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  • Human Language Intelligence Lab - Prof. JINYEONG BAK

    Major research interests

    Evaluating of Generated Text

    Detecting and Mitigating Toxic Contents

    Translating low-resource languages

    Simulating Human Behaviors in Texts


    Major research achievements (2021~present)

    DongJin Jeong and JinYeong Bak, Conversational Emotion-Cause Pair Extraction with Guided Mixture of Experts, EACL 2023

    HyunJin Kim, JinYeong Bak, Kyunghyun Cho, and Hyungjoon Koo, A Transformer-based Function Symbol Name Inference Model from an Assembly Language for Binary Reversing, ASIACCS 2023

    Juhee Son, Jiho Jin, Haneul Yoo, JinYeong Bak, Kyunghyun Cho, and Alice Oh, Translating Hanja Historical Documents to Contemporary Korean and English, EMNLP 2022 Findings

    Haneul Yoo, Jiho Jin, Juhee Son, JinYeong Bak, Kyunghyun Cho, and Alice Oh, HUE: Pretrained Model and Dataset for Understanding Hanja Documents of Ancient Korea, NAACL 2022 Findings

    JinUk Cho, MinSu Jeong, JinYeong Bak, and Yun-Gyung Cheong, Genre-Controllable Story Generation via Supervised Contrastive Learning, WWW 2022

    Yohan Jo, Haneul Yoo, JinYeong Bak, Alice Oh, Chris Reed, and Eduard Hovy, Knowledge-Enhanced Evidence Retrieval for Counterargument Generation, EMNLP 2021 Findings

    YunSeok Choi, JinYeong Bak, CheolWon Na, and Jee-Hyong Lee,

    Learning Sequential and Structural Information for Source Code Summarization, ACL 2021 Findings

    JinYeong Bak and Alice Oh. A Leader’s Final Decision Classification Model Tested on Meeting Records with BERT. In Journal of KIISE, volume 48, 2021.

    Minsoo Park, Dai Quoc Tran, JinYeong Bak, and Seunghee Park. Advanced wildfire detection using generative adversarial network-based augmented datasets and weakly supervised object localization. In International Journal of Applied Earth Observation and Geoinformation, volume 114, page 103052, 2022.


  • Data eXperience Lab - Prof. EUNIL PARK

    Research interests

    Data Science, Computational Social Science, Industrial AI, Social Computing

    User Innovation, Human-Computer Interaction, User eXperience


    Major research achievements (2022~present)

    “OSANet: Object Semantic Attention Network for Visual Sentiment Analysis”

    IEEE Transactions on Multimedia (in press)

    “DeepAUP: a deep neural network framework for abnormal underground heat transport pipelines” IEEE Transactions on Automation Science and Engineering (in press)

    “Detecting agro: Korean Trolling and Click-baiting Behavior in Online Environments”

    Journal of Information Science (in press)

    “CRNet: A multimodal deep convolutional neural network for customer revisit prediction”

    Journal of Big Data (2023)

    “For sustainable development in the transportation sector: Determinants of acceptance of sustainable transportation using the innovation diffusion theory and technology acceptance model” Sustainable Development (2022)

    “Movie Recommendation Systems using Actor-based Matrix Computations in South Korea” IEEE Transactions on Computational Social Systems (2022)

    “Satisfied or Not: User Experience of Mobile Augmented Reality in Using Natural Language Processing Techniques on Review Comments” Virtual Reality (2022)

    “DemoHash: Hashtag Recommendation based on User Demographic Information”

    Expert Systems With Applications (2022)

    “Understanding social resistance to determine the future of Internet of Things (IoT) services” Behaviour & Information Technology (2022)

    “Deep learning model based on expectation-confirmation theory to predict customer satisfaction in hospitality services” Information Technology & Tourism (2022)

    “MultiEmo: multi-task framework for emoji prediction” Knowledge-Based Systems (2022)

    “Social networking services as new venue for public perceptions of energy issues: the case of Paris agreement” Energy Strategy Reviews (2022)


  • Data-Intensive Computing Lab - Prof. BEOMSEOK NAM

    Major research interests

    Computer System Software

    Data Intensive Computing (Indexing and IO Stack)

    Database Systems

    Distributed and Parallel Computing


    Major research achievements (2021~present)

    Mijin An, Jonghyeok Park, Tianzheng Wang, Beomseok Nam, and Sang-Won Lee, NV-SQL: Boosting OLTP Performance with Non-Volatile DIMMs, To appear at 49th International Conference on Very Large Databases (VLDB 2023), Sep. 2023.

    Hobin Woo, Daegyu Han, Seungjoon Ha, Sam H. Noh, Beomseok Nam, On Stacking a Persistent Memory File System on Legacy File Systems, 21st USENIX Conference on File and Storage Technologies (FAST 2023), Feb. 2023.

