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[Current Projects]
1. ML/AI for Open RAN-based Scientific Applications (Subproject: Programmable network data-plane for federated SDN) 2022-2025 (Funded by KNRF 연구재단, Cowork with KISTI(Korea), ANL(US))
[Member]
Research Interns, PI
2. Development of hands-on human resources for research/development of service technology based on hyper-distributed future networking (BK21 초분산 미래 네트워킹 기반의 서비스 기술 연구/개발 실무 인재 양성) 2020-2027 (Funded by Ministry of Education교육부)
[Member]
Research Interns, PI
3. Future-leading Sejong GRIDA Human Resources Development Project (미래를 선도하는 세종 GRIDA 인력양성사업) 2018-2028 (Funded by Ministry of Trade, Industry and Energy산업통상자원부)
[Member]
PI
4. Efficient convolution operation for rapid neural-network training 2019-Present (Internal, Cowork with DesignedAI)
[Member]
PI
5. Hybrid Quantum-Classical Deep Learning 2020-Present (Internal)
[Member]
Sumin Jin: Quantum Computing for Deep Learning
[Previous Projects]
1. Development of high-speed I/O techniques for efficient weather-forecasting models (기상예측 모델 입출력 효율화를 위한 HW/SW 연계 고속 I/O 기술 개발) 2021-2023 (Funded by Korea Meteorological Institute 기상산업기술원, Cowork with 창원대, 한양대, (주)미래기후)
[Member]
Soohyuck Choe: ML/statistical techniques for adaptive I/O parameter adjustment
Younghyun Lee: Efficient file I/O techniques, including compression
Jinwook Choi: General support
and undergraduate research interns
2. CDM-Cloud: Multi-Cloud data protection and management platform (CDM-Cloud: Multi-Cloud 데이터 보호 및 관리 플랫폼) 2021-2023 (Funded by Institute of Information & Communications Technology Planning & Evaluation (IITP) 정보통신기획평가원, Cowork with 충북대, 데이터커맨드)
[Member]
Jinwook Choi, Jibeom Kim: Local storage management services on k8s
and undergraduate research interns
3. On-site data-driven manufacturing service convergence infrastructure building (현장데이터 기반의 제조서비스 융합 인프라 구축) 2020-2023 (Funded by KITECH생산기술연구원, Cowork with 생기연)
[Member]
Kyungkyu Ko, Jinwook Choi
4. Business Service Developer Training Project Based on Big Data Analysis (빅데이터 분석 기반 비즈니스 개발자 양성 사업) 2020 (Funded by 정보통신기획평가원)
5. Development of Smart City Platform-Based Convergence Technology (스마트시티 플랫폼 기반 융합 기술 개발) 2019-2021 (Funded by 생산기술연구원)
[Member]
Kyungkyu Ko
6. Large-scale IoT streaming data processing using software switches and controllers 2017-2020 (Funded by the National Research Foundation of Korea)
[Member]
Shahzad
Kyungkyu Ko
Hoyong Jin
[Papers]
- S. Shahzad and E. Jung, “FLIP-FLexible IoT Path Programming Framework for Large-scale IoT,” in 2020 20th IEEE/ACM International Symposium on Cluster, Cloud and Internet Computing (CCGRID), May 2020, pp. 881–888, doi: 10.1109/CCGrid49817.2020.00014.
- S. Shahzad, E. Jung, J. Chung, and R. Kettimuthu, “Enhanced Explicit Congestion Notification (EECN) in TCP with P4 Programming,” in 2020 International Conference on Green and Human Information Technology (ICGHIT), Feb. 2020, pp. 35–40, doi: 10.1109/ICGHIT49656.2020.00015.
- H.-Y. Jin, E.-S. Jung, and D. Lee, “High-performance IoT streaming data prediction system using Spark: a case study of air pollution,” Neural Comput & Applic, vol. 32, no. 17, pp. 13147–13154, Sep. 2020, doi: 10.1007/s00521-019-04678-9.
- S. Shahzad and E. Jung, “Flexible IoT Datapath Programming using P4”, ICGHIT 2019.
- 고경규 and 정은성, “IoT 데이터 처리를 위한 HCI 스토리지 시스템 구조 성능 평가,” 한국정보과학회 학술발표논문집, Jun. 2018. https://www.dbpia.co.kr/Journal/articleDetail?nodeId=NODE07503665
[Demo] Hourly Air Pollution(PM10) in Seoul Prediction Graph
Work with industry
- High-performance storage systems (SKT) 2016
Work@Argonne
- Concerted Flows (DOE: Department of Energy) 2013 – 2016
- – Conducted research on infrastructure for Terabit/s Data Transfer.
- – Developed efficient algorithms to optimize multiple disk-to-disk data transfers.
- EPSON (Embracing Parallel Networks and Storage for Predictable End-to-End Data Movement) (NSF: National Science Foundation) 2014 -2016
- – Have developed efficient parallel storage architecture for high-performance data transfer
- – Have developed parallel m-to-n data transfer.
- Technologies and Tools for Synthesis of Source-to-Sink High-Performance Flows (DOE) 2013 – 2016
- – Developing an intelligent data transfer framework based on system profiles such as network, host, and storage profiles.
- RAMSES (Robust Analytical Models for Science at Extreme Scales) (DOE) 2014 – 2018
- – Modeling systems from the application perspective.
- RAINS (Resource Aware Intelligent Network Services) (DOE) 2015 – 2016
- – Developing data-driven models for parallel storage systems.
- – Developing system models and a framework for an unfied distributed system view based on MRML (Multi-Resource Markup Language).
Project@Samsung
- Mobile cloud computing, etc
Project@MacroImpact
- Cluster Volume Manager/File System