Hung-yi Lee

Papers

Total Citations

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About

Hung-yi Lee is a leading researcher in artificial intelligence, with a primary focus on deep learning, speech processing, and natural language understanding. His most influential work centers on advancing self-supervised learning and generative models for speech, where he has introduced innovative frameworks that enable machines to learn from unlabeled audio data with remarkable efficiency. Notably, his contributions to sequence-to-sequence learning and pre-trained models like wav2vec variants have reshaped how speech recognition systems are built, achieving state-of-the-art results with fewer labeled resources. With over 10,000 citations across his top papers, Lee’s research has had a profound impact on both academia and industry, particularly in making speech technology more accessible and robust. He is also recognized for his work on adversarial attacks and explainable AI, which has deepened understanding of model vulnerabilities. A recipient of multiple best paper awards, Lee is a sought-after speaker and mentor, known for demystifying complex topics through his popular online courses. His work continues to push the boundaries of how machines understand and generate human language.