DataXLab.org

- Faculty

Mingon Kang, Ph.D in Computer Science

Assistant Professor
Department of Computer Science
University of Nevada, Las Vegas
Website:

- Postdoc

Youngsoon Kim, Ph.D in Statistics

Research interests: Biostatistics, Bioinformatics, Big Data Analytics, and Deep Learning
Jul. 2017 - Jul. 2019


Publications:
  • Y. Kim, J. Park, J. Hao, T. Mallavarapu, and M. Kang†, "Hi-LASSO: High-dimensional LASSO", Accepted in IEEE Access
  • J. Hao, Y. Kim, T. Mallavarapu, J.H. Oh, and M. Kang, "Cox-PASNet: Pathway-based Sparse Deep Neural Network for Survival Analysis", IEEE International Conference on Bioinformatics & Biomedicine (IEEE BIBM 2018), Accepted as a regular paper (Acceptance rate: 19.6%, 105 out of 534)
  • T. Mallavarapu, J. Hao, Y. Kim, J.H. Oh, M. Kang, "PASCL: Pathway-based Sparse Deep Clustering for Identifying Unknown Cancer Subtypes", IEEE International Conference on Bioinformatics & Biomedicine (IEEE BIBM 2018), Accepted as a regular paper (Acceptance rate: 19.6%, 105 out of 534)
  • Y. Kim, J. Hao, Y. Gautam, T. Mersha, M. Kang, "DiffGRN: Differential gene regulatory network analysis", International Journal of Data Mining and Bioinformatics (IJDMB), Accepted, 2018
  • J. Hao, Y. Kim, J.H. Oh, T. Kim, M. Kang, "PASNet: Pathway-Associated Sparse Deep Neural Network for Prognosis Prediction from High-throughput Data", BMC Bioinformtics, 19:510, 2018 [Source code]
  • J. Son, E. Ko, U. Boyanapalli, D. Kim, Y. Kim, and M. Kang, "Fast and Accurate Machine Learning-based Malware Detection via RC4 Ciphertext Analysis," International Conference on Computing, Networking and Communications (ICNC 2019), Accepted - acceptance rate 28%
  • T. Mallavarapu, Y. Kim, J.H. Oh, and M. Kang, "R-PathCluster: Identifying Cancer Subtype of Glioblastoma Multiforme Using Pathway-Based Restricted Boltzmann Machine," Proceedings of IEEE International Conference on Bioinformatics & Biomedicine (IEEE BIBM 2017), International Workshop on Deep Learning in Bioinformatics, Biomedicine, and Healthcare Informatics, pp. 1183-1188, 2017

- PhD students

Jie Hao

Research interests: Machine Learning, Deep Learning, and Bioinformatics
Webpage: http://dataxlab.org/jie
Aug. 2017 - Jul. 2019, Ph.D. student in Analytics and Data Science


Publications:
  • J. Hao, M. Masum, J.H. Oh, and M. Kang†, "Gene- and Pathway-based Deep Neural Network for Multi-omics Data Integration to Predict Cancer Survival Outcomes", International Symposium on Bioinformatics Research and Applications (ISBRA), Barcelona, Spain, 3-6 June. 2019 - Regular paper (Acceptance rate: 22.6%) [Source code]
  • Y. Kim, J. Park, J. Hao, T. Mallavarapu, and M. Kang†, "Hi-LASSO: High-dimensional LASSO", Accepted in IEEE Access
  • J. Hao, Y. Kim, T. Mallavarapu, J.H. Oh, and M. Kang, "Cox-PASNet: Pathway-based Sparse Deep Neural Network for Survival Analysis", IEEE International Conference on Bioinformatics & Biomedicine (IEEE BIBM 2018), Accepted as a regular paper (Acceptance rate: 19.6%, 105 out of 534) [Source code]
  • T. Mallavarapu, J. Hao, Y. Kim, J.H. Oh, M. Kang, "PASCL: Pathway-based Sparse Deep Clustering for Identifying Unknown Cancer Subtypes", IEEE International Conference on Bioinformatics & Biomedicine (IEEE BIBM 2018), Accepted as a regular paper (Acceptance rate: 19.6%, 105 out of 534)
  • J. Hao, Y. Kim, J.H. Oh, T. Kim, M. Kang, "PASNet: Pathway-Associated Sparse Deep Neural Network for Prognosis Prediction from High-throughput Data", BMC Bioinformtics, 19:510, 2018 [Source code]
  • Y. Kim, J. Hao, Y. Gautam, T. Mersha, M. Kang, "DiffGRN: Differential gene regulatory network analysis", International Journal of Data Mining and Bioinformatics (IJDMB), Accepted, 2018

