We work on the intersection between computer science and biology. In the computational side, we develop theories and methods in the field of machine learning, algorithms, and optimization. In the biological side, we develop novel methods to tackle a wide range of open problems, from sequence analysis, to 3D structure determination, to function annotation, to biological networks, and to healthcare.

News

Location

  • Building 3, Office 4217

Courses

  • Data Analytics

Education Profile

  • 2009-2010, Lane Fellow, Lane Center for Computational Biology, Carnegie Mellon University, US
  • 2004-2009, Ph.D., Computer Science, University of Waterloo, Waterloo, Canada.
  • 2000-2004, B.Sc., Computer Science, Tsinghua University, Beijing, China.

Editorial Membership

Associate Editor:

  • Genomics, Proteomics & Bioinformatics
  • BMC Bioinformatics
  • Quantitative Biology
  • IEEE/ACM Transactions on Computational Biology and Bioinformatics

Guest Editor-in-Chief:

  • Special issues of BIOKDD2017 and BIOKDD2018 at IEEE/ACM Transactions on Computational Biology and Bioinformatics
  • Special Issue of “Deep learning in bioinformatics” at Methods (Elsevier)
  • Special Issue of "AI in Biological and Biomedical Imaging" at Frontiers in Molecular Bioscience

Xin Gao is an Associate Professor of Computer Science, Acting Associate Director of Computational Bioscience Research Center (CBRC), and Lead of the Structural and Functional Bioinformatics Group at KAUST. 

Education and early career

Prof. Gao received his bachelor degree in Computer Science from Tsinghua University, and his PH.D. degree in Computer Science from the University of Waterloo in Canada. Prior to joining KAUST, he was a Lane Fellow at the Lane Center for Computational Biology at Carnegie Mellon University, U.S.A. 

Areas of expertise and current scientific interests

Prof. Gao's group works on the intersection between computer science and biology. In the computational side, they work on developing theories and methods for deep learning, graphical models, kernel methods, matrix factorization, optimization and graph algorithms. In the biological side, they collaborate closely with experimental scientists to develop novel computational methods to solve key open problems in biology and medicine. The biological problems they are working on range from analyzing biomolecular sequences to determining their 3D structures to annotating their functions and understanding and controlling their behaviors in complex biological networks. His group also works on medicine and healthcare.

Career recognitions

Prof. Gao was selected as a KAUST DIstinguished Teaching Award Finalist in 2018. He held the Lane Fellowship at Carnegie Mellon from 2009-2010. His scientific and professional memberships include: 

  • Professional member for the Association for Computing Machinery (ACM)
  • Institute of Electrical and Electronics Engineers (IEEE)
  • American Association for the Advancement of Science (AAAS)
  • Association for the Advancement of Artificial Intelligence (AAAI)
  • International Society for Computational Biology (ISCB)
  • Life Science Society (LSS)
  • American Chemical Society (ACS)
  • Synthetic Biology Open Language (SBOL)

Editorial activities

Prof. Gao is the associate editor of Genomics, Proteomics & Bioinformatics, BMC Bioinformatics, Quantitative Biology, IEEE/ACM Transactions on Computational Biology and Bioinformatics, Current Gene Therapy, and the guest editor-in-chief of IEEE/ACM Transactions on Computational Biology and Bioinformatics, Methods, and Frontiers in Molecular Bioscience

    Why computational bioscience?

    The massive amount and complex nature of biological and clinical data generated by high-throughput technologies have posed numerous challenges and opportunities to computational scientists. I believe to overcome the challenges, we have to combine both the experimental and the computational expertise.

    Why KAUST?

    KAUST offers us, computational biologists, a unique platform to have an equal dialogue to collaborate with biologists. At KAUST, you can work on both curiosity-driven research and mission-driven research.

    KAUST CEMSE CS SFB Group Photo 2019