Ang Li
Assistant Professor
Develops efficient and trustworthy on-device AI systems, including federated learning and edge inference for LLMs.
Ang Li's lab at UMD works on efficient training & inference, distributed, cloud & edge systems and trustworthy & safe ML. 20 papers since 2023, 11 at top venues, mostly ICLR and MobiCom. 10 PhD students and 1 master's student.
At a glance
- papers, 2023–2026
- 5/ year
- at T1 venues
- 55%
- PhD students
- 10
- MS students
- 1
- alumni listed
- —
Papers by yearfrom the lab homepage, 2023–2026
- 0 papers2026 · T1 0 · collecting
- 6 papers2025 · T1 2
- 10 papers2024 · T1 6
- 4 papers2023 · T1 3
T1 venuesOtherVenues: ICLR, MobiCom, NeurIPS, CVPR, Findings of EMNLP
Research focuspaper titles that use each topic's words
- Efficient training & inference4 papers
- Distributed, cloud & edge systems2 papers
- Trustworthy & safe ML1 paper
- develops efficient0 papers
- on-device0 papers
Paper list
From the lab homepage.
20256 papers
- Findings of EMNLPBag of Tricks for Sparse Mixture-of-Experts: A Benchmark Across Reasoning, Efficiency, and Safety
- Proceedings of the Annual International Conference on Mobile Systems, Applications and ServicesEdgeLoRA: An Efficient Multi-Tenant LLM Serving System on Edge Devices
- IEEE/CVF Winter Conference on Applications of Computer Vision (WACV)MLLM-LLaVA-FL: Multimodal Large Language Model Assisted Federated Learning
- ACM SIGMETRICS Performance Evaluation ReviewRethinking Pruning for Vision-Language Models: Strategies for Effective Sparsity
- NeurIPS (T1)SymRTLO: Enhancing RTL Code Optimization with LLMs and Neuron-Inspired Symbolic Reasoning
- ICLR (T1)Towards counterfactual fairness thorough auxiliary variables
202410 papers
- IEEE Internet of Things JournalADEAS: Authentication Using Doppler Effect of Acoustic Signals Caused by Hands Moving
- WSDM (T1)Efficient, direct, and restricted black-box graph evasion attacks to any-layer graph neural networks via influence function
- ICLR (T1)Enhancing One-Shot Federated Learning Through Data and Ensemble Co-Boosting
- MobiCom (T1)Federated Black-box Prompt Tuning System for Large Language Models on the Edge
- ICLR (T1)FedHyper: A Universal and Robust Learning Rate Scheduler for Federated Learning with Hypergradient Descent
- CVPR (T1)MAPSeg: Unified Unsupervised Domain Adaptation for Heterogeneous Medical Image Segmentation Based on 3D Masked Autoencoding and Pseudo-Labeling
- Proceedings of the on ACM SIGSAC Conference on Computer and Communications SecurityModerator: Moderating Text-to-Image Diffusion Models through Fine-grained Context-based Policies
- IEEE Transactions on Dependable and Secure ComputingRecipient-Aware Photo Automatic Deletion Control Policy Recommendation Scheme in Online Social Networks
- MLSys (T1)SiDA: Sparsity-Inspired Data-Aware Serving for Efficient and Scalable Large Mixture-of-Experts Models
- IEEE Transactions on Mobile Computingt-READi: Transformer-Powered Robust and Efficient Multimodal Inference for Autonomous Driving
20234 papers
- MobiCom (T1)AutoFed: Heterogeneity-Aware Federated Multimodal Learning for Robust Autonomous Driving
- ICML (T1)Fed-cbs: A heterogeneity-aware client sampling mechanism for federated learning via class-imbalance reduction
- NeurIPS (T1)FedNAR: Federated Optimization with Normalized Annealing Regularization
- WACVInterpreting Disparate Privacy-Utility Tradeoff in Adversarial Learning via Attribute Correlation
Where alumni went
Alumni are not listed on the lab homepage.
Apply to UMDFall 2027
Not announced yet
UMD Computer Science PhD
- Round
- Single round
- Opens
- —
- GRE
- See program page
- English
- See program page
- Fee
- See program page
Not yet confirmed for this intake.
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Data
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- The alumni list could not be found on the lab site.
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