    Lam-Duy Nguyen, Sang-Won Lee, Beomseok Nam, In-Page Shadowing and Two-Version Timestamp Ordering for Mobile DBMSs,, 48th International Conference on Very Large Databases (VLDB 2022), Sep. 2022

    Wonbae Kim, Chanyeol Park, Dongui Kim, Hyeongjun Park, Young-ri Choi, Alan Sussman, Beomseok Nam, ListDB: Union of Write-Ahead Logs and Persistent SkipLists for Incremental Checkpointing on Persistent Memory, 16th USENIX Symposium on Operating Systems Design and Implementation (OSDI 2022), July. 2022.

    Sunghwan Ahn, Hyeongjun Park, V. A. Boleaz Sanchez, Deukyeon Hwang, Wonbae Kim, Alan Sussman, Beomseok Nam, VeloxDFS: Streaming Access to Distributed Datasets to Reduce Disk Seeks, 22nd IEEE/ACM International Symposium on Cluster, Cloud and Internet Computing (CCGrid 2022), May. 2022.

    Jonghyeon Yoo, Hokeun Cha, Wonbae Kim, Wook-Hee Kim, Sung-Soon Park, Beomseok Nam, Pivotal B+tree for Byte-Addressable Persistent Memory, IEEE Access, vol 10, pages 46725--46737, Apr, 2022

    Soojeong Cho, Wonbae Kim, Sehyeon Oh, Changdae Kim, Kwangwon Koh, Beomseok Nam, Failure-Atomic Byte-Addressable R-tree for Persistent Memory, IEEE Transactions on Parallel and Distributed Systems, (TPDS). vol. 32, no. 3, 601--614, Mar. 2021


  • Security Engineering Lab - Prof. HYOUNGSHICK KIM

    Major research interests

    Security engineering

    Usable security


    Major research achievements (2021~present)

    “SmartMark: Software Watermarking Scheme for Smart Contracts”, Taeyoung Kim, Yunhee Jang, Chanjong Lee, Hyungjoon Koo, and Hyoungshick Kim, ICSE: The 45th International Conference on Software Engineering, Melbourne, Australia, 2023.

    “AppSniffer: Towards Robust Mobile App Fingerprinting Against VPN”, Sanghak Oh, Minwook Lee, Hyunwoo Lee, Elisa Bertino, and Hyoungshick Kim, WWW: The 32nd Web Conference, Austin, USA, 2023.

    “Mutexion: Mutually Exclusive Compression System for Mitigating Compression Side-Channel Attacks”, Taegeun Moon, Hyoungshick Kim, and Sangwon Hyun, ACM Transactions on the Web (TWEB), 2022.

    “Evaluation and Optimization of Distributed Machine Learning Techniques for Internet of Things”, Yansong Gao, Minki Kim, Chandra Thapa, Alsharif Abuadbba, Zhi Zhang, Seyit Camtepe, Hyoungshick Kim, and Surya Nepal, IEEE Transactions on Computers (TC), 2022.

    “Design and Evaluation of a Multi-Domain Trojan Detection Method on Deep Neural Networks”, Yansong Gao, Yeonjae Kim, Bao Gia Doan, Zhi Zhang, Gongxuan Zhang, Surya Nepal, Damith C. Ranasinghe, and Hyoungshick Kim, IEEE Transactions on Dependable and Secure Computing (TDSC), 2022.

    “ARGH!: Automated Rumor Generation Hub”, Larry Huynh, Manh Duy Thai Nguyen, Joshua Goh, Hyoungshick Kim, and Jin Hong, CIKM: The 30th ACM International Conference on Information and Knowledge Management (selected as “best paper nominations”), Virtual, 2021.

    “Rocky: Replicating Block Devices for Tamper and Failure Resistant Edge-based Virtualized Desktop Infrastructure”, Beom Heyn Kim and Hyoungshick Kim, ACSAC: The 37th Annual Computer Security Applications Conference, Virtual, 2021.

    “Fine with “1234”? An Analysis of SMS One-Time Password Randomness in Android Apps”, Siqi Ma, Juanru Li, Hyoungshick Kim, Elisa Bertino, Surya Nepal, Diet Ostry, and Cong Sun, ICSE: The 43rd International Conference on Software Engineering, Madrid, Spain, 2021.


  • Machine Intelligent Lab - Prof. JAEKWANG KIM

    Major research interests

    Recommender Systems

    - Optimization of recommendation

    - Graph-based recommendation

    - Session-based recommendation

    - Contents (cartoon, music, book) recommendation

    Machine Intelligence for Various Domains

    - Medical data based prediction/detection

    - Time series data prediction (ECG signal, Water level, ...)