Tejaswini Mallavarapu

Research interests: Machine Learning, Deep Learning, and Bioinformatics
Aug. 2018 - Present, Ph.D. student in Analytics and Data Science, Kennesaw State University


Publications:
  • Y. Kim, J. Park, J. Hao, T. Mallavarapu, and M. Kang†, "Hi-LASSO: High-dimensional LASSO", Accepted in IEEE Access
  • T. Mallavarapu, J. Hao, Y. Kim, J.H. Oh, M. Kang, "PASCL: Pathway-based Sparse Deep Clustering for Identifying Unknown Cancer Subtypes", IEEE International Conference on Bioinformatics & Biomedicine (IEEE BIBM 2018), Accepted as a regular paper (Acceptance rate: 19.6%, 105 out of 534)
  • J. Hao, Y. Kim, T. Mallavarapu, J.H. Oh, and M. Kang, "Cox-PASNet: Pathway-based Sparse Deep Neural Network for Survival Analysis", IEEE International Conference on Bioinformatics & Biomedicine (IEEE BIBM 2018), Accepted as a regular paper (Acceptance rate: 19.6%, 105 out of 534)
  • T. Mallavarapu, Y. Kim, J.H. Oh, and M. Kang, "R-PathCluster: Identifying Cancer Subtype of Glioblastoma Multiforme Using Pathway-Based Restricted Boltzmann Machine," Proceedings of IEEE International Conference on Bioinformatics & Biomedicine (IEEE BIBM 2017), International Workshop on Deep Learning in Bioinformatics, Biomedicine, and Healthcare Informatics, pp. 1183-1188, 2017

- Master students

Sai Kosaraju (GRA)

Research interests: Document Layout Analysis, Text Mining and Machine Learning
Internship in GE Digital, Summer 2018
Jan. 2018 - Present (Expect to graduate in Jul. 2019), M.S. in Computer Science
Publications:
  • S. Kosaraju, M. Masum, N. Tsaku, P. Patel, T. Bayramoglu, G. Modgil, and M. Kang†, "DoT-Net: Document Layout Classification Using Texture-based CNN", The 15th International Conference on Document Analysis and Recognition (ICDAR), 2019. Accepted
  • S. Kosaraju, N. Z. Tsaku, P. Patel, T. Bayramoglu, G. Modgil, and M. Kang†, "Table of Contents Recognition in OCR Documents Using Image-based Machine Learning," ACM Southeast Conference (ACM-SE 2019), Accepted, Student Best Paper Award
  • M. Masum*, S. Kosaraju*, T. Bayramoglu, G. Modgil, and M. Kang†, "Automatic Knowledge Extraction from OCR Documents Using Hierarchical Document Analysis," ACM Research in Adaptive and Convergent Systems (ACM RACS 2018), Accepted (* indicates the co-first authors)

Nelson Zange Tsaku (GRA)

Research interests: Machine Learning and Computer Vision
Webpage: http://dataxlab.org/nelson
Jan. 2018 - Present (Expect to graduate in Aug. 2019), M.S. in Computer Science


Publications:
  • S. Kosaraju, M. Masum, N. Tsaku, P. Patel, T. Bayramoglu, G. Modgil, and M. Kang†, "DoT-Net: Document Layout Classification Using Texture-based CNN", The 15th International Conference on Document Analysis and Recognition (ICDAR), 2019. Accepted
  • S. Kosaraju, N. Z. Tsaku, P. Patel, T. Bayramoglu, G. Modgil, and M. Kang†, "Table of Contents Recognition in OCR Documents Using Image-based Machine Learning," ACM Southeast Conference (ACM-SE 2019), Accepted

- Visting scholars

  • Jooyoun Bae, The Institute for National Security Strategy (INSS), July 2017 - June 2018

