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Martin Renqiang Min |
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Martin Renqiang Min |
Martin Renqiang Min received his MSc and PhD degrees in Computer Science from Machine Learning Group, Department of Computer Science, University of Toronto. During his Master study, he trained regularized deep autoencoders under the supervision of Nobel Prize laureate Prof. Geoffrey Hinton. He did a one-year postdoc advised by Prof. Mark Gerstein at Yale University. In May 2011, he accepted a tenure-track faculty position from Department of Computer Science and Engineering, Hong Kong University of Science and Technology. Due to family reasons, he immigrated to the New York metropolitan area. He works in the areas of deep learning and biomedicine, focusing on representation learning, generative models, multimodal reasoning, generative biomedicine, and omics for personalized healthcare. He contributed to the ENCODE Project, and his text-to-video generation paper published in 2018 was reported by Science, MIT Technology Review, and many international news media. He was a co-chair of NIPS Workshop on Machine Learning in Computational Biology in 2014. He taught Topics in Deep Learning: Methods and Biomedical Applications at Department of Statistics and Data Science, Yale University in Spring 2020, and has given several guest lectures on Deep Learning at Yale University each year since then. Since 2022, he has been heading the Machine Learning Department, NEC Laboratories America founded by Vladimir Vapnik, Yann LeCun, and Hans Peter Graf, which is located in Princeton, NJ, USA.