Xiaoli Li, PhD

Data Analytics Department Head
Institute for Infocomm Research
1 Fusionopolis Way #21-01
Connexis Singapore 138632
Email: xlli@i2r.a-star.edu.sg
Tel: 65 64082167;
Fax: 65 67761378

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李晓黎 (Li Xiaoli)

Adjunct Associate Professor
r Citation: >10,000
r H-Index: 33
r 4 Best Paper Awards in prestigious international conferences
r 1 Best Poster Award in prestigious international conference
r 2 Best performance Awards in International Benchmark Competitions

    r  Multi-cultural, cross-disciplinary research scientist and technology executive specializing in data mining, machine learning, bioinformatics, social network analysis, information retrieval,  and artificial intelligence;

    r  Practical skills in conducting individual research & development, managing inter-disciplinary research & development teams, post-graduate lectureship and student supervision;

    r  Active in establishing professional leadership and collaboration in bioinformatics and data mining community, e.g. serve as Editor-in-Chief (IJKDB), Editorial Review Board members of international journals and also serve as organizing chairs/session chairs/workshop chairs (ICDM, AAAI, ECML/PKDD etc) and technical PC members of many premier/leading international conferences (such as KDD, IJCAI, AAAI, ICDM, SDM, WWW, CIKM, PKDD/ECML, PAKDD, ACML, ACM BCB, etc).

    r  has published more than 170 peer-reviewed papers, including top tier Data Mining, Machine Learning, Artificial Intelligence, Information Retrieval conferences, such as KDD, ICDM, SDM, PKDD/ECML, PAKDD, ICDE, ICML, IJCAI, AAAI, UAI, ACL, SIGIR, EMNLP, CIKM, UbiCom etc, as well as some top tier journals such as IEEE Transactions TKDE, IEEE Transactions on Reliability. Some impact bioinformatics papers published in PLOS Computational Biology, Bioinformatics, BMC Genomics, PLOS ONE, BMC Bioinformatics, Annals of the New York Academy of Sciences, Journal of Computational Biology, as well as some top bioinformatics conferences such as ISMB/ECCB, CSB etc.

     
    Some of his reprehensive research include  
    • Positive unlabelled based learning (more than 2000 citations)
    • Social/biological network mining (more than 1000 citations)
    • Web pages noisy information eliminating (more than 500 citations)