Henry Li

Henry Li has a robust background in machine learning and research, with experience as a Research Intern at ByteDance and the Simons Foundation. At ByteDance, Henry contributed to the Seed Foundation Model Team by developing novel techniques for cross-modal applications. At the Simons Foundation, Henry developed a technique for low-photon nanoscale phase retrieval using deep neural network priors, which was published at MSML 2021. Additional experience includes working at Bosch Center for Artificial Intelligence, where research focused on robust training-free approaches and image-to-image translation. Henry currently holds a Research Scientist position at Google, following an internship at Google DeepMind, where efforts were directed towards audio diffusion models for unsupervised source separation. Henry Li holds a Bachelor of Science in Computer Science and Mathematics from Yale University.