Contact:
Email: jun.li@ucr.edu | Tel: 951-827-3787 | Fax: 951-827-3286
Office: 1346 Olmsted Hall
           University of California, Riverside
           Riverside, CA 92521

Education:                                                                                   
   Rutgers, the State University of New Jersey                    Statistics               Ph.D. (2006)
   Hong Kong University of Science & Technology              Applied Math         M.S. (2001)
   Peking University                                                              Applied Math         B.S. (1999)

Professional Appointments:
   Associate Professor (7/12- present), Department of Statistics, University of California, Riverside
   Assistant Professor (7/06-6/12), Department of Statistics, University of California, Riverside
   
Manuscripts under review:​

• Li, J. (2019+). A Simple and Efficient Two-Sample Test for High-Dimensional Means. 
• Tang, L. and Li, J. (2019+). Combining Dependent Tests Based on Data Depth with Applications to the Two-Sample Problem with Arbitrary Data Types.

Refereed Journal Publications:

• Li, J. (2019+). Asymptotic Distribution-Free Change-Point Detection Based on Interpoint Distances for High-Dimensional Data.  Accepted by Journal of Nonparametric Statistics.
• Li, J. (2020). Nonparametric Adaptive CUSUM Chart for Detecting Arbitrary distributional changes. Accepted by Journal of Quality Technology.
• Li, J. (2019). Efficient Global Monitoring Statistics for High-Dimensional Data Streams. Accepted by Quality and Reliability Engineering International.
• Qiu, P., Li, W. and Li, J. (2019). A New Process Control Chart for Monitoring Short-range Serially Correlated Data. Accepted by Technometrics.
• Li, J., Jeske, D. R., Zhou, Y. and Zhang, X. (2019). A Wavelet-based Nonparametric CUSUM Control Chart for Autocorrelated Processes with Applications to Network Surveillance.  Quality and Reliability Engineering International, Vol. 35, 644-658.
• Li, J. (2019). A Two-Stage On-line Monitoring Procedure for High-Dimensional Data. Journal of Quality Technology, Vol. 51, 392-406.
• Williams, M., Li, J., and Talbot, P. (2019). Effects of Model, Method of Collection, and Topography on Chemical Elements and Metals in the Aerosol of Tank-Style Electronic Cigarettes. Scientific Reports, Vol. 9:13969. 
• Jiang, L., Xiong, Z., Song, Y., Lu, Y., Chen, Y., Schultz, J.S., Li, J., and Liao, J. (2019). Protein-Protein Affinity Determination by Quantitative FRET Quenching. Scientific Reports, Vol. 9: 2050.
• Li, J. (2018). EDF Goodness-of-Fit Tests Based on Center-Outward Ordering. Journal of Nonparametric Statistics, Vol. 30, 973-989.
• Li, J. (2018). Asymptotic Normality of Interpoint Distances for High Dimensional Data with Applications to the Two-Sample Problem. Biometrika, Vol. 105, 529-546.
• Li, J. and Qiu, P. (2017). Construction of An Efficient Multivariate Dynamic Screening System. Quality and Reliability Engineering International, Vol. 33, 1969-1981.
• Myhre, J., Jeske, D.R., Li, J. and Hansen, A. M. (2017). Combining Binomial Test Data via Two-Stage Solutions. Applied Stochastic Models in Business and Industry, Vol. 34, 20-30.
• Ying, S., Schaefer, M. V., Cock-Esteb, A., Li, J. and Fendorf, S. (2017). Depth stratification leads to distinct zones of manganese and arsenic contaminated groundwater. Environmental Science & Technology, Vol. 51, 8926-8932.
• Li, J. and Liu, R. Y. (2016). New Nonparametric Tests for Comparing Multivariate Scales Using Data Depth. Robust Rank-Based and Nonparametric Methods, Michigan, USA, Springer, 209-226.
• Li, J. and Qiu, P. (2016). Nonparametric Dynamic Screening System for Monitoring Correlated Longitudinal Data. IIE Transactions, Vol. 48, 772-786.
• Zhang, X., Jeske, D. R., Li, J. and Wong, V. (2015). A Sequential Logistic Regression Classifier Based on Mixed Effects with Applications to Longitudinal Data. Computational Statistics and Data Analysis, Vol. 94, 238-249.
• Einmahl, J. H. J., Li, J. and Liu, R. Y. (2015). Bridging Centrality and Extremity: Refining Empirical Data Depth Using Extreme Value Statistics. Annals of Statistics, Vol. 43, 2738-2765.