    - Graph Neural Network and Network analysis

    - Self-training on various domains

       Computer Vision and NLP

    - Image classification, Object detection, Segmentation,

    - Image super resolution

    - point cloud for 3D image

    - Text mining, NER problems, Intelligent chatbot


    Major research achievements (2021~present)

    "Time-Weighted Cumulative LSTM Method Using Log Data for Predicting Credit Card Customer Turnover," IEEE Access (early access), Feb. 2023.

    "Merchant Recommender System Using Credit Card Payment Data," Electronics 12 (4), MDPI, Feb. 2023.

    "Mitigating Dataset Bias via Image Translation,” Joint 12th International Conference on Soft Computing and Intelligent Systems and 23rd International Symposium on Advanced Intelligent Systems (ISIS), Nov. 2022.

    "Hybrid Oversampling Technique Based on Star Topology and Rejection Methodology for Classifying Imbalanced Data,” ICDM 2022 Workshop Machine Learning on Higher-Order Structured data (ICDMW), Nov. 2022.

    "Edge Profile Super Resolution,” IEEE Access, Aug. 2021.

    "Differentiable Ranking Metric Using Relaxed Sorting For Top-K Recommendation,” IEEE Access, Aug. 2021.


  • Data Science and Social Analytics Lab - Prof. JANG HYUN KIM

    Major research interests

    Social/Semantic Network Data Analysis

    User Satisfaction Analysis with Big Data

    Human-AI Interaction

    Data Science


    Major research achievements (2021~present)

    I Know My Teammates: The Role of Group Member Familiarity in Computer-Supported and Face-to-Face Collaborative Learning. Education and Information Technologies (SSCI, JCR 2021 IF= 3.666, Q1 in Educational Research) (accepted)

    My video game console is so cool! A coolness theory-based model for intention to use video game consoles. Technological Forecasting and Social Change, 176, 121451. (SSCI, JCR 2021 IF=10.884, Top #1 in Regional & Urban Planning)

    Should a small-sized store have both online and offline channels? An efficiency analysis of the O2O platform strategy. Journal of Retailing and Consumer Services, 64, 102823. (SSCI, JCR 2021, IF=10.972, Q1 in Business)

    Predicting User Satisfaction of Mobile Healthcare Services Using Machine Learning: Confronting the COVID-19 Pandemic. Journal of Organizational and End User Computing (JOEUC), 34(6), 1-17. (SSCI, JCR 2021 IF= 7.400, Q1 in Computer Science & Information Systems) (2022)

    Will coolness factors predict user satisfaction and loyalty? Evidence from an artificial neural network–structural equation model approach. Information Processing and Management, 59(6), 103108. (SSCI, JCR 2021 IF=7.466, Q1 in Information Science & Library Science)

    Are global over-the-top platforms the destroyers of ecosystems or the catalysts of innovation?. Telematics and Informatics, 60, 101581. (SSCI, JCR 2021 IF= 9.140, Q1 in Information Science & Library Science)

    A value of civic voices for smart city: A big data analysis of civic queries posed by Seoul citizens. Cities, 108, 102941. (SSCI, JCR 2021 IF= 6.077, Top #3 in Urban Studies)


  • Computer Systems & Intelligence (CSI) Lab - Prof. YUSUNG KIM

    Major research interests

    Intelligent Systems

    Reinforcement Learning

    Representation Learning

    Self-Supervised Learning


    Major research achievements (2021~present)

    “DS2MA: A Deep Learning-Based Spectrum Sensing Scheme for a Multi-Antenna Receive”, IEEE Wireless Communications Letters, February, 2023.

    “Dream to Generalize: Zero-Shot Model-based Reinforcement Learning for Unseen Visual Distractions”, 37th AAAI Conference on Artificial Intelligence, February 2023.

    Spectrum Challenge 2022 Grand Prize Award, November 2022.

    “Self-Predictive Dynamics for Generalization of Vision-based Reinforcement Learning”, International Joint Conference on Artificial Intelligence (IJCAI), July 2022.

    "Rethinking Autocorrelation for Deep Spectrum Sensing in Cognitive Radio Networks", IEEE Internet of Things Journal, 2022.

    "A Signal-to-Data Translation Model for Robust Backscatter Communications", IEEE Access, March 2022.

    "Efficient User-Level Multi-Path Utilization in RDMA Networks", IEEE Access, September 2021.

    Spectrum Challenge 2021 Grand Prize Award, November 2021.


