연구처

학력

- 2011: Ph.D. in EE, Stanford University
- 2005: M.S. in EECS, Seoul National University
- 2003: B.S. in MSE and EE, Seoul National University

주요 경력

- 2022~Present: Professor, Department of Computer Science and Engineering, UNIST
- 2017~2022: Associate Professor, Department of Computer Science and Engineering, UNIST
- 2013~2017: Assistant Professor, Department of Computer Science and Engineering, UNIST, Ulsan, Republic of Korea
- 2012~2013: Senior Researcher, Electronics and Telecommunications Research Institute (ETRI), Daejeon, Republic of Korea
- 2011~2012: Platform Software Engineer, Microsoft Corporation, Redmond, WA, US

수상/학회/외부활동

- Most Influential PLDI Paper Award, 2020
- Program committee member for premier computer systems conferences including FAST'21, PACT'20, CCGrid'20, and DATE'20
- Best Paper Finalist at CCGrid, 2018
- Stanford Graduate Fellowship
- Samsung Scholarship (formerly known as the Samsung Lee Kun Hee Scholarship Foundation (SLSF))

연구분야

System software for machine learning, ML-augmented system software, scalable parallel and distributed computing, computer systems security

System software for machine learning, ML-augmented system software, scalable parallel and distributed computing, computer systems security

연구주제

1. System Software for High-Performance and Efficient Machine Learning
- Characterizing and optimizing the machine-learning frameworks using high-performance accelerators
- Resource management for large-scale distributed systems for high-performance machine learning

2. Machine Learning-Augmented System Software
- Improving the efficiency of parallel and distributed task schedulers and resource managers using machine learning
- Machine learning-augmented dynamic data placement and migration techniques

3. Scalable and Efficient Parallel and Distributed Computing
- OS and runtime scheduling techniques for parallel and heterogeneous systems
- Performance/power analysis and optimization of real-world and emerging applications

4. Computer Systems Security
- Design and implementation of secure system software for safe and efficient computing
- Developing security attacks by exploiting the vulnerabilities of the system software and computer architecture

1. System Software for High-Performance and Efficient Machine Learning
- Characterizing and optimizing the machine-learning frameworks using high-performance accelerators
- Resource management for large-scale distributed systems for high-performance machine learning

2. Machine Learning-Augmented System Software
- Improving the efficiency of parallel and distributed task schedulers and resource managers using machine learning
- Machine learning-augmented dynamic data placement and migration techniques

국가연구개발사업 기술 분류체계

국가과학기술표준분류

EE. 정보/통신 > EE02. 소프트웨어 > EE0203. System Integration

논문

- Myeonggyun Han, Jihoon Hyun, Seongbeom Park, and Woongki Baek, “Hotness- and Lifetime-Aware Data Placement and Migration for High-Performance Deep Learning on Heterogeneous Memory Systems,” in the IEEE Transactions on Computers (TC), 2020.

- Myeonggyun Han, Jihoon Hyun, Seongbeom Park, Jinsu Park, and Woongki Baek, “MOSAIC: Heterogeneity-, Communication-, and Constraint-Aware Model Slicing and Execution for Accurate and Efficient Inference,” in the Proceedings of the 28th International Conference on Parallel Architectures and Compilation Techniques (PACT), Sep. 2019.

- Jinsu Park, Seongbeom Park, and Woongki Baek, “CoPart: Coordinated Partitioning of Last-Level Cache and Memory Bandwidth for Fairness-Aware Workload Consolidation on Commodity Servers,” in the Proceedings of the 14th European Conference on Computer Systems (EuroSys), Mar. 2019.

특허

- Lightweight architecture for aliased memory operations, Woongki Baek and Seung Hoe Kim, US Patent 10,223,261, 2019

- Reliability-aware application scheduling, Woongki Baek et al., US Patent 9,436,517, 2016