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Mahbubul is an Associate Professor of Statistics with an interest in the emerging field of data science. His research interests include Exploratory Data Analysis, Data Visualization and Visual Inference, Statistical Modeling, and Data Science.

Mahbubul Majumder

Mahbubul Majumder

Data Science
  • Ph.D., Statistics, Iowa State University, 2013
  • M.A., Statistics, Ball State University, 2007
  • M.S., Statistics, University of Dhaka, 1998
  • B.S., Statistics, University of Dhaka, 1996

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SUMMARY OF ACTIVITIES/INTERESTS

  • Exploratory Data Analysis
  • Data Visualization and Visual Inference
  • Statistical Modeling
  • Data Science

Academic and Professional Experience

Mahbubul is an Associate Professor of Statistics with an interest in the emerging field of data science. He received his Ph.D. in Statistics from Iowa State University in 2013 and his M.A. in Statistics from Ball State University. Mahbubul started his academic career at the University of Nebraska at Omaha in 2013 as an Assistant Professor of Data Science. He teaches data science courses at UNO where he also developed the data science program.

His research interests include techniques of effective data visualization, statistical inference using graphics, exploratory data analysis, and statistical and machine learning tools/models to convert data into data products. His publication on visual statistical inference appears on the Journal of American Statistical Association as a featured article.

Mahbubul has about 20 years of experience in working with real data and data related technologies. He worked for various companies such as Travelers Insurance, Hy-Vee, and Novartis Pharmaceutical Company. He supervised at least 16 students on data analysis and visualization projects with local industries such as Union Pacific Railroad, TD Ameritrade, Methodist Health System, Omaha Public Power District, First National Bank, Claas of America, etc. Mahbubul helps local industries develop in-house data science teams and provides essential training in the field. He recently organized workshops for the First National Bank and U.S. Strategic Command. He is currently working to develop an intensive training program for Kiewit Corporation. He also has worked on multiple projects funded by the Nebraska Applied Research Institute.

PUBLICATIONS

  • Williams, T., Cheng, X., Majumder, M., Hastings, M., Hongwook, S., Dash, K., Yeo, J. (2020) Collaborative Big Data Review for Educational Impact. School Community Journal. (Accepted)
  • Craig Maher, Majumder, M., Wei-Jie Liao, Yansi Liao (2019), Spatial Analysis of Local Government Fiscal Condition in Nebraska, Socialiniai Tyrimai / Social Research. Vol. 42 (1), Pp 19-31.
  • Haitham M. Yousof, Majumder, M., S. M. A. Jahanshahi, M. Masoom Ali and G.G. Hamedani (2018), a New Weibull Class of Distributions: Theory, Characterizations and Applications, Journal of Statistical Research of Iran (JSRI), Vol 15, Pp 1-39.
  • Roy-Chowdhury,N.,Cook,D.,Hofmann,H.,Majumder,M.(2017) Measuring Lineup Difficulty by Matching Distance Metrics With Subject Choices in Crowd-Sourced Data, Journal of Computational and Graphical Statistics. Pp 1-14. Doi: 10.1080/10618600.2017.1356323.
  • Majumder, M., Cheng, X.(2017), Focusing on the Needs: Experiences of Developing a Data Science Program, Journal of Computational and Graphical Statistics. Vol. 26(4), Pp 779-780, Doi: 10.1080/10618600.2017.1385475
  • Luna,F.,Cheng,X.,Majumder,M.(2016) Interactive Visualization of Latino Political Participation in Nebraska and USA. in JSM Proceedings, Section of Statistical Graphics. Alexandria, VA: American Statistical Association. Pp 3698-3709.
  • Puniya, B., Allen, L., Hochfelder, C., Majumder, M., Helikar, T. (2016) Systems Perturbation Analysis of a Large Scale Signal Transduction Model Reveals Potentially Influential Candidates for Cancer Therapeutics, Frontiers in Bioengineering and Biotechnology, Section Bioinformatics and Computational Biology. Vol. 4, Pp 10. Doi: 10.3389/Fbioe.2016.00010
  • Cook, D., Lee, E. and Majumder, M. (2016) Data Visualization and Statistical Graphics in Big Data Analysis, Annual Review of Statistics and Its Application 3:133-59
  • Pandey, P., Pasternack, G., Majumder, M., Soupir, M., Kaiser, M. (2015) a Neighborhood Statistics Model for Predicting Stream Pathogen Indicator Levels. Environmental Monitoring and Assessment. Vol. 187(3), Pp 124.
  • Roy-Chowdhury N., Cook D., Hofmann H., Majumder M., Lee E., Toth A.(2014) Using Visual Statistical Inference to Better Understand Random Class Separations in High Dimension, Low Sample Size Data, Computational Statistics, ISSN 0943-4062 Pp 1-24. Doi: 10.1007/S00180-014-0534-X.
  • Majumder, M., Hofmann, H., Cook, D. (2013), Validation of Visual Statistical Inference, Applied to Linear Models, Journal of the American Statistical Association , Vol. 108(503) Pp 942-956."
  • Atwood, S. E., O'Rourke, J. A., Peier, G. A., Yin, T., Majumder, M., Zhang, C., Cianzio, S., Hill, J. H., Cook, D., Whitham, S. A., Shoemaker, R. C. and Graham, M. A. (2013), Replication Protein a Subunit 3 and the Iron Efficiency Response in Soybean, Plant Cell and Environment, Vol. 37(1), Pp 213-234.
  • Yin, T., Majumder, M., Roy Chowdhury, N., Cook, D., Shoemaker, R. and Graham, M. (2013), Visual Mining Methods for RNA-Seq Data: Data Structure, Dispersion Estimation and Significance Testing, Journal of Data Mining in Genomics & Proteomics, Vol. 4(4):139. Doi: 10.4172/2153- 0602.1000139.
  • Zhao, Y., Cook, D., Hofmann, H., Majumder, M., and Roy-Chowdhury, N. (2013), Mind Reading: Using an Eye-Tracker to See How People Are Looking at Lineups, International Journal of Intelligent Technologies and Ap- Plied Statistics, Vol. 6(4) Pp 393-413.
  • Hofmann H., Follet L., Majumder M., Cook D. (2012), Graphical Tests for Power Comparison of Competing Designs, IEEE Transactions on Visualization and Computer Graphics, Vol. 18(12), Pp 2441-2448.
  • Roy-Chowdhury N., Cook D., Hofmann H. , and M. Majumder (2011), Visual Statistical Inference for Large P, Small N Data, in JSM Proceedings, Section of Statistical Graphics. Alexandria, VA: American Statistical Association, Pp 4436-4446.
  • Majumder, M., Ali M. M. (2008), a Comparison of Methods of Estimation of Parameters of Tukey’s Gh Family of Distributions, Pakistan J. Statistics, Vol. 24(2) Pp 135-144.
  • Jay, B. and Heinz, A., Majumder, M. (2007), an Algorithm for Graceful Labeling of Cycle, Congressus Numerantium 186 (2007) Pp. 57-63.
  • Jay,B.andHeinz,A.,Majumder,M.(2007),Properties of Graceful Labeling of Cycle,Congressus Numerantium 188 (2007) Pp. 109-115.
  • Majumder, M., Haque, A., Kaykobad, M. (2000), Graceful Labelling of Complete Binary Trees, Proceedings of International Conference on Computer and Information Technology, ICCIT 2000.