• Li, J. (2015). Nonparametric Multivariate Statistical Process Control Charts: A Hypothesis Testing-based Approach. Journal of Nonparametric Statistics, Vol. 27, 384-400.
• Li, J. and Yu, Y. (2015). A Nonparametric Test of Missing Completely at Random for Incomplete Multivariate Data. Psychometrika, Vol. 80, 707-726.
• Jiang, L., Saavedra, A. N., Way, G., Alanis, J., Kung, R., Li, J., Xiang, W. and Liao, J. (2014). Specific Substrate Recognition and Thioester Intermediate Determinations in Ubiquitin and SUMO Conjugation Cascades Revealed by A High-sensitive FRET Assay. Molecular BioSystems, Vol.10, 778-786.
• Jeske, D. R., Li, J. and Wong, V. (2013). On the Effectiveness of Mixed Model Based Logistic Regression Classifiers for Longitudinal Data. Integration: Mathematical Theory and Applications, Vol. 3, 233-244.
• Li, J. and Ban, J. (2013). Species Assemblage Comparison with Abundance-based Data Using Zero-inflated Poisson Mixtures. Statistica Sinica, Vol. 23, 1215-1233.
• Li, J., and Zhang, X. and Jeske, D. R. (2013). Nonparametric Multivariate CUSUM Control Charts for Location and Scale Changes. Journal of Nonparametric Statistics, Vol. 25, 1-20.
• Pettyjohn, J., Jeske, D. R. and Li, J. (2013). Estimation and Confidence Intervals for Clock Offset in Networks with Bivariate Exponential Delays. Communications in Statistics - Theory and Methods, Vol. 42, 1024-1041.
• Li, J., Cuesta-Alberto, J. A. and Liu, R. Y. (2012). DD-Classifier: Nonparametric Classification Procedures Based on DD-plot. Journal of the American Statistical Association, Vol. 107, 737-753.
• Li, J. and Mao, C. X. (2012). Simultaneous Confidence Inference on Species Accumulation Curves. Journal of Agricultural, Biological, and Environmental Statistics, Vol. 17, 1-14.
• Li, J., Ban, J. and Santiago, L. S. (2011). Nonparametric Tests for Homogeneity of Species Assemblages: A Data Depth Approach. Biometrics, Vol. 67, 1481-1488.
• Pettyjohn, J., Jeske, D. R. and Li, J. (2010). Least Squares-Based Estimation of Relative Clock Offset and Frequency in Sensor Networks with High Latency. IEEE Transactions on Communications, Vol. 58, 3613-3620.
• Pei, L. K. and Li, J. (2010). Effects of Unequal Variance in Ability Distribution on the Performance of Logistic Regression, Mantel-Haenszel, SIBTESTIRT and IRT Likelihood Ratio for DIF Detection. Applied Psychological Measurement, Vol. 34, 453-456.
• Li, J. and Jeske, D. R. (2009). Sequential Fixed Width Confidence Intervals for the Offset between Two Network Clocks. Sequential Analysis, Vol. 28, 475-487.
• Mao, C. X. and Li, J. (2009). Comparing Species Assemblages via Species Accumulation Curves. Biometrics, Vol. 65, 1063-1067.
• Li, J. and Jeske, D. R. (2009). Maximum Likelihood Estimators of Clock Offset and Skew under Exponential Delays. Journal of Applied Stochastic Models in Business and Industry, Vol. 25, 445-459 (see also errata, 506-507).
• Einmahl, J. H. J., Li, J. and Liu, R. Y. (2009). Thresholding Events of Extreme in Simultaneous Monitoring of Multiple Risks. Journal of the American Statistical Association, Vol. 104, 982-992.
• Li, J., Jeske, D. R. and Pettyjohn, J. (2009). Approximate and Generalized Pivotal Quantities for Deriving Confidence Intervals for the Offset between Two Network Clocks. Statistical Methodology, Vol. 6, 97-107.
• Li, J. and Liu, R. Y. (2008). Multivariate Spacings Based on Data Depth: I. Construction of Nonparametric Multivariate Tolerance Regions. Annals of Statistics, Vol. 36, 1299-1323. 
• Li, J. and Liu, R. Y. (2004). New Nonparametric Tests of Multivariate Locations and Scales Using Data Depth. Statistical Science, Vol. 19, 686-696.

​Teaching:
​Ph.D. Thesis Advisor for:
          Yao Yu (Ph.D. 2012), Jifei Ban (Ph.D. 2012), Xin Zhang (Ph.D. in 2014), Linli Tang

Courses taught:
         Regression Analysis 
         Design of Experiments 
         Multivariate Analysis I  
         Multivariate Analysis II
         Nonparametric Statistics I
         Nonparametric Statistics II