papers + projects

so many fun projects, so little time

A collection of research papers, many of which were done primarily by undergraduate collaborators.

From Lu and Hardin, 2021.

Link to Jo Hardin’s CV

(links to preprints, arXiv, or journal website)

• Indicates work done as an undergraduate student.

2023

• Cruz, M., Wei, A., Hardin, J., Radunskaya, A. Long-term Averages of the Stochastic Logistic Map, submitted, 2023.
• Adams, J., Hoang, J., Petroni, E., Ashby, E., Hardin, J., Stoebel, D. The timing of transcription of RpoS-dependent genes varies across multiple stresses, submitted, 2023.
• Ashby, E., Havens, J., Hardin, J., Schulz, D. Chemical inhibition of bromodomain proteins in insect stage African trypanosomes perturbs silencing of the Variant Surface Glycoprotein repertoire and results in widespread changes in the transcriptome, Microbiology Spectrum, accepted, 2023.
• Hardin, J., Shahriari, S. Community, Collaboration, and Climate, PRIMUS: Problems, Resources, and Issues in Mathematics Undergraduate Studies, 33(5), 2023.

2022

• Çetinkaya-Rundel, M., Hardin, J., Baumer, B., McNamara, A., Horton, N., Rundel, C. An Educator’s Perspective on the Tidyverse, Technology Innovations in Statistics Education, 14 2022.
• Ashby, E., Paddock, L., Rollosson, L., Tang, E., Miller, G., Wade, S., Betts, H., Porter, A., *Saada, C., Hardin, J., Schulz, D. Genomic occupancy of the bromodomain protein Bdf3 is dynamic during differentiation of African trypanosomes from bloodstream to procyclic forms, mSphere, 7 (3), 2022.

2021

• Çetinkaya-Rundel, M., Hardin, J. Introduction to Modern Statistics OpenIntro, 2021.
• Kim, A.Y., Hardin, J. “Playing the whole game”: A data collection and analysis exercise with Google Calendar, Journal of Statistics and Data Science Education, 29(S1), 2021.
• *Lu, B., Hardin, J. A Unified Framework for Random Forest Prediction Error Estimation, Journal of Machine Learning Research, 22(8), 2021.
  • Prediction intervals for random forests with applications to high throughput data (Computational Genomics Summer Institute @ IPAM, 2017)
• Hardin, J. 9 out of 10 Seniors Recommend this First-Year Seminar: Statistics in the World, In Mathematical Themes in a First-Year Seminar, eds. J. Schaefer, J. Bowen, M. Kozek, and P. Pierce,  MAA Notes Series; 2021.

2020

• Hardin, J., Haushalter, K., Yong, D.Turning STEM Education Inside-Out: Teaching and Learning Inside of Prisons, Science Education and Civic Engagement: An International Journal, 12 (2); 2020.
• Allison, K., Hallman, M., *Koskelo E., Radunskaya, A., Hardin, J., Hudgings, J. Increasing the speed of CCD-based thermoreflectance imaging, Review of Scientific Instruments, 91: 044901, 2020. https://doi.org/10.1063/1.5135922.
• Baumer, B., Bray, A., Çetinkaya-Rundel, M., and Hardin, J.  Teaching Introductory Statistics with DataCamp, Journal of Statistics Education, 28(1); 89-97, 2020.

2019

• Fiksel, J., Jager, L., Hardin, J., Taub, M. Using GitHub Classroom To Teach Statistics, Journal of Statistics Education, 27(2): 110-119, 2019.
• Duron, C., Pan, Y., D. Gutmann, Hardin, J., Radunskaya, A.Variability of Betweenness Centrality and Its Effect on Identifying Essential Genes, Bulletin of Mathematical Biology, 81(9): 3655-3673, 2019. (also here)

