Big Data Research

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Big Data Research

Dr. Rama VenkatDean, College of EngineeringPhone: (702) 895-1094Email: Rama.Venkat@unlv.edu

Dr. Mohamed TrabiaAssociate Dean, College of EngineeringPhone: (702) 895-0957Email: Mohamed.Trabia@unlv.edu

For more than a decade, UNLV researchers have been conducting a world-class effort in various aspects of big data research. This program has been funded by federal and state agencies, as well as many industrial partners. Our researchers have addressed questions related to many fields, including big data, relative to national security and health issues.

We would like to introduce you to some of our researchers. Please feel free to contact us if we can help with future collaboration.

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Photo on Slide 1: Example of 3D view of buildings and land cover with light screen artistic effect

Big DataResearch Areas of Expertise

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• Big data analysis• Cloud computing• Deep learning• Document analysis• Enhance quality of healthcare without increasing costs • Expansion of public transparency• Healthcare informatics• Human-computer interaction• Information privacy and security• Information retrieval• Machine learning• Text mining• UAS and GIS integration

Why UNLV?

• UNLV is a leader among the state’s public entities dedicated to advancing big data research in the region and beyond.

• UNLV is located centrally in the west and houses the high-performance computing center, also known as The National Supercomputing Institute.

• Additional data center space is located at Switch Communications, which houses the Intel Supercomputer “Cherry Creek” (26,000 cores) cluster. The site provides Federal and State researchers with state-of-the-art, highly-secure cloud computing.

Big Data Research

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Why UNLV?

• UNLV’s outstanding achievements in big data research, its success in forging public/private partnerships, and its excellent academic programs place the university at the forefront of the field.

• UNLV has acquired over $6 million in research funding in the last two years on wide-ranging subjects in the big data area, including:

— Big data analytics;— Network science;— Healthcare informatics;— Social computing; and — Information privacy and security.

Big Data Research

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Faculty Involved in Big Data Research

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Dr. Mingon KangAssistant Professor, Department of Computer Science

Dr. Fatma NasozAssociate Professor, Department of Computer ScienceAffiliate Faculty, Nevada Institute of Personalized Medicine (NIPM)

Dr. Haroon StephenAssociate Professor, Department of Civil and Environmental Engineering and Construction

Dr. Kazem TaghvaProfessor and Chair, Department of Computer Science

Big Data Research

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Additional Resourceswww.NevadaCompareCare.Net

Big Data

Research Highlights

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Big Data Research

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Dr. Mingon Kang

Assistant Professor, Department of Computer Science

Director, DataX Lab (http://www.dataxlab.org)

Phone: (702) 895-4884

Email: mingon.kang@unlv.edu

• Expertise• Data science, machine learning, big data

analytics• Deep learning:

• Interpretable deep learning• Integrative deep learning

• Integrative analysis of multiple types of data such as image and text data

• Medical image analysis• Document layout analysis

Big Data Research

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Dr. Mingon KangAssistant Professor, Department of Computer ScienceRelevant Publications

• WenJia Feng, ZekunYu, Mingon Kang, Haijun Gong, Tae-Hyuk Ahn, “Practical Evaluation of Different Omics Data Integration Methods,”. Precision Health and Medicine 2020: 193-19.

• J. Hao, S. Kosaraju, N. Tsaku, D. H. Song, and M. Kang, "PAGE-Net: Interpretable and Integrative Deep Learning for Survival Analysis Using Histopathological Images and Genomic Data", Pacific Symposium on Biocomputing (PSB), 2019

• T. Mallavarapu, J. Hao, Y. Kim, J.H. Oh, M. Kang, "Pathway-based Deep Clustering for Molecular Subtyping of Cancer," Methods, 2019

• S. Kosaraju, M. Masum, N. Tsaku, P. Patel, T. Bayramoglu, G. Modgil, and M. Kang, "DoT-Net: Document Layout Classification Using Texture-based CNN," International Conference on Document Analysis and Recognition (ICDAR), 2019

• Y. Kim, J. Hao, T. Mallavarapu, J. Park, and M. Kang, "Hi-LASSO: High-Dimensional LASSO," IEEE Access, 2019, pp 44562-44573, 2019

• 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," 2019 International Symposium on Bioinformatics Research and Applications (ISBRA), 2019

• J. Hao, Y. Kim, T. Mallavarapu, J.H. Oh, and M. Kang, "Cox-PASNet: Pathway-based Sparse Deep Neural Network for Survival Analysis", Proceedings of IEEE International Conference on Bioinformatics & Biomedicine (IEEE BIBM), 2018

• J. Hao, Y. Kim, J.H. Oh, T. Kim, and M. Kang, "PASNet: Pathway-Associated Sparse Deep Neural Network for Prognosis Prediction from High-throughput Data", BMC Bioinformatics, 19:510, 2018

• M. Kang, J. Park, D. Kim, A. Biswas, C. Liu, and J. Gao, "An Integrative Genomic Study for Multimodal Genomic Data Using Multi-Block Bipartite Graph," IEEE/ACM Transactions on Computational Biology and Bioinformatics (IEEE TCBB), vol.PP, no.99, pp.1-1, 2016

• M. Kang, L. Tang, and J. Gao, "Computational modeling of animal foreign body responses to implanted biomaterials", BMC Bioinformatics, 17:111, 2016

Big Data Research

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Dr. Fatma Nasoz

Associate Professor, Department of Computer Science

Affiliate Faculty, Nevada Institute of Personalized Medicine (NIPM)

Phone: (702) 895-0097

Email: fatma.nasoz@unlv.edu

• Expertise• Human computer interaction• Affectively intelligent systems• Machine learning • Deep learning• Cloud computing

Big Data Research

Relevant Publications• Wu Q, Nasoz F, Jung JY, Bhattarai B. “Comparison of Machine Learning Approaches in Prediction of

Bone Mineral Density in Elderly Men.” MedRxiv. January 24, 2020. • Wu Q, Nasoz F, Jung JY, Bhattarai B, Han MV. ”Machine learning approaches for fracture risk

assessment: a comparative analysis of genomic and phenotypic data in 5,130 older men.” MedRxiv. January 13, 2020.

