Xin Jin
Associate Professor with Tenure
Researches computer systems, operating systems and networks, including systems for serving large models.
Xin Jin's lab at Peking works on efficient training & inference, distributed, cloud & edge systems and large language models. 26 papers since 2023, 12 at top venues, mostly SIGCOMM and SOSP. 4 alumni. Applications close .
At a glance
- papers, 2023–2026
- 7/ year
- at T1 venues
- 46%
- PhD students
- 0
- MS students
- 0
- alumni listed
- 4
Papers by yearfrom the lab homepage, 2023–2026
- 0 papers2026 · T1 0 · collecting
- 0 papers2025 · T1 0
- 9 papers2024 · T1 2
- 17 papers2023 · T1 10
T1 venuesOtherVenues: SIGCOMM, SOSP, ASPLOS, SIGMOD
Research focuspaper titles that use each topic's words
- Efficient training & inference10 papers
- Large language models9 papers
- Distributed, cloud & edge systems7 papers
- serving large0 papers
- operating systems0 papers
Paper list
From the lab homepage.
20249 papers
- USENIX OSDIBurstable Cloud Block Storage with Data Processing Units
- IEEE/ACM Transactions on NetworkingDistMind: Efficient Resource Disaggregation for Deep Learning Workloads
- USENIX OSDIDistServe: Disaggregating Prefill and Decoding for Goodput-optimized Large Language Model Serving
- USENIX OSDIdLoRA: Dynamically Orchestrating Requests and Adapters for LoRA LLM Serving
- USENIX NSDIFast Vector Query Processing for Large Datasets Beyond GPU Memory with Reordered Pipelining
- USENIX NSDIJolteon: Unleashing the Promise of Serverless for Serverless Workflows
- SOSP (T1)LoongServe: Efficiently Serving Long-Context Large Language Models with Elastic Sequence Parallelism
- USENIX NSDIScaling Large Language Model Training to More Than 10,000 GPUs
- ASPLOS (T1)SoCFlow: Efficient and Scalable DNN Training on SoC-Clustered Edge Servers
202317 papers
- USENIX OSDIAlpaServe: Statistical Multiplexing with Model Parallelism for Deep Learning Serving
- SOSP (T1)Automated Verification of an In-Production DNS Authoritative Engine
- ASPLOS (T1)Disaggregated RAID Storage in Modern Datacenters
- SIGCOMM (T1)Ditto: Efficient Serverless Analytics with Elastic Parallelism
- ASPLOS (T1)ElasticFlow: An Elastic Serverless Training Platform for Distributed Deep Learning
- ACM TOSEMFaaSLight: General Application-level Cold-start Latency Optimization for Function-as-a-Service in Serverless Computing
- USENIX NSDIFast, Approximate Vector Queries on Very Large Unstructured Datasets
- SOSP (T1)Halfmoon: Log-Optimal Fault-Tolerant Stateful Serverless Computing
- SIGCOMM (T1)Klotski: Efficient and Safe Network Migration of Large Production Datacenters
- SOSP (T1)Oobleck: Resilient Distributed Training of Large Models Using Pipeline Templates
- ACM TOSEMRise of Distributed Deep Learning Training in the Big Model Era: From a Software Engineering Perspective
- ACM TOSEMRise of the Planet of Serverless Computing: A Systematic Review
Where alumni went4 listed
Apply to PekingFall 2027
Computer Science PhD (international applicants)
- Round
- Main
- Opens
- —
- GRE
- See program page
- English
- See program page
- Fee
- See program page
Not yet confirmed for this intake.
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- Papers
- The lab homepage
- Members
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Tiers are venue classifications, not lab rankings.
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