Stephanie Wang
Assistant Professor
Builds large-scale distributed systems for machine learning, including LLM training and inference.
Stephanie Wang's lab at UW works on efficient training & inference and distributed, cloud & edge systems. 11 papers since 2023, 7 at top venues, mostly MLSys and OSDI. 72 alumni. Applications close .
At a glance
- papers, 2023–2026
- 3/ year
- at T1 venues
- 64%
- PhD students
- 0
- MS students
- 0
- alumni listed
- 72
Papers by yearfrom the lab homepage, 2023–2026
- 2 papers2026 · T1 2 · collecting
- 5 papers2025 · T1 2
- 1 papers2024 · T1 1
- 3 papers2023 · T1 2
T1 venuesOtherVenues: MLSys, OSDI, NeurIPS, SIGCOMM
Research focuspaper titles that use each topic's words
- Efficient training & inference3 papers
- Distributed, cloud & edge systems1 paper
- distributed systems1 paper
- llm1 paper
- large-scale0 papers
Paper list
From the lab homepage.
20262 papers
- MLSys (T1)DynaFlow: Transparent and Flexible Intra-Device Parallelism via Programmable Operator Scheduling
- MLSys (T1)TeleRAG: Efficient Retrieval-Augmented Generation Inference with Lookahead Retrieval
20255 papers
- MLSys (T1)FlashInfer: Efficient and Customizable Attention Engine for LLM Inference Serving (opens in a new tab)
- OSDI (T1)Nanoflow: Towards optimal large language model serving throughput (opens in a new tab)
- PACMIPiper: Towards Flexible Pipeline Parallelism for PyTorch (opens in a new tab)
- HotNetsProgrammable and Adaptive Scheduling for Distributed Systems (opens in a new tab)
- HotOSTowards ML System Extensibility (opens in a new tab)
20241 paper
- NeurIPS (T1)Datacomp-lm: In search of the next generation of training sets for language models
Where alumni went72 listed
Apply to UWFall 2027
UW Allen School Computer Science PhD
- Round
- Single round
- Opens
- GRE
- not required
- English
- See program page
- Fee
- See program page
Not yet confirmed for this intake.
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Your notes
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Data
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- Collected
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- Papers
- The lab homepage
- Members
- Lab homepage
- Alumni
- Lab homepage
This lab shares a website with other labs. Member and alumni counts may include them.
Tiers are venue classifications, not lab rankings.
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