• Shrestha, N. and Nasoz, F. (2019). “Deep Learning Sentiment Analysis of Amazon.com Reviews and Ratings.” International Journal on Soft Computing, Artificial Intelligence, and Applications, Vol. 8(1): 1-15.

• Nasoz F. and Shrestha C. (2017) “A Web-Based User Interface for Machine Learning Analysis”. In: Yamamoto S. (eds) Human Interface and the Management of Information: Supporting Learning, Decision-Making and Collaboration. HIMI 2017. Lecture Notes in Computer Science, vol. 10274. Springer, Cham.

• Nasoz, F., Bryce R.C., Palmer, J.P., and Rugg, D.J. “A User-Centric Metadata Creation Tool for Preserving the Nation’s Ecological Data.” In M.J. Smith and G. Solvency (Eds.): Human Interface and the Management of Information - Interacting with Information, LNCS 6771, (pp. 122–131), Springer-Verlag Berlin Heidelberg (2011).

• Nasoz, F., Lisetti, C.L., and T. Vasilakos. Affectively Intelligent and Adaptive Car Interfaces. Information Sciences, Vol. 180(20): 3817-3836 (2010).

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Dr. Fatma NasozAssociate Professor, Department of Computer ScienceAffiliate Faculty, Nevada Institute of Personalized Medicine (NIPM)

Big Data Research

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Dr. Haroon StephenAssociate Professor, Department of Civil and Environmental Engineering

Director, GIS and Remote Sensing Core Lab

Phone: (702) 774-1463

Email: haroon.stephen@unlv.edu

• Research Interests• Sensors aboard UAS for remote sensing: testing

and applications• UAS and GIS integration• Applications of UAS to resource mapping,

monitoring, and management• Data fusion and data mining of multi-scale sources:

UAS, aerial, and spaceborne sensors

Example of 3D view of buildings and land cover

Big Data Research

Relevant Publications

• Abedin, S. J. H., & Stephen, H. (2019). GIS Framework for Spatiotemporal Mapping of Urban Flooding. Geosciences, 9(2), 77.

• Stephen, H. (2018). Trend Analysis of Las Vegas Land Cover and Temperature Using Remote Sensing. Land, 7(4), 135.

• Lachniet, M. S., Lawson, D. E., Stephen, H., Sloat, A. R., & Patterson, W. P. (2016). Isoscapes of δ18O and δ2H reveal climatic forcings on Alaska and Yukon precipitation. Water Resources Research, 52(8), 6575-6586.

• Black, A., & Stephen, H. (2014). Relating temperature trends to the normalized difference vegetation index in Las Vegas. GIScience & Remote Sensing, 51(4), 468-482. doi: 10.1080/15481603.2014.940695.

• Acharya, A., Piechota, T. C., Stephen, H., & Tootle, G. (2011). Modeled streamflow response under cloud seeding in the North Platte River watershed. Journal of Hydrology, 409(1), 305-314, doi:10.1016/j.jhydrol.2011.08.027.

• Puri, S., Stephen, H., & Ahmad, S. (2011). Relating TRMM Precipitation Radar backscatter to water stage in wetlands. Journal of Hydrology, 401(3), 240-249, doi:10.1016/j.jhydrol.2011.02.026.

• Ahmad, S., Kalra, A., & Stephen, H. (2010). Estimating soil moisture using remote sensing data: A machine learning approach. Advances in Water Resources, 33(1), 69-80, doi:10.1016/j.advwatres.2009.10.008.

Dr. Haroon StephenAssociate Professor, Department of Civil and Environmental Engineering and Construction

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Dr. Kazem Taghva

Chair and Professor, Department of Computer Science

Phone: (702) 895-0873

Email: kazem.taghva@unlv.edu

• Research Interests• Databases• Machine learning• Information retrieval

Big Data Research

Relevant Publications

• Fonseca Cacho J.R., Taghva K. Reproducible Research in Document Analysis and Recognition. Advances in Intelligent Systems and Computing, vol. 738, pp. 389-395, 2018, Springer.

• Kazem Taghva, Name identification and extraction with formal concept analysis. International Journal of Machine Learning and Cybernetics, 2017, Volume 8, Number 1, Page 171.

• Taghva, K. & Gilbreth, Recognizing acronyms and their definitions, International Journal on Document Analysis and Recognition, May 1999, Volume 1, Issue 4, pp 191–198.

• Taghva, Kazem. "Name identification and extraction with formal concept analysis." International Journal of Machine Learning and Cybernetics, 8.1 (2017): 171-178.

• Bozorgi, Mandana, Taghva, Kazem, and Singh, Ashok, "Cancer Survivability with Logistic Regression", Proceedings of Computing Conference 2017, 416- 420.

• Kazem Taghva, Contextual Analysis for Middle Eastern Languages with Hidden Markov Models, International Journal on Natural Language Computing, vol. 4, No. 4, August 2015, pp. 1-11.

• Kazem Taghva, Shivam Agarwal, Utilizing web data in identification and correction of OCR errors. Document Recognition and Retrieval 2014: 902109-902109-6.

• K. Taghva, J. Borsack, A. Condit, Evaluation of model-based retrieval effectiveness with OCR text, ACM Transactions on Information Systems (TOIS), 1996, pp. 64-93, vol. 14(1).

Dr. Kazem TaghvaChair and Professor, Department of Computer Engineering

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