Sang-gil Lee

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I am a research scientist at NVIDIA. I work on deep generative models for sequences, with a particular focus on speech and audio.

I received my Ph.D. from the Data Science & AI Lab (DSAIL) at Seoul National University. During my Ph.D., I served as a research intern at NVIDIA, under the advisement of Wei Ping and Boris Ginsburg. Prior to that, I completed internships at Microsoft Research Asia, where I was advised by Xu Tan, Tao Qin (speech), and Bin Shao (bioinformatics). I received my B.S. in Electrical and Computer Engineering from Seoul National University.

• [Jan 2026] UALM and Music Flamingo are accepted at ICLR 2026.
• [Jan 2026] PersonaPlex is accepted at ICASSP 2026.
• [Jan 2026] Since its release, BigVGAN-v2 has accumulated approximately 25M total downloads and is one of the most downloaded models on NVIDIA Hugging Face.
• [Jan 2026] Universal Music Group announces collaboration with NVIDIA to pioneer responsible AI for music discovery, creation, and engagement, powered by Music Flamingo.

My research interest spans a wide range of deep generative models (AR, flow, GAN, diffusion, etc.) applied to sequential data. Specifically, I am working on building multi-modal large language models with a focus on audio.

During my Ph.D., I focused on time-domain waveform data (speech and audio) to advance generative modeling for audio.

I am also broadly interested in speech and audio applications, including text-to-speech, voice conversion, music generation, neural audio codecs, and audio language models.

Representative papers are highlighted.

During my time at DSAIL, I collaborated with Seoul National University Hospital on a computer-aided diagnosis project for liver cancer. The project yielded a high-performance medical object detection model to help reduce human errors from radiologists for the early detection of liver disease.

Last update: Jan 2026. Template borrowed from here.