.
2022.03 - Present
Korea Advanced Institute of Science and Technology | School of Computing
Data Mining Lab advised by Jae-Gil Lee
KAIST
M.S
2020.03 - 2022.02
Korea Advanced Institute of Science and Technology | Industrial and System Engineering
(Graduate School of Data Science)
Data Mining Lab advised by Jae-Gil Lee
POSTECH
B.S
2013.03 - 2017.02
Pohang University of Science and Technology | Industrial and Management Engineering
Experience
Fitogether, Innovating the Sports Culture Using IT Technology.
Based on a wearable EPTS with its own technology, Fitogether measures player/team data during sports (association football) activities, and runs an analytics service ‘OhCoach’ using the data. In particular, I contributed to the launch of the soccer data analysis web service.
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Producing manuals for Fitogether ‘Ohcoach’ service users at the ‘2018 K League U17/U18 Championship’ as a sports data analysis startup intern.
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Creating crawling module with detailed information about K League K1, K2, R League, and ACL competition.
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Creating the ‘Register’ module, the foundation for data analysis using Python, and implementing the algorithm.
Hyundai AutoEver leads a paradigm shift for the future of the 4th industrial revolution.
Working at Hyundai Autoever IT Jobs Finance Team (Hyundai Card, Capital, Commercial).
‘Hyundai Card/Capital/Commercial Blockchain Platform Construction and Service Challenge Implementation’ Project Developer.
Projects
Samsung Mobile Experience
KAIST
Jul. 2022 - Jul. 2023
Few-shot Anomaly Detection and Root Cause Estimation development.
Samsung Mobile Experience
KAIST
Jun. 2021 - Jun. 2022
Real-time service incident prediction development.
Samsung SDS
Seoul National University
Aug. 2018 - Jul. 2018
Storage-based platform model business idea research project.
Platform proposal that provides new value to storage consumers.
Hyundai Card
Hyundai Autoever
Jun. 2017 - Dec. 2017
Hyundai Card/Capital/Commercial Blockchain Platform Construction and Service Task Implementation.
Publications
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Bae, M., Shin, Y., Nam, Y., Lee, Y., and Lee, J., Semi-Supervised Learning for Time Series Collected at a Low Sampling Rate. Proceedings of the 30th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining (KDD), Barcelona, Spain, 2024.
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Nam, Y., Yoon, S., Shin, Y., Bae, M., Song, H., Lee, J., and Lee, B. S., Breaking the Time-Frequency Granularity Discrepancy in Time-Series Anomaly Detection. International World Wide Web Conference (WWW), Singapore, Singapore, 2024. [Paper] [Video] [Poster]
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Nam, Y., Trirat, P., Kim, T., Lee, Y., and Lee, J., Context-Aware Deep Time-Series Decomposition for Anomaly Detection in Businesses. Proceedings of European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECML-PKDD). Torino, Italy, 2023. [Paper] [Slide] [Poster]
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{Trirat, P., Nam, Y.}, Kim, T., and Lee, J., AnoViz: A Visual Inspection Tool of Anomalies in Multivariate Time Series. The Thirty-Seventh AAAI Conference on Artificial Intelligence (AAAI Demonstration Program). Washington, DC, 2023. [Paper] [Video] [Poster] [Live Demo]
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Kim, D., Min, H., Nam, Y., Song, H., Yoon, S., Kim, M., Lee, J. COVID-EENet: Predicting Fine-Grained Impact of COVID-19 on Local Economies. The Thirty-Sixth AAAI Conference on Artificial Intelligence (AAAI). Virtual Event, 2022. [Paper] [Video]
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Nam, Y., Kang, J., Lee, J. ActiveBoostThief: Model Extraction Attack Using Reliable Active Learning. Korea Computer Congress. 2021. [Paper] [Slide]
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Kim, M., Kang, J., Kim, D., Song, H., Min, H., Nam, Y., Park, D. Lee, J. Hi-COVIDNet: Deep Learning Approach to Predict Inbound COVID-19 Patients and Case Study in South Korea. Proceedings of the 26th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining (KDD). Virtual Event, California, 2020. [Paper] [Video]
Awards
- School of Computing, KAIST, Outstanding Teaching Assistant Award, 2023 Spring
- The Thirty-Seventh AAAI Conference on Artificial Intelligence Scholarship, 2023 ($500)
- Department of Industrial & Systems Engineering, KAIST, Scholarship, 2021 (₩1,000,000)
Reviewer Services
- Program Committee
- International World Wide Web Conference (WWW): 2024
- ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD): 2024