报告人：张伏博士(复旦大学)题目：Path-Dependent Optimal Stochastic Control and Viscosity Solution of Associated Bellman Equations时间：2013年4月25日(星期四)下午3：00—4：00地点：数学系致远楼10

报告人：应坚刚教授(复旦大学)题目： 关于鞅表示定理时间：2013年4月18日(星期四)下午3：30—4：30地点：数学系致远楼10

报告人：郑惠恒教授（英国帝国理工）题目：Markov Modulated Weak Stochastic Maximum Principle时间：2013年4月11日，星期四下午3：00—4：00地点：数学系致远楼10

报告人：汤善健教授(复旦大学)题目： 路径相关微分系统的最优控制时间：2013年4月11日(星期四)下午2：00—3：00地点：数学系致远楼10

When looking at surfaces in R^3, the most appealing pictures of surfaces frequently show the presence of symmetries. In this talk we will start by discussing the notion of "symmetry" for arbitrary surfaces. We will continue by introducing two types of "moving frames". A characterization of and a construction principle for surfaces of constant mean curvature will follow naturally. Returning to symmetries, we will express them for arbitrary surfaces in terms of certain "orthogonal moving frames".

High-dimensional data pose many challenges to traditional statistical and computational methods. Specifically, due to the small sample size, there are more uncertainties associated with standard estimations of parameters such as the mean and variance estimations. As a consequence, statistical analyses based on such parameter estimation are usually unreliable. To obtain more accurate parameter estimation some statistical methods, such as regularization through shrinkage, may yield better results. In this talk, I will first review some new developments in shrinkage estimation on variances, under the framework of microarray data analysis. I will then introduce a recent work of mine that develops the shrinkage estimation of the population mean under the quadratic loss function for high-dimensional data. For illustration, a shrinkage-based diagonal discriminant rule will be proposed and it will be demonstrated to outperform the existing competitor in a wide range of settings.

题目：Estimation of Partially Linear Regression Model under Partial Consistency Property报告人：Professor Heng PENG(香港浸会大学教授)AbstractClassic linear regression models are often criticized for requiring a response surface to be linear and additive in predictors. To ameliorate this problem, partially linear model of the form Y=Xβ+g(Z) was proposed to reduced bias. The nonparametric es...

报 告 人：沈 纯 理 教授（华东师范大学数学系）报告题目：图像处理中与微分几何相关的一些问题报告时间：2013年3月15日（周五）下午4:00-5:00报告地点：致远楼107