Demo Data from EDBT 2026 / reviewers in the wild / expert
Kwanghee Lee
dblp:219/4441
· DBLP profile ↗
6ranked-venue papers
0as first author
3since 2021 · last 2026
0000-0002-5606-0761ORCID · reported
Domains — the database's venue-derived domains; a paper can count in several
Artificial intelligence and machine learning · 3 · 2 since 2021Systems, architecture and hardware · 2Software engineering, systems software and programming languages · 1 · 1 since 2021Graphics, computer vision, multimedia, augmented reality and games · 1 · 1 since 2021
Expertise — from the expertise taxonomy: the topics of the expert's papers under the CCF categories. A weight counts papers with recency: 1 for a paper about the topic, 0.3 when the topic is its context, halved every five years.
| Artificial intelligence
2 papers |
Generative modeling · 50% 3D vision · 32% Deep learning architectures and training · 18% | |
| Computer architecture, parallel and distributed computing, and storage systems
1 paper |
Memory systems · 100% |
Topics — the 8 heaviest of 8, each with the papers that count most for it
| Topic | Weight | Papers | Last | Evidence papers |
|---|---|---|---|---|
Computer vision › 3D vision
3d shape representation |
0.8 | 1 | 2024 | Hybrid Video Diffusion Models with 2D Triplane and 3D Wavelet Representation · ECCV (52) 2024 |
Machine learning › Generative modeling › diffusion model
video diffusion model |
0.8 | 1 | 2024 | Hybrid Video Diffusion Models with 2D Triplane and 3D Wavelet Representation · ECCV (52) 2024 |
Machine learning › Deep learning architectures and training
attention mechanism |
0.4 | 1 | 2020 | U-GAT-IT: Unsupervised Generative Attentional Networks with Adaptive Layer-Instance Normalization for Image-to-Image Translation · ICLR 2020 |
Machine learning › Generative modeling › generative adversarial network
image-to-image translation |
0.4 | 1 | 2020 | U-GAT-IT: Unsupervised Generative Attentional Networks with Adaptive Layer-Instance Normalization for Image-to-Image Translation · ICLR 2020 |
Memory systems › non-volatile memory › persistent memory
byte-addressable persistent memory |
0.4 | 1 | 2020 | NVDIMM-C: A Byte-Addressable Non-Volatile Memory Module for Compatibility with Standard DDR Memory Interfaces · HPCA 2020 |
Memory systems
memory interface |
0.4 | 1 | 2020 | NVDIMM-C: A Byte-Addressable Non-Volatile Memory Module for Compatibility with Standard DDR Memory Interfaces · HPCA 2020 |
Memory systems
non-volatile memory |
0.4 | 1 | 2020 | NVDIMM-C: A Byte-Addressable Non-Volatile Memory Module for Compatibility with Standard DDR Memory Interfaces · HPCA 2020 |
Memory systems › non-volatile memory › non-volatile main memory
NVDIMM |
0.4 | 1 | 2020 | NVDIMM-C: A Byte-Addressable Non-Volatile Memory Module for Compatibility with Standard DDR Memory Interfaces · HPCA 2020 |
Methods — techniques the papers use, named apart from their topics
3d wavelet representation · 0.82d triplane · 0.8system-wide mechanisms · 0.4generative adversarial network · 0.4asynchronous timing support · 0.4adaptive layer-instance normalization · 0.4
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2026 | FDPEmu: How to Separate Workloads for Better WAF on FDP SSDsabstractAs data-centric applications proliferate, mitigating Write Amplification Factor (WAF) in SSDs has become critical for sustaining performance and longevity. Flexible Data Placement (FDP), a recently ratified NVMe standard, addresses this by allowing hosts to guide data placement while retaining block interface compatibility. However, research on FDP is constrained by the scarcity of prototype device and the lack of emulation tools supporting its multi-stream architecture. In this paper, we propose FDPEmu, a high-fidelity FDP emulator extended from FEMU. FDPEmu addresses the architectural limitations of legacy emulators by implementing core FDP data structures, per-RUH write pointers, and isolation-aware Garbage Collection (GC). We also introduce Striding, a dynamic channel offset allocation technique, to mitigate channel contention in multi-stream environments. Validation against prototype FDP SSDs demonstrates high fidelity ($r \gt 0.89$ for skewed workloads) in capturing WAF trends, confirming the emulator as a credible research platform. Furthermore, our case studies on RocksDB and F2FS reveal that strictly separating data is not universally beneficial. We demonstrate that the effectiveness of isolation policies relies heavily on workload patterns, indicating that optimal FDP strategies must carefully balance data lifetime separation with effective resource utilization. Nakyeong Kim, Kwanghee Lee, Bryan S. Kim, See-hwan Yoo, Jaedong Lee, Jongmoo Choi |
ISPASS | 2 |
| 2025 | DiffFace: Diffusion-based face swapping with facial guidance
Seokju Cho, Junyoung Seo, Jisu Nam, Kyuchul Lee, Seungryong Kim, Kwanghee Lee |
Pattern Recognit. | 8 |
| 2024 | Hybrid Video Diffusion Models with 2D Triplane and 3D Wavelet Representation
Haneol Lee, Kwanghee Lee, Seungryong Kim, Jaejun Yoo 0001 |
ECCV (52) | 5 |
| 2020 | A New LSM-style Garbage Collection Scheme for ZNS SSDs
Gunhee Choi, Kwanghee Lee, Myunghoon Oh, Jongmoo Choi, Jhuyeong Jhin, Yongseok Oh |
HotStorage | 2 |
| 2020 | NVDIMM-C: A Byte-Addressable Non-Volatile Memory Module for Compatibility with Standard DDR Memory InterfacesabstractCurrently, there are two representative non-volatile dual in-line memory module (NVDIMM) interfaces: a proprietary Intel DDR-T and the JEDEC NVDIMM-P, which are not supported by existing platforms. Adoption of new platform is costly and measuring its efficiency of migrating to the new platform is much more complex. This study is an alternative way of them—finding a new memory device that can be supported by all existing systems. In this paper, we propose an NVDIMM architecture with several system-wide mechanisms to allow the synchronous DDR4 memory interfaces to support non-deterministic (asynchronous) timing. The proposed memory architecture is implemented as a real device prototype, and also evaluated using synthetic and real workloads on an x86-64 server system. Changmin Lee 0004, Wonjae Shin, Dae Jeong Kim, Yongjun Yu, Sung-Joon Kim, Taekyeong Ko, Deokho Seo, Kwanghee Lee, Seongho Choi 0002, Namhyung Kim, Vishak G, Arun George, Vishwas V, Donghun Lee 0001, Kang-Woo Choi, Changbin Song, Dohan Kim 0003, Insu Choi, Ilgyu Jung, Yong Ho Song, Jinman Han |
HPCA | 9 |
| 2020 | U-GAT-IT: Unsupervised Generative Attentional Networks with Adaptive Layer-Instance Normalization for Image-to-Image Translation
Hyeonwoo Kang, Kwanghee Lee |
ICLR | 4 |