  • Networking System Lab - Prof. YOUNGHOON KIM

    Major research interests


    GPU-Enabled Networking and its Applications

    Enhanced container resource management techniques (interference, networking)

    Research on GPU Resource Management Techniques for Improving Machine Learning


    Major research achievements (2021~present)


    “GPU-ether: GPU-native packet I/O for GPU applications on commodity ethernet”, IEEE International Conference on Computer Communications (INFOCOM), May, 2021

    “Virtualizing GPU direct packet I/O on commodity Ethernet to accelerate GPU-NFV”, Journal of Network and Computer Applications (JNCA), Oct. 2022

    “UD-assisted Multi-path Transport in RDMA”, International Conference on ICT Convergence (ICTC), Oct. 2022

    “Comparative Analysis of GPU Stream Processing between Persistent and Non-persistent Kernels” International Conference on ICT Convergence (ICTC), Oct. 2022

    “i-NVMe: Isolated NVMe over TCP for a Containerized Environment”, IEEE International Conference on Computer Communications (INFOCOM), May, 2023 (To be appeared)

    “Underlying-crosstalk-aware consolidated resource management framework for GPU clusters”, Young Researcher Program, NRF, 2021.3-2023.2


  • AI convergence Lab - Prof. KWANGSU KIM

    Major research interests


    Computer Vison

    Domain Adaptation

    Federated Learning

    AI Applications

    Explainable AI

    Privacy Protection


    Major research achievements (2021~present)


    A Convolutional Transformer Model for Multivariate Time Series Prediction, IEEE ACCESS 10-0 101319-, 2022

    Empirical Measurement of Client Contribution for Federated Learning With Data Size Diversification, IEEE ACCESS, 10-0 118563- 2022

    Deep Non-Line-of-Sight Imaging Using Echolocation, MDPI  SENSORS, 22-21, 2022

    Generalized Facial Manipulation Detection with Edge Region Feature Extraction, IEEE/CVF Winter Conference on Applications of Computer, 1-1 2784-, 2022

    Image Perturbation-Based Deep Learning for Face Recognition Utilizing Discrete Cosine Transformation, MDPI ELECTRONICS, 11-1 25-33, 2021

    A Daily Tourism Demand Prediction Framework Based on Multi-head Attention CNN, IEEE Symposium Series on Computational Intelligence, 1-1, 1-10, 2021

    Canine Behavior Interpretation Framework Using Deep Graph Model, ICAISC 2021: Artificial Intelligence and Soft Computing, 1-1, 99-110, 2021



  • SecAI Lab - Prof. HYUNGJOON KOO

    Major research interests

    Security with Artificial Intelligence

    Software Security

    System Security

    Security for Emerging Technologies


    Major research achievements (2018~present)

    SmartMark: Software Watermarking Scheme for Smart Contracts, Taeyoung Kim, Yunhee Jang, Chanjong Lee, Hyungjoon Koo and Hyoungshick Kim, In the 45th IEEE/ACM International Conference on Software Engineering, 2023 (ICSE ’23; To appear)

    A Transformer-based Function Symbol Name Inference Model from an Assembly Language for Binary Reversing, Hyunjin Kim, Jinyeong Bak, Kyunghyun Cho, and Hyungjoon Koo, In the 18th ACM Asia Conference on Computer and Communications Security, 2023 (ASIACCS ’23; To appear)

    Practical Binary Code Similarity Detection with BERT-based Transferable Similarity Learning, Sunwoo Ahn, Seonggwan Ahn, Hyungjoon Koo, and Yunheung Paek, In the 38th Annual Computer Security Applications Conference (ACSAC ’22)

    DeView: Confining Progressive Web Applications by Debloating Web APIs, ChangSeok Oh, Sangho Lee, Chenxiong Qian, Hyungjoon Koo, and Wenke Lee, In the 38th Annual Computer Security Applications Conference (ACSAC ’22)

    Software Watermarking via a Binary Function Relocation, Honggoo Kang, Yonghwi Kwon, Sangjin Lee, and Hyungjoon Koo, In the 37th Annual Computer Security Applications Conference (ACSAC ’21)

    A Look Back on a Function Identification Problem, Hyungjoon Koo, Soyeon Park, and Taesoo Kim, In the 37th Annual Computer Security Applications Conference (ACSAC ’21)

    Slimium: Debloating the Chromium Browser with Feature Subsetting, Chenxiong Qian, Hyungjoon Koo, Changseok Oh, Taesoo Kim, and Wenke Lee, In the 27th ACM Conference on Computer and Communications Security (CCS ’20)

    Compiler-assisted Code Randomization, Hyungjoon Koo, Yaohui Chen, Long Lu, Vasileios P. Kemerlis, and Michalis Polychronakis, In the 39th IEEE Symposium on Security & Privacy, 2018 (S&P ’18)