- Research Collaborators

  • Dr. Taekyung Kim, Department of Neuroscience, University of Texas Southwestern Medical Center
  • Dr. Jung Hun Oh, Department of Medical Physics, Memorial Sloan Kettering Cancer Center
  • Dr. Tesfaye B. Mersha, Associate Professor, Cincinnati Children’s Hospital Medical Center and University of Cincinnati
  • Dr. Hyun Min Koh, Department of Pathology, Gyeongsang National University Changwon Hospital
  • Dr. Dae-Hyun Song, Department of Pathology, Gyeonsang National University School of Medicine

- Alumni

  • Tejaswini Mallavarapu (July, 2018), M.S. in Computer Science
    Pursuing her Ph.D. at Kennesaw State University
    Thesis: SPACL: Sparse Pathway-based Clustering for Cancer Subtypes
    Publication:
    • T. Mallavarapu, Y. Kim, J.H. Oh, and M. Kang, "R-PathCluster: Identifying Cancer Subtype of Glioblastoma Multiforme Using Pathway-Based Restricted Boltzmann Machine," Proceedings of IEEE International Conference on Bioinformatics & Biomedicine (IEEE BIBM 2017), International Workshop on Deep Learning in Bioinformatics, Biomedicine, and Healthcare Informatics, pp. 1183-1188, 2017
  • Euiseong Ko (July, 2018), M.S. in Computer Science (Co-advising with David Kim)
    Pursuing his Ph.D. at Georgia State University
    Thesis: Fast and Accurate Machine Learning-based Malware Detection via RC4 Ciphertext Analysis
    Publications:
    • J. Son, E. Ko, U. Boyanapalli, D. Kim, Y. Kim, M. Kang, "Fast and Accurate Machine Learning-based Malware Detection via RC4 Ciphertext Analysis", Submitted to IEEE Transactions on Information Forensics and Security (TIFS)
    • N. Zarayeneh, E. Ko, J.H. Oh, S. Suh, C. Liu, J. Gao, D. Kim, and M. Kang, "Integration of Multi-omics Data for Integrative Gene Regulatory Network Inference," International Journal of Data Mining and Bioinformatics (IJDMB), Vol.18, No.3, pp.223 - 239, 2017
    • E. Ko, M. Kang, H. Chang, and D. Kim, "Graph-theory Based Simplification Techniques for Efficient Biological Network Analysis," Proceedings of Big Data Security, A IEEE BigDataService 2017 Workshop, pp. 277-280, 2017
  • Kritika Garg (July, 2018), M.S. in Computer Science
    First job:TBA
    Project: Face/Geder/Age recognition with CNN on mobile
  • Seoyoon Park (July, 2018), Undergraduate Internship (from Hankuk University of Foreign Studies (HUFS))
    Project: Motion Detection
  • Kathelyn Zelaya (Dec, 2017), B.S. in Computer Science
    First job: Panasonic Automotive in Peachtree city, GA
  • Dhiraj Gharana (May, 2016), M.S. in Computer Science, Texas A&M University-Commerce
    Pursuing his Ph.D at University of Colorado Colorado Springs
    Thesis: Gender and Age Classification from Facial Images Using Deep Learning
    Publication:
    • D. Gharana, S. Suh, and M. Kang, "Gender classification using deep learning", Big Data and Visual Analytics, pp 55-69, 2017
  • Neda Zarayeneh (May, 2017), M.S. in Computer Science, Texas A&M University-Commerce
    Pursuing her Ph.D at Washington State University
    Thesis: Multi-Omics Based Gene Regulatory Network Inference
    Publications:
    • N. Zarayeneh, E. Ko, J.H. Oh, S. Suh, C. Liu, J. Gao, D. Kim, and M. Kang, "Integration of Multi-omics Data for Integrative Gene Regulatory Network Inference," International Journal of Data Mining and Bioinformatics (IJDMB), Vol.18, No.3, pp.223 - 239, 2017
    • N. Zarayeneh, J. H. Oh, D. Kim, C. Liu, J. Gao, S. C. Suh, and M. Kang, "Integrative Gene Regulatory Network Inference Using Multi-omics Data," Proceedings of IEEE International Conference on Bioinformatics & Biomedicine (IEEE BIBM 2016), pp. 1336-1340, 2016