Mingon KANG, Ph.D

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Dr. Kang is an assistant professor in the Department of Computer Science at The University of Nevada, Las Vegas since Fall 2019. Prior to UNLV, he was at Kennesaw State University and Texas A&M University-Commerce. His research interests include Bioinformatics, Machine Learning, Data Mining, Computer Vision, and Big Data Analytics. He received his M.S. and Ph.D. degree in the Department of Computer Science at the University of Texas at Arlington in 2010 and 2015 respectively.

Office: SEB-3214, 4505 S. Maryland Pkwy. Las Vegas, NV 89154-4022
Email: mingon.kang@unlv.edu
Tel: 702-895-4884

"Not only so, but we also rejoice in our sufferings, because we know that suffering produces perseverance; perseverance, character; and character, hope. And hope does not disappoint us, because God has poured out his love into our hearts by the Holy Spirit, whom he has given us."


DataX Lab: DataXlab.org

BIBM Workshops: Deep Learning in Bioinformatics , Biological Network Analysis


Recent News

  • Dec. 23, 2019

    "Interpretable deep neural network for cancer survival analysis by integrating genomic and clinical data" is published in BMC Medical Genomics

  • Dec. 18, 2019

    A book chapter "Integration of Multi-omics Data for Expression Quantitative Trait Loci (eQTL) Analysis and eQTL Epistasis" is published in eQTL Analysis Methods in Molecular Biology

  • Oct. 7, 2019

    "Semi-supervised Discriminative Transfer Learning in Cross-language Text Classification" is accepted in IEEE ICMLA 2019

  • Oct. 1, 2019

    "Texture-based Deep Learning for Effective Histopathological Cancer Image Classification" is accepted as a short paper (acceptance rate of 36.4%) in BIBM 2019

  • Sep. 16, 2019

    "PAGE-Net: Interpretable and Integrative Deep Learning for Survival Analysis Using Histopathological Images and Genomic Data" is accepted in PSB 2020, and invited for an oral presentation. See you in Hawaii at Janaury 3-7, 2020.

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Upcoming Conferences

Bioinformatics: ISMB (Jan), PSB (Aug), RECOMB (Oct), BIBM (Jul), BCB (May), APBC (Jul), GIW (Jun), ISBRA (Feb)
Machine Learning: ICML (Feb), ICDM(Jun), KDD (Feb), NIPS (Apr), ICDE (Aug), SDM (Oct), IJCAI (Jan), AAAI (Jan) ICLR (Sep)