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Jun Zhuang

Jun Zhuang has 2 papers in our library, in 2026, most of them on Language models & pretraining, Speech & spoken language; often with Haohan Wang.

2 papers in our library · 2026

OpenAlex · ORCID · Wikidata · h-index 6 · 29 works · 110 citations OpenAlex, 2026-10-14

📄 2 works in our library · ⭐ Paper Stars ranking: #12995

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

🎓Boise State UniversityBoise · United States2023–2026 · 30 papersEmployment since 2023current🎓University at Buffalo, State University of New YorkBuffalo · United States2018Education until 2018current🎓Indiana University – Purdue University IndianapolisIndpls · United States2019–2024 · 10 papersEducation until 2023🎓Purdue University SystemWest Lafayette · United States2023 · 2 papers🎓Indiana UniversityBloomington · United States2019–2020 · 2 papers🎓Rochester Institute of TechnologyRochester · United States2013Education until 2013🎓South China University of TechnologyGuangzhou · China2011Education until 2011

From the bylines of their papers (OpenAlex, 2026-10-14), ORCID and Wikidata; institutions on papers are matched by machine. 4 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.

📚 Shelves 6The field of science this work belongs to (OpenAlex fields).Artificial Intelligence · 2 worksComputer Science · 2 worksAtomic and Molecular Physics, and Optics · 1 workPhysics and Astronomy · 1 workArts and Humanities · 1 workLanguage and Linguistics · 1 work

🧭 Topics 2Research subjects and languages the work is about.Language models & pretraining · 2 worksSpeech & spoken language · 1 work

 Works by year

2020s

20262 papers

 Papers

The most cited first (OpenAlex and our library).

  1. Large Language Models Can Help Mitigate Barren Plateaus in Quantum Neural Networks (2026 · 1 citation)
    Quantum neural networks (QNNs) are often untrainable because of barren plateaus (BPs). At initialization, gradient variance shrinks exponentially as qubits are added, so gradient-based optimizers stall almost immediately. Existing initialization fixes, such as GaInit and BeInit, sample parameters once from a fixed…
  2. Now You Hear Me: Audio Narrative Attacks Against Large Audio–Language Models (2026)
    The paper asks whether the way something is said, and not only its wording, can make large audio–language models (LALMs) ignore their safety training.

 Co-authors here

1 co-author 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.

Updated

This page in Markdown → https://md.research.elamur.ai/free/en/authors/jun-zhuang/