<html xmlns:v="urn:schemas-microsoft-com:vml" xmlns:o="urn:schemas-microsoft-com:office:office" xmlns:w="urn:schemas-microsoft-com:office:word" xmlns:m="http://schemas.microsoft.com/office/2004/12/omml" xmlns="http://www.w3.org/TR/REC-html40"><head><META HTTP-EQUIV="Content-Type" CONTENT="text/html; charset=us-ascii"><meta name=Generator content="Microsoft Word 14 (filtered medium)"><style><!--
/* Font Definitions */
@font-face
{font-family:Calibri;
panose-1:2 15 5 2 2 2 4 3 2 4;}
@font-face
{font-family:Consolas;
panose-1:2 11 6 9 2 2 4 3 2 4;}
/* Style Definitions */
p.MsoNormal, li.MsoNormal, div.MsoNormal
{margin:0in;
margin-bottom:.0001pt;
font-size:11.0pt;
font-family:"Calibri","sans-serif";}
p.MsoBodyText, li.MsoBodyText, div.MsoBodyText
{mso-style-link:"Body Text Char";
margin:0in;
margin-bottom:.0001pt;
text-align:justify;
line-height:200%;
font-size:12.0pt;
font-family:"Times New Roman","serif";}
a:link, span.MsoHyperlink
{mso-style-priority:99;
color:blue;
text-decoration:underline;}
a:visited, span.MsoHyperlinkFollowed
{mso-style-priority:99;
color:purple;
text-decoration:underline;}
p.MsoPlainText, li.MsoPlainText, div.MsoPlainText
{mso-style-priority:99;
mso-style-link:"Plain Text Char";
margin:0in;
margin-bottom:.0001pt;
font-size:10.5pt;
font-family:Consolas;}
span.PlainTextChar
{mso-style-name:"Plain Text Char";
mso-style-priority:99;
mso-style-link:"Plain Text";
font-family:Consolas;}
p.papertitle, li.papertitle, div.papertitle
{mso-style-name:"paper title";
margin-top:0in;
margin-right:0in;
margin-bottom:6.0pt;
margin-left:0in;
text-align:center;
font-size:24.0pt;
font-family:"Times New Roman","serif";}
p.Biography, li.Biography, div.Biography
{mso-style-name:Biography;
margin-top:0in;
margin-right:0in;
margin-bottom:12.0pt;
margin-left:0in;
text-align:justify;
layout-grid-mode:char;
font-size:11.0pt;
font-family:"Times New Roman","serif";}
span.EmailStyle21
{mso-style-type:personal;
font-family:"Calibri","sans-serif";
color:windowtext;}
span.apple-converted-space
{mso-style-name:apple-converted-space;}
span.EmailStyle23
{mso-style-type:personal;
font-family:"Calibri","sans-serif";
color:#1F497D;}
span.EmailStyle24
{mso-style-type:personal;
font-family:"Calibri","sans-serif";
color:#1F497D;}
span.EmailStyle25
{mso-style-type:personal;
font-family:"Calibri","sans-serif";
color:#1F497D;}
span.EmailStyle26
{mso-style-type:personal;
font-family:"Calibri","sans-serif";
color:#1F497D;}
span.EmailStyle27
{mso-style-type:personal;
font-family:"Calibri","sans-serif";
color:#1F497D;}
span.EmailStyle28
{mso-style-type:personal;
font-family:"Calibri","sans-serif";
color:#1F497D;}
span.EmailStyle29
{mso-style-type:personal-reply;
font-family:"Calibri","sans-serif";
color:#1F497D;}
span.BodyTextChar
{mso-style-name:"Body Text Char";
mso-style-link:"Body Text";}
.MsoChpDefault
{mso-style-type:export-only;
font-size:10.0pt;}
@page WordSection1
{size:8.5in 11.0in;
margin:1.0in 1.0in 1.0in 1.0in;}
div.WordSection1
{page:WordSection1;}
--></style><!--[if gte mso 9]><xml>
<o:shapedefaults v:ext="edit" spidmax="1026" />
</xml><![endif]--><!--[if gte mso 9]><xml>
<o:shapelayout v:ext="edit">
<o:idmap v:ext="edit" data="1" />
</o:shapelayout></xml><![endif]--></head><body lang=EN-US link=blue vlink=purple><div class=WordSection1><p class=MsoNormal align=center style='text-align:center;line-height:110%'><b><span style='color:red'>INDUSTRIAL ENGINEERING SEMINAR<o:p></o:p></span></b></p><p class=MsoNormal align=center style='text-align:center;line-height:110%'><b><span style='color:red'><o:p> </o:p></span></b></p><p class=MsoNormal align=center style='text-align:center;line-height:110%'><b><span style='color:red'>Date: April 5, 2013<o:p></o:p></span></b></p><p class=MsoNormal align=center style='text-align:center;line-height:110%'><b><span style='color:red'>Time: 10am-11am<o:p></o:p></span></b></p><p class=MsoNormal align=center style='text-align:center;line-height:110%'><b><span style='color:red'>Place: D 102<o:p></o:p></span></b></p><p class=MsoNormal align=center style='text-align:center;line-height:110%'><b><span style='color:navy'><o:p> </o:p></span></b></p><p class=MsoNormal align=center style='text-align:center;line-height:150%'><a name="_GoBack"></a><b>Machine Learning Framework for Multi-Voxel Pattern Analysis of fMRI Data <o:p></o:p></b></p><p class=MsoNormal align=center style='text-align:center;line-height:150%'><b>By<o:p></o:p></b></p><p class=MsoNormal align=center style='text-align:center;line-height:150%'><b>Dr. W. Art Chaovalitwongse<o:p></o:p></b></p><p class=MsoNormal align=center style='text-align:center;line-height:150%'><b>Departments of Industrial & Systems Engineering and Radiology <o:p></o:p></b></p><p