Scholar
Ziheng Jiang
Google Scholar ID: tuRCeekAAAAJ
Research Scientist, ByteDance
Citations & Impact
Citations
4,400
H-index
19
i10-index
23
Publications
20
Co-authors
0
Publications
Resume (English only)
Academic Achievements
- Ziheng's work has been cited more than 4000 times. Some selected papers include:
- - MegaScale: Scaling Large Language Model Training to More Than 10,000 GPUs (2024)
- - MegaScale-Infer: Serving Mixture-of-Experts at Scale with Disaggregated Expert Parallelism (2025)
- - Understanding Stragglers in Large Model Training Using What-if Analysis (2025)
- - Comet: Fine-grained Computation-communication Overlapping for Mixture-of-Experts (2025)
- - TileLink: Generating Efficient Compute-Communication Overlapping Kernels using Tile-Centric Primitives (2025)
- - FLUX: Fast Software-based Communication Overlap On GPUs Through Kernel Fusion (Preprint, 2024)
- - Characterizing Structural Regularities of Labeled Data in Overparameterized Models (ICML, 2021)
- - Learning to optimize tensor programs (NIPS, 2018)
- - TVM: An Automated End-to-End Optimizing Compiler for Deep Learning (OSDI, 2018)
- - Efficient Deep Learning Inference on Edge Devices (MLSys, 2018)
Research Experience
- Heavily involved in projects such as MegaScale, Apache TVM, and Apache MXNet.
Education
- Ph.D. from the Paul G. Allen School of Computer Science & Engineering at the University of Washington, advised by Luis Ceze and Tianqi Chen; Bachelor’s degree from Fudan University, where he was a member of Fudan NLP Lab, working with Xipeng Qiu and Zheng Zhang.
Background
- Ziheng works on large language model (LLM) systems at ByteDance, focusing on scaling and optimizing LLM training and inference. His research interests include Machine Learning Systems and Large Language Models.
Co-authors