Demo Data from SIGIR 2026 / reviewers in the wild / expert
Sooyeon Park
dblp:97/3322
· DBLP profile ↗
5ranked-venue papers
3as first author
3since 2021 · last 2025
—ORCID · conflict
Domains — the database's venue-derived domains; a paper can count in several
Artificial intelligence and machine learning · 3 · 1 first-author · 3 since 2021Systems, architecture and hardware · 1 · 1 first-authorGraphics, computer vision, multimedia, augmented reality and games · 1 · 1 since 2021Human-computer interaction and ubiquitous computing · 1 · 1 first-author
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 · 54% Efficient and distributed learning · 23% Transfer learning and domain adaptation · 18% | |
| Computer graphics and multimedia
1 paper |
Audio and music processing · 100% |
Topics — the 7 heaviest of 9, each with the papers that count most for it
| Topic | Weight | Papers | Last | Evidence papers |
|---|---|---|---|---|
Machine learning › Transfer learning and domain adaptation
cross-modal transfer |
0.9 | 1 | 2025 | Are Any-to-Any Models More Consistent Across Modality Transfers Than Specialists? · ACL (1) 2025 |
Machine learning › Generative modeling
diffusion model |
0.9 | 1 | 2025 | V.I.P.: Iterative Online Preference Distillation for Efficient Video Diffusion Models · ICCV 2025 |
Machine learning › Efficient and distributed learning › distillation
preference distillation |
0.9 | 1 | 2025 | V.I.P.: Iterative Online Preference Distillation for Efficient Video Diffusion Models · ICCV 2025 |
Machine learning › Generative modeling › diffusion model
video diffusion model |
0.9 | 1 | 2025 | V.I.P.: Iterative Online Preference Distillation for Efficient Video Diffusion Models · ICCV 2025 |
Machine learning › Generative modeling
video generation |
0.9 | 1 | 2025 | V.I.P.: Iterative Online Preference Distillation for Efficient Video Diffusion Models · ICCV 2025 |
Audio and music processing › room acoustics
room geometry inference |
0.5 | 1 | 2021 | Iterative Echo Labeling Algorithm With Convex Hull Expansion for Room Geometry Estimation · IEEE ACM Trans. Audio Speech Lang. Process. 2021 |
Machine learning › Efficient and distributed learning
model compression |
0.3 | 1 | 2025 | V.I.P.: Iterative Online Preference Distillation for Efficient Video Diffusion Models · ICCV 2025 |
Methods — techniques the papers use, named apart from their topics
online preference distillation · 0.9iterative distillation · 0.9evaluation study · 0.9time-of-arrival estimation · 0.5convex hull · 0.5
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2025 | Are Any-to-Any Models More Consistent Across Modality Transfers Than Specialists?abstractJiwan Chung, Janghan Yoon, Junhyeong Park, Sangeyl Lee, Joowon Yang, Sooyeon Park, Youngjae Yu. Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers). 2025. Jiwan Chung, Janghan Yoon, Sangeyl Lee, Joowon Yang, Sooyeon Park, Youngjae Yu |
ACL (1) | 6 |
| 2025 | V.I.P.: Iterative Online Preference Distillation for Efficient Video Diffusion Models
Wooseok Seo, Junwan Kim, Seungho Park, Sooyeon Park, Youngjae Yu |
ICCV | 5 |
| 2021 | Iterative Echo Labeling Algorithm With Convex Hull Expansion for Room Geometry EstimationabstractEstimating room geometry is an important problem in audio signal processing, with various applications such as dereverberation and spatial audio. Several methods have been proposed to localize the positions of the walls given the times of arrival (TOAs) from acoustic room impulse responses (RIRs). To localize a reflector, reflections from the same wall should be identified first. When multiple microphones are widely installed inside a room, however, it is hard to identify the reflections generated by the same walls, which leads to the echo labeling problem. In this paper, we propose an iterative echo labeling algorithm to solve the echo labeling problem. We construct a convex hull of ellipses with first-arriving reflections at the initial iteration and expand the convex hull by utilizing later reflections at subsequent iterations. While expanding the convex hull, we sequentially localize the walls by inspecting tangents of the convex hull. Finally, the iteration is terminated when the expanded convex hull of ellipses becomes a superset of a room. As a result, the proposed algorithm can solve the echo labeling problem without prior knowledge of the number of walls and high computational complexity. Sooyeon Park, Jung-Woo Choi |
IEEE ACM Trans. Audio Speech Lang. Process. | 1 |
| 2010 | RESS: A Data Dissemination Protocol Using Residual Energy and Signal Strength for Wireless Sensor Networks
Sooyeon Park, Moonseong Kim, Euihoon Jeong, Young-Cheol Bang |
UIC | 1 |
| 2007 | RWA Algorithm for Scheduled Lightpath Demands in WDM Networks
Sooyeon Park, Jong S. Yang, Moonseong Kim, Young-Cheol Bang |
ISPA | 1 |