Yapeng Jiang

Demonstration venue · read-only. Every page can be browsed; the buttons that would change it are switched off. Create an account to run TaxoReview on your own data. A simulation from public publication records: the people listed were not invited and did not agree to review.

? Tutorial for this page

Demo Data from EDBT 2026 / reviewers in the wild / expert

dblp:411/1716 · DBLP profile ↗

← Back

2ranked-venue papers

1as first author

2since 2021 · last 2026

0009-0006-5577-6094ORCID · reported

Domains — the database's venue-derived domains; a paper can count in several

Systems, architecture and hardware · 2 · 1 first-author · 2 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

Efficient and distributed learning · 73% Language models and text generation · 22% Deep learning architectures and training · 5%

Topics — the 4 heaviest of 5, each with the papers that count most for it

Topic	Weight	Papers	Last	Evidence papers
Machine learning › Efficient and distributed learning › distributed training › model parallelism pipeline parallelism	1.9	2	2026	Crimson: Collaborative Parameter Updates for Efficient Pipeline Training of Large Language Models · EuroSys 2026 Obscura: Concealing Recomputation Overhead in Training of Large Language Models with Bubble-filling Pipeline Transformation · USENIX ATC 2025
Natural language and speech › Language models and text generation large language model training	1.2	2	2026	Obscura: Concealing Recomputation Overhead in Training of Large Language Models with Bubble-filling Pipeline Transformation · USENIX ATC 2025 Crimson: Collaborative Parameter Updates for Efficient Pipeline Training of Large Language Models · EuroSys 2026
Machine learning › Efficient and distributed learning distributed training	1.0	1	2026	Crimson: Collaborative Parameter Updates for Efficient Pipeline Training of Large Language Models · EuroSys 2026
Machine learning › Efficient and distributed learning memory-efficient training	1.0	1	2026	Crimson: Collaborative Parameter Updates for Efficient Pipeline Training of Large Language Models · EuroSys 2026

Methods — techniques the papers use, named apart from their topics

bubble-filling pipeline transformation · 0.9

Year	Publication	Venue	Position
2026	Crimson: Collaborative Parameter Updates for Efficient Pipeline Training of Large Language Models abstract Large language models (LLMs) have driven significant progress in natural language processing, yet their training and fine-tuning remain limited by memory constraints, particularly the substantial memory footprints of optimizer states. Existing solutions address this challenge by offloading optimizer states and update tasks to the CPU, but this often leads to increased GPU idleness due to the CPU's limited computational capabilities, especially in pipeline parallelism. Yapeng Jiang, Wuhui Chen, Ganhong Huang, Yuzhou Huang, Zicong Hong, Song Guo 0001, Yue Yu 0001	EuroSys	1
2025	Obscura: Concealing Recomputation Overhead in Training of Large Language Models with Bubble-filling Pipeline Transformation Yuzhou Huang, Yapeng Jiang, Zicong Hong, Wuhui Chen, Bin Wang 0034, Weixi Zhu, Yue Yu 0001, Zibin Zheng	USENIX ATC	2