I am an Assistant Professor in the Department of Performing Arts Technology at the University of Michigan. I am also affiliated with Computer Science and Engineering and Electrical and Computer Engineering.

My research aims at augmenting human creativity with generative AI. I develop human-centered generative AI technology that can be integrated into professional creative workflows, with a focus on music, audio, and video creation. My long-term goal is to make professional content creation accessible to everyone.

My research interests include music generation, music technology, audio synthesis, video editing, and multimodal AI. Here are the three main pillars of my research:

• I develop generative models for music creation where I pioneer the adoption of deep neural networks for generating multi-instrument music. Topics include multitrack music generation (AAAI 2018, ISMIR 2018, ISMIR 2020, ICASSP 2023, ISMIR 2024), text-to-music generation (ISMIR 2025), video-to-music generation (ISMIR 2025), and symbolic music processing tools (ISMIR LBD 2019, ISMIR 2020).
• I build AI-assisted music creation tools that aim to augment human creativity in their creative workflow. Topics include expressive violin performance synthesis (ICASSP 2022, ICASSP 2025), music instrumentation (ISMIR 2021), music arrangement (AAAI 2018), and music harmonization (JNMR 2020).
• I develop multimodal generative models for content creation that can process, understand and generate data in multiple modalities at the same time. Topics include long-to-short video editing (ICLR 2025, NeurIPS 2025), text-queried sound separation (ICLR 2023), and text-to-audio synthesis (WASPAA 2023).

Currently, I am most interested in multimodal generative AI and human-AI co-creative tools for music, audio, and video creation.

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 Recent Talks

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 Currently Teaching

• Generative AI for Music and Audio Creation (PAT 464/564)

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 News

• 🏅 Selected for the AAAI New Faculty Highlights at AAAI 2026
• 📜 Paper on long-to-short video editing (demo) accepted to NeurIPS 2025
• 💡 Organized the AI for Music Workshop at NeurIPS 2025
• 📜 Paper on text-to-symbolic music generation (demo) accepted to ISMIR 2025
• 📜 Paper on video-guided text-to-music generation (demo) accepted to ISMIR 2025
• 📜 Paper on documentary teaser generation (demo) accepted to ICLR 2025
• 📜 Paper on expressive violin synthesis (demo) accepted to ICASSP 2025
• 📜 Paper on music LLMs (demo) accepted to ICASSP 2025
• 🏅 Awarded the Doctoral Award for Excellence in Research by UCSD CSE Department

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 Team

Please read this if you’re interested in working with me.

Master’s Students

Ting-Yu Pan
ECE @ UMich

Kexin (Phyllis) Ju
Information @ UMich

Yuna Lee
Information @ UMich

Junnuo Wang
Music Tech @ NYU

Alumni

• Weihan Xu, MS in Computer Science @ Duke → Incoming PhD @ UW
• Erfun Ackley, MA in Performing Arts Technology @ UMich

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 Funding

Our research is partly sponsored by:

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Researchers create knowledge.
Teachers organize knowledge.
Engineers apply knowledge.
Artists challenge knowledge.

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 Selected Honors & Awards

• AAAI New Faculty Highlights AAAI

  2026
• Doctoral Award for Excellence in Research UCSD CSE Department

  2024
• Rising Stars in AI KAUST AI Initiative

  2024
• Rising Stars in Data Science UChicago DSI and UCSD HDSI

  2023
• IEEE SPS Scholarship IEEE Signal Processing Society

  2023
• Friends of International Center Fellowship UCSD GEPA

  2023
• Rising Stars in Signal Processing ICASSP

  2023
• Interdisciplinary Research Award UCSD GPSA

  2023
• Government Scholarship to Study Abroad Taiwan Ministry of Education

  2022
• J. Yang Scholarship J. Yang & Family Foundation

  2021
• ECE Department Fellowship UCSD ECE Department

  2019

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 Selected Publications

See the full list of publications here (Google Scholar).

• REGen: Multimodal Retrieval-Embedded Generation for Long-to-Short Video Editing
  Weihan Xu, Yimeng Ma, Jingyue Huang, Yang Li, Wenye Ma, Taylor Berg-Kirkpatrick, Julian McAuley, Paul Pu Liang, and Hao-Wen Dong
  Advances in Neural Information Processing Systems (NeurIPS), 2025
  paper  demo  poster  code  reviews
• Generating Symbolic Music from Natural Language Prompts using an LLM-Enhanced Dataset
  Weihan Xu, Julian McAuley, Taylor Berg-Kirkpatrick, Shlomo Dubnov, and Hao-Wen Dong
  International Society for Music Information Retrieval Conference (ISMIR), 2025
  paper  demo  poster  code  reviews

• Video-Guided Text-to-Music Generation Using Public Domain Movie Collections
  Haven Kim, Zachary Novack, Weihan Xu, Julian McAuley, and Hao-Wen Dong
  International Society for Music Information Retrieval Conference (ISMIR), 2025
  paper  demo  poster  code  reviews

• TeaserGen: Generating Teasers for Long Documentaries
  Weihan Xu, Paul Pu Liang, Haven Kim, Julian McAuley, Taylor Berg-Kirkpatrick, and Hao-Wen Dong
  International Conference on Learning Representations (ICLR), 2025
  paper  demo  poster  code  reviews

