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MSL Lecture Theatre 102 (2185 East Mall)
Tue 24th March 2015
Projecting the uncertainty of sea level rise using climate models and statistical downscaling
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Most global climate models do not estimate sea level directly. A semi-empirical approach is to relate sea level change to temperature, and then apply this relationship to climate model projections of temperature for different future scenarios. Another possibility is to estimate the relationship between global mean temperature in historical runs of a model, and instead apply this relationship to future temperature projections. We compare these two methods to estimate global annual mean sea level, and assess the resulting uncertainty.
Of more practical importance is to estimate local sea level. We exemplify this by developing models for projected sea level rise in Vancouver and Washington State, and illustrate different sources of uncertainty in the projections.

ESB 4192
Tue 17th March 2015
Junjian Zhang
Dr. Junjian Zhang is a professor at Guangxi Normal University, Guilin China. He received his PhD in 2006 at Academy of Mathematics and Systems Science, Chinese Academy of Sciences under the supervision of Professor Guoying Li. Following his PhD, he conducted post-doctoral research at Beijing University of Technology under supervision of Professor Zhongzhan Zhang. He is currently visiting the department of statistics, UBC hosted by Dr. Jiahua Chen for the period of 2015. His research interests include mathematical statistics and its applications, especially in nonparametric likelihood ratio. His research is supported by the following funds: National Social Science Foundation of China, National Natural Science Foundation of China, Guangxi Science Foundation. He was the winner of the “best paper award of Zhong Jiaqing” at the 10th Jingjin Wusi youth meeting and the “best paper” award at the 8th Guangxi statistical science colloquium. He was invited speaker in many research conferences including the united meeting of Hunan,Guangdong and Guangxi mathematical societies, the Tenth National Congress of Chinese Society of Probability and Statistics. His email address is
Nonparametric Likelihood and Its Applications
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Nonparametric likelihood is one of the important topics in statistics. In this talk, we will introduce the basic ideas for nonparametric likelihood and present our latest research achievements. For example, we generalize the empirical likelihood to the empirical Lq likelihood and Empirical power divergent likelihood. The former is usually used to the estimating theory, the latter is usually used to the goodness-of-fit. This talk will focus on the goodness-of-fit. In addition, the talk will discuss the adjusted empirical (Euclidean) likelihood and its applications, the nonparametric likelihood for the complex data such as the rounded data, dependent data, high-dimensional data, and so on.

ESB 4192
Tue 10th March 2015
Quantum Computation and Statistics
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Quantum computation and quantum information are of great current interest in computer science, mathematics, physical sciences and engineering. They will likely lead to a new wave of technological innovations in communication, computation and cryptography. As the theory of quantum physics is fundamentally stochastic, randomness and uncertainty are deeply rooted in quantum computation, quantum information and quantum simulation. Thus statistics can play an important role in quantum computation and quantum simulation, which in turn offer great potential to revolutionize computational statistics. This talk will first give a brief introduction on quantum computation and then present my recent work on quantum tomography via compressed sensing as well as statistical modeling and analysis of quantum computing experimental data.

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