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‎about/index.html‎

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"url": "https://irislucent.github.io/about/",
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"name": "Yuxuan Wu - About",
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"description": "TODO\n",
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‎atom.xml‎

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<feed xmlns="http://www.w3.org/2005/Atom" xml:lang="en">
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<title type="text">Yuxuan Wu</title>
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<subtitle type="html">YuxuanWu</subtitle>
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<updated>2025-07-17T16:44:30&#43;08:00</updated>
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<updated>2025-08-07T16:24:32&#43;04:00</updated>
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<id>https://irislucent.github.io/</id>
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<link rel="alternate" type="text/html" href="https://irislucent.github.io/" />
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<title type="text">Test</title>
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<link rel="alternate" type="text/html" href="https://irislucent.github.io/posts/test/" />
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<id>https://irislucent.github.io/posts/test/</id>
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<updated>2023-06-03T14:32:56&#43;08:00</updated>
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<updated>2023-06-03T10:32:56&#43;04:00</updated>
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<published>2022-10-03T06:55:36-04:00</published>
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<author>
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<name>Yuxuan Wu</name>

‎cv/Yuxuan_Wu_CV_20250807.pdf‎

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‎experiences/index.html‎

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"datePublished": "2022-10-03T08:45:26-04:00",
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"dateModified": "2025-05-15T16:58:24+08:00",
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"dateModified": "2025-05-15T12:58:24+04:00",
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"url": "https://irislucent.github.io/experiences/",
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"name": "Yuxuan Wu - Experiences",
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"description": " [Project Name] Project Timeline Research talk on emergent content-style disentanglement at National University of Singapore, April 2025 Poster presentation at ICLR, April 2025 AI-music performance with representatives from China National Symphony Orchestra, December 2024 Research talk on self-supervised learning at Nanjing University of the Arts, June 2024 September 2023 - Now Research on self-supervised learning and interpretable representation learning August 2023 Entered MBZUAI August 2023 M.Sc. in Music and Technology Oral presentation at ICASSP, June 2023 February - August 2023 Research on representation learning of symbolic music motifs January - May 2023 Teaching assistant: Introduction to Computer Music Giving a computer music recitation at CMU, February 2023 August 2022 - April 2023 Research on AI jazz improvisation tutoring August 2022 - December 2022 Teaching assistant: Machine Learning for Signal Processing Giving a lecture about HMM at CMU, November 2022 May - August 2022 Internship at TikTok SAMI team January - May 2022 Participate in AI Song Contest 2022 Speaking at AISC Ceremony, June 2022 January - May 2022 Research project: Timbre transfer with flexible timbre control (ICASSP 2023) November 2021 - November 2023 Research on expressive performance synthesis September 2021 - Janurary 2022 Course project: Speech anonymization with pseudo VC (Interspeech 2022) Presentation at project poster, December 2021 August 2021 Entered Cargenie Mellon University June 2021 B.Eng. in Information Engineering In the studio, recording for an SJTU gala, June 2021 September 2020 - June 2021 Bachelor thesis on a speech synthesis tool Presentation at CSMT, November 2020 July 2020 - July 2021 Internship at ByteDance AI-Lab SA Performance at the Freshman Welcoming Gala, June 2019 September 2019 - August 2020 Research on vocal register recognition at LAMT, Fudan University July-August 2019 Summer school at UC Berkeley, studying Sound and Music Computing Performance at the 110th Anniversary of School of Electronic Information and Electrical Engineering, October 2018 September 2017 Entered Shanghai Jiao Tong University ",
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‎index.html‎

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"url": "https://irislucent.github.io/",
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"description": "Hello there - welcome to my personal webpage!\nMy name is Yuxuan Wu. I'm a second-year PhD student of Music X Lab at Mohamed bin Zayed University of Artificial Intelligence (MBZUAI), advised by Professor Gus Xia and Professor Bhiksha Raj.\nAI emerges not only for music but also from it. My research interest lies in self-supervised learning and interpretable representation learning, as well as their applications in audio and music. The core of my research is inspired by the hierarchical nature of human perception, thinking, and communication, with music as their most intriguing manifestation. Building on this, I am dedicated to researching AI that more closely resembles human intelligence, enhancing both AI systems' effectiveness and human artistry.\n",
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By the way I'm proud to stand as a MtF. As I got some vital help and care from those in China when I was frustrated and confused the most, I pay attention to and am willing to attend LGBTQ+ nonprofit organizations because millions of brothers and sisters are still there in need, and that we should stand together to make the world a more diverse and colorful place.
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<p><a href="./cv/Yuxuan_Wu_CV_20250717.pdf"><font color=#f06292>Curriculum Vitae</font></a></p>
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<p><a href="./cv/Yuxuan_Wu_CV_20250807.pdf"><font color=#f06292>Curriculum Vitae</font></a></p>
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‎music/index.html‎

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"url": "https://irislucent.github.io/music/",
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"name": "Yuxuan Wu - Music",
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"description": "\nBowOfAtlas I publish music in the name of BowOfAtlas starting from 2014.\nSpotify Page Apple Music Page Amazon Music Page QQ Music Page NetEase Music Page Albums / Mini-albums: Fallen Florets (2024) CTcity (Original Game Soundtrack) (2024) Evanescent Boundaries (2022) Previous Track, Pause (2020) A Glass of Clouds (2019) The Cold Fish Logbook (2018) AI Song Contest 2022 Our team 3+i participated in AI Song Contest 2022 with our music A to I.\nTeam members include:\n",
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‎posts/index.html‎

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‎posts/test/index.html‎

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<meta property="og:locale" content="en" /><meta property="og:image" content="https://irislucent.github.io/icons/apple-touch-icon.png" />
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‎projects/index.html‎

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"url": "https://irislucent.github.io/projects/",
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"name": "Yuxuan Wu - Projects",
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"description": " Variance Versus Invariance We contribute an unsupervised method V3 (variance-versus-invariance) that learns disentangled content and style representations from sequences of observations. Unlike most methods that rely on domain-specific labels or knowledge, our method is based on the domain-general statistical differences between content and style --- content varies more among different fragments within a sample but maintains an invariant vocabulary across data samples, whereas style remains relatively invariant within a sample but exhibits more significant variation across different samples. V3 outperforms existing unsupervised methods in disentanglement and surpasses supervised models in out-of-distribution generalization under few-shot adaptation. Also, the learned content codebook exhibits symbolic-level interpretability, aligning machine representations closely with human knowledge.\n",
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‎rss.xml‎

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