Liqun Wang obtained his Bachelor degree in mathematics from Northern Jiaotong University in Beijing, Master degree in statistics from Beijing Normal University, and Doctoral degree in statistics and econometrics from Vienna University of Technology, Austria. He also received a Postgraduate Diploma in mathematical and computer sciences from Vienna Institute for Advanced Studies. Liqun was a postdoctoral research fellow at Universities of Hannover and Dortmund, Germany, an assistant professor at University of Basel, Switzerland, and a visiting scholar at University of Southern California, Los Angeles, University of California  Berkeley, and University of Toronto.

Liqun Wang's research is mainly in the areas of applied probability and statistical theory and methodology for complex data analysis. His current research topics include boundary crossing probability (first passage time) for diffusion processes, identification and estimation in nonlinear measurement error models and in longitudinal data models with missing data, highdimensional variable selection and data assimilation, and Monte Carlo simulation methods in statistical computation and optimization. Liqun is also interested in biostatistics, econometrics, as well as statistical applications in engineering optimization, environmental, medical and health sciences.

 Wang L, Shan S, Wang GG (2004). Modepursuing sampling method for global optimization on expensive blackbox functions. Engineering Optimization, 36, 419438.
 Wang L, Poetzelberger K (1997). Boundary crossing probability for Brownian motion and general boundaries. Journal of Applied Probability, 34, 5465.
 Poetzelberger K, Wang L (2001). Boundary crossing probability for Brownian motion. Journal of Applied Probability, 38, 152164.
 Wang L (2004). Estimation of nonlinear models with Berkson measurement errors. Annals of Statistics, 32, 25592579.
 Fu JC, Wang L (2002). A randomdiscretization based Monte Carlo sampling method and its application. Methodology and Computing in Applied Probability, 4, 525.
 Wang L, Poetzelberger K (2007). Crossing probabilities for diffusion processes with piecewise continuous boundaries. Methodology and Computing in Applied Probability, 9, 2140.
 Fu JC, Wang L, Lou WY (2003). On exact and large deviation approximation for the distribution of the longest run in a sequence of twostate Markov dependent trials. Journal of Applied Probability, 40, 346360.
 Wang L, Leblanc A (2008). Secondorder nonlinear least squares estimation. Annals of the Institute of Statistical Mathematics, 60, 883900.
 Wang L (2003). Estimation of nonlinear Berksontype measurement error models. Statistica Sinica, 13, 12011210.
 Wang L, Hsiao C (1995). A simulated semiparametric estimation of nonlinear errorsinvariables models. Working Paper, Department of Economics, University of Southern California.
 Wang GG, Wang L, Shan S (2005). Reliability assessment using discriminative sampling and metamodeling. SAE Transactions Journal of Passenger Cars  Mechanical Systems, 114 (6), 291300.
 Wang L (1998). Estimation of censored linear errorsinvariables models. Journal of Econometrics, 84, 383400.
 Wang L, Hsiao C (2011). Method of moments estimation and identifiability of semiparametric nonlinear errorsinvariables models. Journal of Econometrics, 165, 3044.
 Wang L (1990). Generalized shrunken least squares estimators. Chinese Journal of Applied Probability and Statistics, 6, 225232.
 Abarin T, Wang L (2006). Comparison of GMM with secondorder least squares estimator in nonlinear models. Far East Journal of Theoretical Statistics, 20, 179196.

 Fan J, Kong L, Wang L, Xiu N. (2016). The uniqueness and greedy method for quadratic compressive sensing. Proceedings of the 2016 IEEE International Conference on Data Science and Advanced Analytics, 808815.
 Li DH, Wang L (2016). A weighted simulationbased estimator for incomplete longitudinal data models. Statistics and Probability Letters, 113, 1622.
 Jin Z, Wang L (2015). First passage time for Brownian motion and piecewise linear boundaries. Methodology and Computing in Applied Probability, doi:10.1007/s1100901594752.
 Xu K, Ma Y, Wang L (2015). Instrument assisted regression for errors in variables models with binary response. Scandinavian Journal of Statistics, 42, 104117.
 Zhang S, Zheng X, Chen JM, Chen Z, Dan B, Yi X, Wang L, Wu G. (2015). A global carbon assimilation system using a modified ensemble Kalman filter. Geosci. Model Dev., 8, 805816.
 Zhu Q, Wang L, Tannenbaum S, Ricci A, DeFusco P, Hegde P (2014). Pathologic response prediction to neoadjuvant chemotherapy utilizing pretreatment near infrared imaging parameters and tumor pathologic criteria. Breast Cancer Research, 16, 456.
 Wu G, Yi X, Wang L, Liang X, Zhang S, Zhang X, Zheng X. (2014). Improving the ensemble transform Kalman filter using a secondorder Taylor approximation of the nonlinear observation operator. Nonlinear Processes in Geophysics, 21, 955970.
 Abarin T, Li H, Wang L, Briollais L (2014). On method of moments estimation in linear mixed effects models with measurement error on covariates and response with application to a longitudinal study of geneenvironment interaction. Statistics in Biosciences, 6, 118.
 Wang L, Lee CH (2014). Discretizationbased direct random sample generation. Computational Statistics and Data Analysis, 71, 10011010.
 Wu G, Zheng X, Wang L, Zhang S, Liang X, Li Y (2013). A new structure of error covariance matrices and their adaptive estimation in EnKF assimilation. Quarterly Journal of the Royal Meteorological Society, 139, 795804.
 Li D, Wang L (2013). A semiparametric estimation approach for linear mixed models. Communications in Statistics  Theory and Methods, 42, 19821997.
 Abarin T, Wang L (2012). Instrumental variable approach to covariate measurement error in generalized linear models. Annals of the Institute of Statistical Mathematics, 64, 475493.
 Li H, Wang L (2012a). A consistent simulationbased estimator in generalized linear mixed models. Journal of Statistical Computation and Simulation, 82, 10851103.
 Li, H., Wang, L (2012b). Consistent estimation in generalized linear mixed models with measurement error. Journal of Biometrics and Biostatistics, S7:007, doi:10.4172/21556180.S7007.
 Chen S, Hsiao C, Wang L (2012). Measurement errors and censored structural latent variables models. Econometric Theory, 28, 696703.
