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.
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- 🎉 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!
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- 📢 New Course Alert: I'll be teaching CS6501 AI Systems in Fall 26!
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- 🎉 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!
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- 🎉 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 teamMaster's, Undergraduate & High School Students
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Yajing Gao
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Chenye Wang
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Junho Lee
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Huarui Liu
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Sherry Zhao
- EZ
Emi Zhang
- JS
Jawand Singh
Alumni
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Rafael Alejandro Madrid Rivera
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Selected publications
LooseLeaf: Enabling Serverless GPUs for Computational Notebooks
Yusen Wu, Tingfeng Lan, Benjamin Carver, Haoliang Wang, Geoffrey Fox, Yue Cheng
C2CServe: Leveraging NVLink-C2C for Elastic Serverless LLM Serving on MIG
Shutian Luo, Ali Zafar Sadiq, Rui Yang, Mingye Zhang, Haiying Shen, Wei Wang, Yue Cheng
LatentStore: An Efficient Latent-First Storage System for AI-Generated Images
Zirui Wang, Yunjia Zheng, Tingfeng Lan, Zhaoyuan Su, Haoran Ni, Juncheng Yang, Yue Cheng
ZeRO-Prefill: Zero Redundancy Overheads in MoE Prefill Serving
Zhaoyuan Su, Olatunji Ruwase, Karthik Ganesan, Aurick Qiao, Samyam Rajbhandari, Juncheng Yang, Yue Cheng, Yuxiong He
ZenFlow: Enabling Stall-Free Offloading for LLM Training via Asynchronous Updates
Tingfeng Lan, Yusen Wu, Bin Ma, Zhaoyuan Su, Rui Yang, Tekin Bicer, Masahiro Tanaka, Olatunji Ruwase, Dong Li, Yue Cheng
TensorDex: A Compact, Tensor-Centric Storage System for Modern AI Models
Tingfeng Lan, Zirui Wang, Yunjia Zheng, Zhaoyuan Su, Juncheng Yang, Yue Cheng
MorphServe: Efficient and Workload-Aware LLM Serving via Runtime Layer Swapping and KV Cache Resizing
Zhaoyuan Su, Zeyu Zhang, Tingfeng Lan, Zirui Wang, Haiying Shen, Juncheng Yang, Yue Cheng
ZipLLM: Efficient LLM Storage via Model-Aware Synergistic Data Deduplication and Compression
Zirui Wang, Tingfeng Lan, Zhaoyuan Su, Juncheng Yang, Yue Cheng
NotebookOS: A Replicated Notebook Platform for Interactive Training with On-Demand GPUs
Benjamin Carver, Jingyuan Zhang, Haoliang Wang, Kanak Mahadik, Yue Cheng
Concurrency-Informed Orchestration for Serverless Functions
Qichang Liu, Yue Cheng, Haiying Shen, Ao Wang, Bharathan Balaji
Open-source projects
Systems and software artifacts from our research, available at github.com/ds2-lab.
LooseLeaf
Serverless AI
A highly efficient Jupyter notebook platform offering free serverless GPUs to everyone at UVA.
A compact, tensor-centric storage system that exploits fine-grained tensor-level relationships across model hubs for lossless model storage reduction.
ZipLLM / BitX
Storage for AI
A model-aware, lossless data reduction pipeline for large-scale LLM storage, with BitX, a 10+ GB/s compressor for safetensors files.
NotebookOS
Serverless AI
A GPU-efficient Jupyter Notebooks-as-a-Service platform that allocates GPUs on demand for interactive deep learning training workloads.
ZenFlow
Serverless AI
A stall-free offloading engine for LLM training that decouples GPU and CPU parameter updates. Integrated into DeepSpeed.
Enables fundamental cacheability for distributed deep learning training by caching the most important training samples without losing accuracy.
InfiniCache
Serverless Storage
A cost-effective in-memory object cache built entirely atop ephemeral serverless functions, using erasure coding to keep data available.
A scalable, locality-enhanced serverless parallel framework that decentralizes DAG scheduling across AWS Lambda executors.
Funded research
Most of our funded projects are interdisciplinary. We are grateful for the support of our sponsors.
- National Science Foundation
- Samsung
- Meta Research
- Adobe Research
- Commonwealth Cyber Initiative
- 4-VA
- Amazon Web Services
- CloudBank
- Google Cloud
- IBM Cloud
FORTAI: AI-Enabled Zero-Trust Security Framework for Agentic AI Serving Infrastructures
PIs: Yue Cheng, Songqing Chen (GMU)
Elements: A Sustainable, Resource-Efficient Cyberinfrastructure for Notebook Interactive ML Training Workloads
PIs: Yue Cheng, Geoffrey Fox
Collaborative Research: OAC Core: Distributed Graph Learning Cyberinfrastructure for Large-scale Spatiotemporal Prediction
PIs: Yue Cheng
Highly Efficient Pre-Trained LLM Storage with Near-Storage Compression and CXL Memory Integration
PIs: Yue Cheng, Ali Anwar (UMN)
Serverless and Scalable GNN Training with Disaggregated Compute and Storage
PIs: Yue Cheng, Liang Zhao (Emory)
CAREER: Harnessing Serverless Functions to Build Highly Elastic Cloud Storage Infrastructure
PIs: Yue Cheng