Yan Jin jyfeather

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。

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

	\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\\

	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)

	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(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]]