Nicholas Lane
Professor of Machine Learning Systems
Leads CaMLSys on scalable and efficient end-to-end machine learning systems, including on-device and federated learning over multimodal sensor data.
Nicholas Lane's lab at Cambridge works on efficient training & inference, trustworthy & safe ML and vision-language & multimodal. 12 papers since 2023, 5 at top venues, mostly ICLR and JMLR. 13 PhD students. 8 alumni. Applications close .
At a glance
- papers, 2023–2026
- 3/ year
- at T1 venues
- 42%
- PhD students
- 13
- MS students
- 0
- alumni listed
- 8
Papers by yearfrom the lab homepage, 2023–2026
- 0 papers2026 · T1 0 · collecting
- 1 papers2025 · T1 1
- 6 papers2024 · T1 1
- 5 papers2023 · T1 3
T1 venuesOtherVenues: ICLR, JMLR, KDD, NeurIPS, USENIX Security
Research focuspaper titles that use each topic's words
- Efficient training & inference3 papers
- Vision-language & multimodal2 papers
- Trustworthy & safe ML0 papers
- end-to-end0 papers
- sensor data0 papers
Paper list
From the lab homepage.
20251 paper
- ICLR (T1)DEPT: Decoupled Embeddings for Pre-training Language Models
20246 papers
- Proceedings of ICLR and International Workshop on Federated Learning in the Age of Foundation Models, NeurIPSBreaking Physical and Linguistic Borders: Multilingual Federated Prompt Tuning for Low-Resource Languages
- Proceedings of the Fifth AAAI Privacy-Preserving Artificial Intelligence (PPAI-24) WorkshopEvaluating Privacy Leakage in Split Learning
- KDD (T1)FLea: Addressing Data Scarcity and Label Skew in Federated Learning via Privacy-preserving Feature Augmentation
- KDD WorkshopHow Data Inter-connectivity Shapes LLMs Unlearning: A Structural Unlearning Perspective
- NeurIPS WorkshopThe Future of Large Language Model Pre-training is Federated
- Harvard Kennedy School Misinformation ReviewThe spread of synthetic media on X
20235 papers
- JMLR (T1)A First Look into the Carbon Footprint of Federated Learning
- Federated Learning Systems Workshop, MLSysEfficient Vertical Federated Learning with Secure Aggregation
- NeurIPS (T1)FedL2P: Federated Learning to Personalized
- USENIX Security (T1)FedVal: Different good or different bad in federated learning
- Proceedings of the Workshop on Machine Learning and SystemsGradient-less Federated Gradient Boosting Tree with Learnable Learning Rates
Where alumni went8 listed
Apply to CambridgeMichaelmas 2027
PhD in Computer Science
- Round
- Funding round (8 Dec 2026)
- Opens
- GRE
- See program page
- English
- See program page
- Fee
- See program page
Confirmed on the program page.
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Data
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- Papers
- The lab homepage
- Members
- Lab homepage
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- Lab homepage
Tiers are venue classifications, not lab rankings.
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