🧑‍🔬 Luis Ceze

🏛️ Affiliations

🎓University of WashingtonSeattle · United States2007–2026 · 1,064 papersEmployment since 2007 · Professor, Paul G Allen School of Computer Science and Engineeringcurrent🎓Carnegie Mellon UniversityPittsburgh · United States2026 · 1 papercurrent🎓University of Illinois Urbana-ChampaignUrbana · United States2003–2009 · 17 papersEducation🏢MicrosoftRedmond · United States2016–2021 · 6 papers🏢IBMArmonk · United States2002–2005 · 3 papers🔬IBM Research - Thomas J. Watson Research CenterYorktown Heights · United States2003–2005 · 3 papers🎓Universidade de São PauloSão Paulo · Brazil2002–2003 · 2 papersEducation

From the bylines of their papers (OpenAlex, 2026-10-14), ORCID and Wikidata; institutions on papers are matched by machine. 5 more institutions on single bylines are not listed.

🧭 Fields, topics and concepts

Of its works in our library, with the number of works on each.

📅 Works by year

2020s

2010s

📄 Papers

The most cited first (OpenAlex and our library).

1. TVM: An Automated End-to-End Optimizing Compiler for Deep Learning (2018 · 1,384 citations)
   Deep learning models increasingly need to run on very different hardware: server GPUs, phones, embedded boards, and custom accelerators such as FPGAs and TPU-like chips.
2. Learning to Optimize Tensor Programs (2019 · 269 citations)
   Deep learning systems depend on fast tensor operators such as matrix multiplication and convolution. Today these come mostly from hand-tuned vendor libraries like cuDNN, which cover only a narrow set of hardware and make every new device expensive to support. The authors treat operator implementation as a search…
3. Atom: Low-bit Quantization for Efficient and Accurate LLM Serving (2023 · 156 citations)
   LLM serving relies on batching to keep GPUs busy, and quantization can further reduce memory use and compute cost.
4. Punica: Multi-Tenant LoRA Serving (2023 · 81 citations)
   Organisations that fine-tune one base LLM with LoRA for many tenants usually end up running each adapter as a separate model.

All works

6 works of theirs in our library, the most cited first; 4 with a page here, the others link their original until we summarise them.

1. TVM: An Automated End-to-End Optimizing Compiler for Deep LearningarXiv:1802.04799Tianqi Chen, Thierry Moreau, Ziheng Jiang, et al.2018 · 1,384 citations
2. Learning to Optimize Tensor ProgramsarXiv:1805.08166Tianqi Chen, Lianmin Zheng, Eddie Yan, et al.2019 · 269 citations
3. A Hardware–Software Blueprint for Flexible Deep Learning Specialization ↗IEEE Micro · no summary yetThierry Moreau et al.2019 · 184 citations
4. Atom: Low-bit Quantization for Efficient and Accurate LLM ServingMLSys 2024Yilong Zhao, Chien-Yu Lin, Kan Zhu, et al.2023 · 156 citations
5. Punica: Multi-Tenant LoRA ServingMLSys 2024Lequn Chen, Zihao Ye, Yongji Wu, et al.2023 · 81 citations
6. Inter-Disciplinary Research Challenges in Computer Systems for the 2020s ↗no summary yetAlbert Cohen et al.2018 · 16 citations

🤝 Co-authors here

12 of 14 co-authors with a page, the most papers together first.

• Arvind Krishnamurthy ×4
• Tianqi Chen ×3
• Carlos Guestrin ×2
• Eddie Yan ×2
• Lequn Chen ×2
• Lianmin Zheng ×2
• Thierry Moreau ×2
• Zihao Ye ×2
• Ziheng Jiang ×2
• Baris Kasikci
• Danyang Zhuo
• Kan Zhu

Venues

Where their works here were published, the most works first, with the editions of those that have a page.

• MLSys 2024 ×2
• IEEE Micro

🔗 Cited by

Papers of our library cite their works 742 times (of the 149,439 papers whose bibliographies we read); 40 citing papers with a page here →.

Updated 2 November 2026

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