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Zhanghao Wu

Zhanghao Wu has 2 papers in our library, in 2020–2023, most of them on Language models & pretraining, GPU & parallel computing; often with Chuang Gan, Eric Xing, Ion Stoica.

2 papers in our library · 2020–2023

OpenAlex · ORCID · Wikidata · h-index 17 · 33 works · 4,050 citations OpenAlex, 2026-10-14

📄 2 works in our library · 🔗 Cited 274 times in our library · ⭐ Paper Stars ranking: #3285

The profile is the library's match of the name; two people with one name are not always told apart.

Elsewhere: Google Scholar · Semantic Scholar · dblp · ACL Anthology

 Affiliations

🎓University of California, BerkeleyBerkeley · United States2020–2026 · 10 paperscurrent🔬Collaborative Innovation Center of Suzhou Nano Science and TechnologySuzhou · China2024 · 1 papercurrent🔬Key Lab of Modern Optical Technologies of Education Ministry of ChinaSuzhou · China2024 · 1 papercurrent🎓Soochow UniversitySuzhou · China2024 · 1 papercurrent🎓Shanghai Jiao Tong UniversityShanghai · China2020–2021 · 4 papers🎓Massachusetts Institute of TechnologyCambridge · United States2019–2020 · 4 papers

From the bylines of their papers (OpenAlex, 2026-10-14), ORCID and Wikidata; institutions on papers are matched by machine. 1 more institution on single bylines are not listed.

 Concepts, shelves and topics

Of its works in our library: the number of works on each and its rating among them.

📚 Shelves 5The field of science this work belongs to (OpenAlex fields).Arts and Humanities · 2 worksComputer Science · 2 worksArtificial Intelligence · 2 worksLanguage and Linguistics · 2 worksComputer Vision and Pattern Recognition · 1 work

🧭 Topics 2Research subjects and languages the work is about.GPU & parallel computing · 1 workLanguage models & pretraining · 1 work

 Works by year

2020s

20231 paperLMSYS-Chat-1M: A Large-Scale Real-World LLM Conversation Dataset20201 paperHAT: Hardware-Aware Transformers for Efficient Natural Language Processing

 Papers

The most cited first (OpenAlex and our library).

  1. HAT: Hardware-Aware Transformers for Efficient Natural Language Processing (2020 · 290 citations)
    Transformer models for machine translation are too slow on constrained devices. For example, Transformer-Big needs about 13G FLOPs and roughly 20 seconds on a Raspberry Pi to translate a 30-word sentence. The authors make two observations: FLOPs poorly predict real latency, and the architecture that runs fastest…
  2. LMSYS-Chat-1M: A Large-Scale Real-World LLM Conversation Dataset (2023 · 236 citations)
    Research into how people use large language models has been held back by the lack of public logs of genuine user interactions.

 Co-authors here

12 of 13 co-authors with a page, the most papers together first.

Venues

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

 Cited by

Papers of our library cite their works 274 times (of the 141,840 papers whose bibliographies we read); 18 citing papers with a page here →.

Updated

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