Yan Jin jyfeather

No.	Research Topics	Researcher
1	Readmission Risk ?	Yazhuo
2	Health Information Exchange	Diego, Aly Strauss, Andres
3	Exit Site Infection in Patients Undergoing Mechanical Circulatory Support at TGH	Lori, Andres, Diego
4	clothing and bleeding in mechanical circulatory support (MCS) patients	Dr Christiano Caldeira
5	predicting complications (RV disfunction) in patients before LVAD implantation	Dr Christiano Caldeira

Operations Research，这个主要研究优化算法，跟数学的人有很大的交叉
Operations Management，这个主要是把实际过程给建出模型，好用上数学的方法去优化，这个跟管理学的人交叉很大
统计有关的，比如statistical process control, quality control, design of experiment, data mining等等。故名思意，这个跟统计的人交叉比较多，但是IE的人更注重应用
Supply Chain Management，这里面又包含很多，比如仓库啊，物流啊，库存管理等等，这个又跟商科的人交叉比较多。但是商科的人可能更注重怎么去理解这个问题，而IE的人更注重如何解决这个问题。
Human Factor, 人因工程，比如研究人体工程学啊什么的。这个跟心理系的交叉很多。
Manufacturing System，生产系统。涉及很广，比如生产线的优化，排程调度什么的。应用也很广，从工厂到交通系统都能用得到。

暂时想到这么多，但是IE的要义还是在于让事情做得更有效率，所以应用的范围也一直在拓展。比如最近很火热的Healthcare system之类的。

	# corrplot

	The R package corrplot package is an elegant visualization tool for correlation matrix and confidence interval. It also contains some algorithms to do matrix reordering. In addition, corrplot is good at details, including choosing color, text labels, color labels, layout, etc.


	## Installation
	You can install the stable version on [CRAN](http://cran.r-project.org/package=corrplot):

	```r
	install.packages('corrplot', dependencies = TRUE)

	library(Rglpk)

	# mas74.mps file could be downloaded from http://miplib.zib.de/miplib2003/miplib2003/mas74.php
	path <- paste(getwd(), "/example/mas74.mps", sep = "")
	model <- Rglpk_read_file(path, type = "MPS_fixed")

	# read components from mps file
	objective <- model$objective
	constraint <- model$constraints
	constraint_1 <- constraint[[1]]

	library("arulesViz")
	data("Groceries")
	summary(Groceries)
	rules <- apriori(Groceries, parameter=list(support=0.001,confidence=0.5))
	rules
	inspect(head(sort(rules,by="lift"),3))

	# Scatter
	plot(rules)
	plot(rules,measure=c("support","lift"),shading="confidence")

	library(mvtnorm)
	x.points <- seq(-3,3,length.out=100) # generate a sequence
	y.points <- x.points
	z <- matrix(0, nrow=100, ncol=100)
	mu <- c(1,1)
	sigma <- matrix(c(2,1,1,1),nrow=2)
	for (i in 1:100) {
	for (j in 1:100) {
	# multivariate normal density
	z[i,j] <- dmvnorm(c(x.points[i],y.points[j]), mean=mu, sigma=sigma)

	\title{A Very Simple \LaTeXe{} Template}
	\author{
	Vitaly Surazhsky \\
	Department of Computer Science\\
	Technion---Israel Institute of Technology\\
	Technion City, Haifa 32000, \underline{Israel}
	\and
	Yossi Gil\\
	Department of Computer Science\\
	Technion---Israel Institute of Technology\\