• CLIPSonic: Text-to-Audio Synthesis with Unlabeled Videos and Pretrained Language-Vision Models
  Hao-Wen Dong, Xiaoyu Liu, Jordi Pons, Gautam Bhattacharya, Santiago Pascual, Joan Serrà, Taylor Berg-Kirkpatrick, and Julian McAuley
  IEEE Workshop on Applications of Signal Processing to Audio and Acoustics (WASPAA), 2023
  paper  demo  video  slides  reviews

• Multitrack Music Transformer
  Hao-Wen Dong, Ke Chen, Shlomo Dubnov, Julian McAuley, and Taylor Berg-Kirkpatrick
  IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 2023
  paper  demo  video  slides  code  reviews

• CLIPSep: Learning Text-queried Sound Separation with Noisy Unlabeled Videos
  Hao-Wen Dong, Naoya Takahashi, Yuki Mitsufuji, Julian McAuley, and Taylor Berg-Kirkpatrick
  International Conference on Learning Representations (ICLR), 2023
  paper  demo  video  slides  poster  code  reviews

• Deep Performer: Score-to-Audio Music Performance Synthesis
  Hao-Wen Dong, Cong Zhou, Taylor Berg-Kirkpatrick, and Julian McAuley
  IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 2022
  paper  demo  video  slides  poster  reviews

• Towards Automatic Instrumentation by Learning to Separate Parts in Symbolic Multitrack Music
  Hao-Wen Dong, Chris Donahue, Taylor Berg-Kirkpatrick, and Julian McAuley
  International Society for Music Information Retrieval Conference (ISMIR), 2021
  paper  demo  video  slides  code  reviews

• MusPy: A Toolkit for Symbolic Music Generation
  Hao-Wen Dong, Ke Chen, Julian McAuley, and Taylor Berg-Kirkpatrick
  International Society for Music Information Retrieval Conference (ISMIR), 2020
  paper  video  slides  poster  code  documentation  reviews

• Convolutional Generative Adversarial Networks with Binary Neurons for Polyphonic Music Generation
  Hao-Wen Dong and Yi-Hsuan Yang
  International Society for Music Information Retrieval Conference (ISMIR), 2018
  paper  demo  video  slides  poster  code  reviews

• MuseGAN: Multi-track Sequential Generative Adversarial Networks for Symbolic Music Generation and Accompaniment
  Hao-Wen Dong,* Wen-Yi Hsiao,* Li-Chia Yang, and Yi-Hsuan Yang (*equal contribution)
  AAAI Conference on Artificial Intelligence (AAAI), 2018
  paper  demo  slides  code

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 Education

• University of California San Diego
  Ph.D. in Computer Science
  Advisors: Julian McAuley and Taylor Berg-Kirkpatrick
  Dissertation: Generative AI for Music and Audio

  Sep 2019 – Jun 2024

  
• University of California San Diego
  M.S. in Computer Science

  Sep 2019 – Jun 2021

  
• National Taiwan Normal University
  Digital Video and Audio Arts Program

  Sep 2019 – Jun 2017

  
• National Taiwan University
  B.S. in Electrical Engineering

  Sep 2013 – Jun 2017

  

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 Professional Experience

• NVIDIA
  Research Intern
  Deep Imagination Research Group, NVIDIA Research
  Advisors: Siddharth Gururani and Ming-Yu Liu
  Topic: Controllable audio generation

  Sep 2023 – Dec 2023

  
• Adobe
  Research Scientist/Engineer Intern
  Audio Research Group, Adobe Research
  Advisors: Justin Salamon and Oriol Nieto
  Topic: Text-to-audio retrieval

  May 2023 – Sep 2023

  
• Dolby
  Speech/Audio Deep Learning Intern
  Applied AI Team, Advanced Technology
  Advisor: Xiaoyu Liu
  Topic: Text-to-audio synthesis

  Jan 2023 – Apr 2023

  
• Amazon
  Applied Scientist Intern
  Natural Understanding Team, Alexa AI
  Advisors: Wenbo Zhao and Gunnar Sigurdsson
  Topic: Text-to-audio synthesis

  Sep 2022 – Jan 2023

  
• Sony
  Student Intern
  Tokyo Laboratory 30, R&D Center
  Advisor: Naoya Takahashi
  Topic: Universal sound separation

  May 2022 – Sep 2022

  
• Dolby
  Deep Learning Audio Intern
  Applied AI Team, Advanced Technology Group
  Advisor: Cong Zhou
  Topic: Music performance synthesis

  Jun 2021 – Sep 2021

  
• Yamaha
  Research Intern
  AI Group, Research and Development Division
  Advisor: Keijiro Saino
  Topic: Deep learning based synthesizer

  May 2019 – Aug 2019

  
• Academia Sinica
  Research Assistant
  Music and AI Lab, Research Center for IT Innovation
  Advisor: Yi-Hsuan Yang
  Topics: Music generation and deep generative models

  Jul 2017 – Apr 2019

  

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For more information, please see my CV.