2018

• Horton, N., Hardin, J. Challenges and Opportunities for Statistics and Data Science Undergraduate Major and Minor Degree Programs. Proceedings of the Tenth International Conference on Teaching Statistics, 2018.
• *Evans, C., Hardin, J., Stoebel, D. Selecting between-sample RNA-Seq normalization methods from the perspective of their assumptions. Briefings in Bioinformatics, 19(5): 776–792, 2018.  (paper @ arxiv.org)
  • Tutorial on RNASeq Normalization and Differential Expression (Computational Genomics Summer Institute @ IPAM, 2016)
  • Assumptions in Normalizing RNASeq Data (Computational Genomics Summer Institute @ IPAM, 2016)
• Hardin, J. Fun, Not Competition: The Story of My Math Club, Journal of Humanistic Mathematics, 8(1): 350-358, 2018.
• Pan, Y., Duron, C., Bush, E., Sims, P., Hardin, J., Radunskaya, A., Gutmann, D. Graph Complexity Analysis Identifies an ETV5 Tumor-Specific Network in Human and Murine Low-Grade Glioma, PLoS ONE, 13(5): e0190001, 2018. 
• Hardin, J.  Dynamic Data in the Statistics Classroom. Technological Innovations in Statistics Education, 11(1), 2018.  (paper @ arxiv.org, full worked-out examples)
  • Dynamic Data in the Classroom (eCOTS 2016)
  • Dynamic Data in the Statistics Classroom (useR 2016)

2017

• Wong, G., Bonocora, R., Schep, A., Beeler, S., Lee, A., Shull, L., Batachari, L., Dillon, M., Evans, C., Becker, C., Bush, E., Hardin, J., Wade, J., Stoebel, D. The genome-wide transcriptional response to varying RpoS levels in Escherichia coli K-12. Journal of Bacteriology, 199:e00755-16, 2017.   (paper @ biorxiv.org)
• Hardin, J., Kloke, J. “Statistical Analyses” in Current Protocols in Molecular Biology, Appendix 4A, John Wiley & Sons, 2017.
• Evans, C., Hardin, J., Huber, M., Stoebel, D., Wong, G. Differential expression analysis for multiple conditions, unpublished, 2017. (paper @ arxiv.org)

2016

• Coleman, J., Replogle, J., Chandler, G., Hardin, J. Resistant Multiple Sparse Canonical Correlation. Statistical Applications in Genetics and Molecular Biology;15 (2): 123-38, 2016. (paper @ arxiv.org)

2015

• Hardin, J., Hoerl, R., Horton, N.J., Nolan, D. Data Science in Statistics Curricula: Preparing Students to ‘Think with Data’. The American Statistician, 69(4):343-353, 2015. (paper @ arxiv.org)
• Hardin, J., Sarkis, G., *URC, P.C. Network Analysis with the Enron Email Corpus. Journal of Statistics Education, 23(2), 2015. (P.C. URC stands for the Pomona College Undergraduate Research Circle whose members for this project were Timothy Kaye, David Khatami, Daniel Metz, and Emily Proulx.) (paper @ arxiv.org)

2013

• Hardin, J., Garcia, S.R., Golan, D. A method for generating realistic correlation matrices Annals of Applied Statistics, 7: 1733-1762, 2013. (paper @ arxiv.org)

2012

• *Brieger, K, J. Hardin. Medicine and Statistics: the inextricable link Chance, 25: 31-34, 2012.
• *Head, A., Hardin, J., Adolph, S. New methods for estimating maximum performance and the correlation of sample measures, Environmental and Ecological Statistics, 19: 127-137, 2012.

2011

• Karnovsky, N.J., *Brown, Z.W., Welcker, J., Harding, A.M.A., Walkusz, W., Cavalcanti, A., Hardin, J., Kitaysky, A., Gabrielsen, G., Grémillet, D. Inter-colony comparison of diving behavior of an Arctic top predator: implications for warming in the Greenland Sea, Marine Ecology Progress Series, 440: 229-240, 2011.  DOI: 10.3354/meps0935.
• Grosfils, E.B., Long, S.M., Venechuk, E.M.,* Hurwitz, D.M., Richards, J.W., Kastl, Brian, *Drury, D.E., Hardin, J., 2011, Geologic map of the Ganiki Planitia quadrangle (V-14), Venus: U.S. Geological Survey Scientific Investigations Map 3121.Now available at USGS as an interactive map.

2010

• *Richards, J., Hardin, J., Grosfils, E. Weighted Model-Based Clustering for Remote Sensing Image Analysis, Computational Geosciences, 14: 125-136, 2010.

