About Yu Li Yu Li Ph.D. Student, Computer Science machine learning Deep learning bioinformatics Research Interests Yu Li is a Ph.D. student at KAUST, majoring in Computer Science. His advisor is Prof. Xin Gao. His research interests are Deep Learning, Machine Learning, and Bioinformatics. He got his Master degree in Computer Science at KAUST in December 2016. Before that, he got his Bachelor degree in Biosciences at the University of Science and Technology of China (USTC). Scientific and Professional Membership Association for the Advancement of Artificial Intelligence(AAAI) Member International Society for Computational Biology (ISCB) Member Education Profile Ph.D student, Computer Articles Related News November 2020 Talented KAUST Ph.D. student aims to revolutionize healthcare with machine learning 3 min read · Tue, Nov 10 2020 News machine learning healthcare bioinformatics KAUST Ph.D. student, Yu Li, is a talented young computer scientist with an interest in developing novel computational methods and algorithms to solve and understand the principles behind the “bio-world.” October 2019 AI reveals nature of RNA-protein interactions 1 min read · Wed, Oct 30 2019 News artificial intelligence Computer science A deep learning tool could help in structure-based drug discovery. July 2017 Our work on developing deep learning method for binding affinity prediction accepted by Bioinformatics (Sequence2Vec) 1 min read · Mon, Jul 24 2017 News Deep learning Congratulates Ramzan, Hiro, and Yu on their work on developing a novel method that combines the strength of probabilistic graphical models, Hilbert space embedding, and deep learning to model binding affinity of transcription factors which was accepted by Bioinformatics.
Talented KAUST Ph.D. student aims to revolutionize healthcare with machine learning 3 min read · Tue, Nov 10 2020 News machine learning healthcare bioinformatics KAUST Ph.D. student, Yu Li, is a talented young computer scientist with an interest in developing novel computational methods and algorithms to solve and understand the principles behind the “bio-world.”
AI reveals nature of RNA-protein interactions 1 min read · Wed, Oct 30 2019 News artificial intelligence Computer science A deep learning tool could help in structure-based drug discovery.
Our work on developing deep learning method for binding affinity prediction accepted by Bioinformatics (Sequence2Vec) 1 min read · Mon, Jul 24 2017 News Deep learning Congratulates Ramzan, Hiro, and Yu on their work on developing a novel method that combines the strength of probabilistic graphical models, Hilbert space embedding, and deep learning to model binding affinity of transcription factors which was accepted by Bioinformatics.
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