class=MsoNormal align=center style='text-align:center;line-height:150%'><b>Integrated Brain Imaging Center (IBIC)<o:p></o:p></b></p><p class=MsoNormal align=center style='text-align:center;line-height:150%'><b>University of Washington<o:p></o:p></b></p><p class=MsoNormal><o:p> </o:p></p><p class=MsoNormal align=center style='text-align:center;line-height:110%'><b><span style='color:red'>Abstract<o:p></o:p></span></b></p><p class=MsoNormal align=center style='text-align:center;line-height:110%'><b><span style='color:red'><o:p> </o:p></span></b></p><p class=MsoBodyText style='line-height:150%'><span style='font-size:11.0pt;line-height:150%'>Multi-voxel pattern analysis (MVPA) of functional magnetic resonance imaging (fMRI) data is an emerging research area that investigates the neural correlates of cognition. MVPA constructs a neural activity model of cognitive representations and processing to predict activity patterns according to stimulus conditions. The problem of activity pattern prediction is commonly cast as a complex classification problem. Such a problem is very challenging because the number of voxels (features) greatly exceeds the number of data instances (stimuli). This can lead to model overfitting, which will deteriorate the classification accuracy. In this research, we developed a feature selection framework to select informative voxels based on mutual information (MI) and partial least square (PLS), which are used to quantify the statistical dependency between features and stimulus conditions. To evaluate the utility of our approach, we employed several linear classification algorithms on a publicly available fMRI data set that has been widely used to benchmark MVPA performance. The computational results suggest that feature selection based on the MI and PLS rankings can drastically improve the classification accuracy. Additionally, high-ranked features provide meaningful insights into the functional-anatomical relationship of neural activity and the associated tasks. <o:p></o:p></span></p><p class=MsoNormal style='text-align:justify'><span style='font-size:12.0pt'><o:p> </o:p></span></p><p class=MsoNormal align=center style='text-align:center;line-height:110%'><b><span style='color:red'>Biography<o:p></o:p></span></b></p><p class=MsoNormal align=center style='text-align:center;line-height:110%'><b><span style='color:red'><o:p> </o:p></span></b></p><p class=MsoBodyText style='line-height:150%'><span style='font-size:11.0pt;line-height:150%'>Wanpracha Art Chaovalitwongse is Associate Professor in the Departments of Industrial & Systems Engineering and Radiology (joint) at the University of Washington, Seattle. Before moving to Seattle, he worked as Visiting Associate Professor in the Department of Operations Research & Financial Engineering at Princeton University in 2011. From 2005 to 2011, he was on the faculty in the Department of Industrial & Systems Engineering at Rutgers University. Before working in academia, he worked at the Corporate Strategic Research, ExxonMobil Research & Engineering, where he managed research in developing efficient mathematical models and novel statistical data analyses for upstream oil exploration and downstream business operations in multi-continent oil transportation. He received a Bachelor’s degree in Telecommunication Engineering from King Mongkut Institute of Technology at Ladkrabang, Thailand, in 1999 and M.S. and Ph.D. degrees in Industrial & Systems Engineering from the University of Florida in 2000 and 2003. His research group conducts basic computational science, applied, and translational research at the interface of engineering, medicine, and other emerging disciplines. His work thus far has focused on (a) computational neuroscience, (b) computational biology, and (c) logistics optimization. He holds three patents of novel optimization techniques adopted in the development of seizure prediction system. His academic honors include 2003 Excellence in Research from the University of Florida, 2006 NSF CAREER Award, 2007 Notable Alumni of King Mongut’s Institute Technology at Ladkrabang, 2004 & 2008 (2-times winner) William Pierskalla Best Paper Award by the Institute for Operations Research and the Management Sciences (INFORMS), 2009 Outstanding Service Award by the Association of Thai Professionals in America and Canada, 2010 Rutgers Presidential Fellowship for Teaching Excellence, and several other student paper awards with his PhD students. He has edited 3 books and published over 100 research articles including 59 papers in leading journals.<o:p></o:p></span></p><p class=MsoNormal><span style='color:#1F497D'><o:p> </o:p></span></p></div></body></html>