 

2009

• Hardin, J., Wilson, J. A note on oligonucleotide expression values not being normally distributed, Biostatistics, 10: 446-450, 2009. (full manuscript at Supplementary Material to A note onoligonucleotide expression values not being normally distributed)

 

2008

• Yiu, G., McCord, A., Wise, A.,  Jindal, R., Hardee, J., Kuo, A., *Yuen Shimogawa, M., Cahoon, L., Wu, M., Kloke, J., Hardin, J., Mays Hoopes, L.L.; Pathways Change in Expression During Replicative Aging in Saccharomyces cerevisiae, Journal of Gerontology, 63A: 21-34, 2008.

 

2007

• Hardin, J., Mitani, A., Hicks, L., *VanKoten, B.; A Robust Measure of Correlation Between Two Genes on a Microarray, BMC Bioinformatics, 8:220, 2007. (R code: biwt.r)
• Adolph, S., Hardin, J.; Estimating Phenotypic Correlations: Correcting for Bias Due to Intraindividual Variability, Functional Ecology, 21: 178-184, 2007.

 

2006

• *Wise, A., Hardin, J., Hoopes, L.; Yeast Through the Ages: a statistical analysis of genetic changes in aging yeast, Chance, 19, 39-44, 2006.
• Hardin, J., Hoopes, L., *Murphy, R.; Analyzing DNA Microarrays with Undergraduate Statisticians, Proceedings of the Seventh International Conference on Teaching Statistics, 2006.

 

2005

• Altman, N., Banks, D., Hardwick, J., Roeder, K., Craigmile, P., Hardin, J., Gupta, M.  The IMS New Researchers’ Survival Guide, Institute of Mathematical Statistics; 2005.
• Hardin, J., Rocke, D.; The Distribution of Robust Distances, Journal of Computational and Graphical Statistics, 14: 1-19, 2005.  (R code: to estimate the MCD – mcd.est.r and to estimate c and m – cm.r.)
• Hardin, J.; Microarray Data from a Statistician’s Point of View, STATS, 42:4-13, 2005.

 

2004

• Hardin, J., Waddell, M., Page, D., Zhan, F., Barlogie, B., Crowley, J., Shaughnessy, J.; Evaluation of Multiple Models to Distinguish Closely Related Forms of Disease Using DNA Microarray Data: an Application to Multiple Myeloma,  Statistical Applications in Genetics and Molecular Biology, 3 (article 10), 2004.
• Hardin, J., Rocke, D.;  Outlier Detection in the Multiple Cluster Setting Using the Minimum Covariance Determinant Estimator, Computational Statistics and Data Analysis, 44: 625-638, 2004.
• Pauler, D., Hardin, J., Faulkner, J., Leblanc, M., Crowley, J.; Survival Analysis with Gene Expression Arrays. In Handbook of Statistics 23: Advances in Survival Analysis, eds. N.Balakrishnan and C.R. Rao, Elsevier Science: Amsterdam; 2004.

 

2002

• Durbin, B., Hardin, J., Hawkins, D., Rocke, D.;  A Variance-Stabilizing Transformation for Gene-Expression Microarray Data; Bioinformatics, 18: S105-S110, 2002.
• Zhan, F., Hardin, J., Kordsmeier, B., Bumm, K., Zheng, M., Tian, E., Sanderson, R., Yang, Y., Wilson, C., Zangari, M., Anaissie, E., Morris, C., Muwalla, F., van Rhee, F., Fassas, A., Crowley, J., Tricot, G., Barlogie, B., Shaughnessy, J.; Global Gene Expression Profiling of Multiple Myeloma, Monoclonal Gammopathy of Undetermined Significance, and Normal Bone Marrow Plasma Cells; Blood, 99: 1745-1757, 2002.

 

1999-2000

• Hardin, J.; Multivariate Outlier Detection and Robust Clustering with Minimum Covariance Determinant Estimation and S-Estimation.  Ph.D. thesis, Statistics; University of California, Davis.  2000.
• Coleman, D., Dong, X., Hardin, J., Rocke, D.M., Woodruff, D.L.; Some Computational Issues in Cluster Analysis with no à priori Metric; Computational Statistics and Data Analysis, 31: 1-11, 1999.

 

  Statistical methods for high-throughput data

  Inference in machine learning

  Statistics and data science education

  Equity and flourising in statistics and data science