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Welcome to the UVA AXIS Research Lab

We are a research team of Data Science and Computer Science at the University of Virginia. We research AI × Infrastructure & Systems (AXIS). Our aim is to explore and build better systems software to sustain the requirements of next-generation data-intensive applications.

We are interested in addressing challenges in real-world AI systems. Our research rethinks the abstractions underlying AI storage, compute, and resource allocation, guided by deep workload characterization . Our goal is to make AI infrastructure scalable, sustainable, and accessible to everyone who builds and uses AI.

Our current efforts are focused on: (1) Rethinking Storage Systems Abstractions for AI: rethinking storage system designs to sustain the exponential AI data explosion. (2) Rethinking AI Compute and Resource Allocation: enabling elastic and affordable GPU resources for both training and inference. (3) Advancing Serverless Computing Foundation: improving serverless computing using an end-to-end approach that cuts across the entire system stack, spanning applications, middleware, platforms, and OS.

We are committed to training the next generation of AI and data systems researchers and developers. Our lab has openings at all levels — PhD/MS/BS students, research interns, and visiting students, with flexible start dates, to work on exciting research on AI systems. Feel free to reach out via email. Check our recent publications for details.

Lab news

Papers, awards, grants, and people.

All news
    • 🚀 We’ve launched LooseLeaf, a highly efficient Jupyter notebook platform offering free serverless GPUs to everyone at UVA. Learn more about the LooseLeaf design in our paper preprint on arXiv.
    • 🎉 Congrats to Yuqi and Ruizhe on Allpass accepted to ACM SoCC 2026!
    • Our survey paper on resource-efficient LLMs is published at ACM Computing Surveys!
    • 🎉 Congrats to Zirui and Zhaoyuan on LatentStore and Zero-Prefill accepted to NSDI 2027! Stay tuned for more updates on the projects!
    • 🎉 Congrats to Tingfeng, Zirui, Yunjia, and Zhaoyuan on TensorDex accepted to SOSP 2026! TensorDex explores fine-grained tensor-level relationships across massive AI model hubs to achieve significant lossless reduction in large-scale model storage. Stay tuned for more updates on the project!
    • 🎉 One paper accepted to SC'26. Congratulations to Patrick and the team!
    • 🎉 Congrats to Tingfeng and Yusen on ZenFlow accepted to SIGMOD 2027! ZenFlow enables stall-free offloaded LLM training and is integrated in DeepSpeed.

Team

Full team

Ph.D. Students

3
  • Zirui Wang

    2nd-year Ph.D. student in Computer Science

  • Tingfeng Lan

    2nd-year Ph.D. student in Computer Science

  • Yusen Wu

    1st-year Ph.D. student in Computer Science

Master's, Undergraduate & High School Students

7
  • Yajing Gao

    Undergraduate visiting scholar (Tsinghua University)

  • Chenye Wang

    Undergraduate visiting scholar (University of Science and Technology of China)

  • Junho Lee

    Undergraduate student in Computer Science @ UVA

  • Huarui Liu

    Undergraduate student in Computer Science @ UVA

  • Sherry Zhao

    Undergraduate student in Computer Science @ UVA

  • EZ

    Emi Zhang

    Sophomore at Thomas Jefferson High School for Science and Technology

  • JS

    Jawand Singh

    Sophomore at Thomas Jefferson High School for Science and Technology

Alumni

8
  • Yuqi Fu

    Faculty position (TBD)

  • Zhaoyuan (Alex) Su

    Research Scientist, Snowflake AI Research

  • Rui Yang

    Software Engineer, ByteDance Cloud Native Infrastructure (Seattle, WA)

  • Ben Carver

    Research Scientist, AI Networking Infrastructure @ Meta

  • Rafael Alejandro Madrid Rivera

    Computer Systems Engineer, GMU

  • Zheng Chai

    Ph.D.

  • Jingyuan Zhang

    Cloud Native Infrastructure @ ByteDance (San Jose, CA)

  • Ao Wang

    Technical Expert @ Alibaba Cloud Function Computing

Selected publications

All publications (66)

Open-source projects

Systems and software artifacts from our research, available at github.com/ds2-lab.

18 projects 533 stars All projects

LooseLeaf

Serverless AI

A highly efficient Jupyter notebook platform offering free serverless GPUs to everyone at UVA.

NotebooksGPUs

TensorDex

Storage for AI

2

A compact, tensor-centric storage system that exploits fine-grained tensor-level relationships across model hubs for lossless model storage reduction.

SOSP'26Artifact Award

ZipLLM / BitX

Storage for AI

9

A model-aware, lossless data reduction pipeline for large-scale LLM storage, with BitX, a 10+ GB/s compressor for safetensors files.

NSDI'26 Code

NotebookOS

Serverless AI

8

A GPU-efficient Jupyter Notebooks-as-a-Service platform that allocates GPUs on demand for interactive deep learning training workloads.

ASPLOS'26

ZenFlow

Serverless AI

A stall-free offloading engine for LLM training that decouples GPU and CPU parameter updates. Integrated into DeepSpeed.

SIGMOD'27DeepSpeed

SHADE

Storage for AI

36

Enables fundamental cacheability for distributed deep learning training by caching the most important training samples without losing accuracy.

FAST'23

InfiniCache

Serverless Storage

253

A cost-effective in-memory object cache built entirely atop ephemeral serverless functions, using erasure coding to keep data available.

FAST'20 Code

Wukong

Serverless Analytics

78

A scalable, locality-enhanced serverless parallel framework that decentralizes DAG scheduling across AWS Lambda executors.

SoCC'20 Code

Funded research

Most of our funded projects are interdisciplinary. We are grateful for the support of our sponsors.

All funded projects (13)

Elements: A Sustainable, Resource-Efficient Cyberinfrastructure for Notebook Interactive ML Training Workloads

PIs: Yue Cheng, Geoffrey Fox

NSF OAC-2411009 $600,000

Collaborative Research: OAC Core: Distributed Graph Learning Cyberinfrastructure for Large-scale Spatiotemporal Prediction

PIs: Yue Cheng

NSF OAC-2403313 $299,973

Highly Efficient Pre-Trained LLM Storage with Near-Storage Compression and CXL Memory Integration

PIs: Yue Cheng, Ali Anwar (UMN)

Samsung GRO 2023 Award $250,000

CAREER: Harnessing Serverless Functions to Build Highly Elastic Cloud Storage Infrastructure

PIs: Yue Cheng

NSF CNS-2322860 $572,897 + $16,000 REU