<div dir="ltr">
<p class="MsoNormal" style="text-align:justify"><b><span style="font-size:22pt">Call for
Papers<span></span></span></b></p>
<p class="MsoNormal" style="text-align:justify"><span> </span></p>
<p class="MsoNormal" style="text-align:justify"><b><span style="font-size:14pt">2017 IEEE
International Conference on Big Data
(IEEE Big Data 2017)<span></span></span></b></p>
<p class="MsoNormal" style="text-align:justify"><span lang="IT"> </span></p>
<p class="MsoNormal" style="text-align:justify"><a href="http://cci.drexel.edu/bigdata/bigdata2017/"><span lang="IT">http://cci.drexel.edu/bigdata/bigdata2017/</span></a><span lang="IT"><span></span></span></p>
<p class="MsoNormal" style="text-align:justify"><span lang="IT"> </span></p>
<p class="MsoNormal" style="text-align:justify"><span lang="IT">December 11-14, 2017, Boston, MA, USA<span></span></span></p>
<p class="MsoNormal" style="text-align:justify"><span lang="IT"> </span></p>
<p class="MsoNormal"><span style="font-size:11pt">In recent years, “Big Data”
has become a new ubiquitous term. Big Data is transforming science,
engineering, medicine, healthcare, finance, business, and ultimately our society
itself. </span><span style="font-size:11pt;font-family:Cambria">The IEEE Big Data conference series started in 2013 has
established itself as the top tier research conference in Big Data.</span><span style="font-size:11pt;font-family:Calibri"> </span><span style="font-size:11pt;font-family:Cambria"> </span><span style="font-size:11pt;font-family:Calibri"><span></span></span></p>
<p class="gmail-MsoListParagraphCxSpFirst" style=""><span style="font-size:11pt;font-family:Symbol">·<span style="font-size:7pt;line-height:normal;font-family:'Times New Roman'">
</span></span><span style="font-size:11pt;font-family:Cambria">The first conference IEEE Big Data
2013 had more than 400 registered participants from 40 countries (<span class="gmail-apple-converted-space"> </span></span><a href="http://cci.drexel.edu/bigdata/bigdata2013/"><span style="font-size:11pt;font-family:Cambria">http://cci.drexel.edu/bigdata/bigdata2013/</span></a><span class="gmail-MsoHyperlink"><span style="font-size:11pt;font-family:Cambria">)</span></span><span class="gmail-apple-converted-space"><span style="font-size:11pt;font-family:Cambria"> and the </span></span><span style="font-size:11pt;font-family:Cambria">regular
paper acceptance rate is 17.0%.</span><span style="font-size:11pt;font-family:Calibri"><span></span></span></p>
<p class="gmail-MsoListParagraphCxSpMiddle" style=""><span style="font-family:Symbol;color:black">·<span style="font-size:7pt;line-height:normal;font-family:'Times New Roman'">
</span></span><span style="color:black">The IEEE Big Data 2016
(<span class="gmail-apple-converted-space"> </span></span><a href="http://cci.drexel.edu/bigdata/bigdata2016/">http://cci.drexel.edu/bigdata/bigdata2016/</a><span class="gmail-apple-converted-space"><span style="color:black"> </span></span><span style="color:black">, regular paper acceptance rate: 18.7%) was held in
Washington DC, Dec 5-8, 2016 with close to 900 registered participants from 43
countries.<span></span></span></p>
<p class="gmail-MsoListParagraphCxSpLast"><span style="font-size:11pt;font-family:Calibri"><span> </span></span></p>
<p class="MsoNormal" style="text-align:justify"> The 2017 IEEE International Conference on Big Data
(IEEE Big Data 2017) will continue the success of the previous IEEE Big Data conferences.
It will provide a leading forum for disseminating the latest results in Big
Data Research, Development, and Applications.
<span></span></p>
<p class="MsoNormal" style="text-align:justify"><span style="font-family:Verdana"><span> </span></span></p>
<p class="gmail-MsoPlainText"><span style="font-size:12pt;font-family:'Times New Roman'">We
solicit high-quality original research papers (and significant work-in-progress
papers) in any aspect of Big Data with emphasis on 5Vs (Volume, Velocity,
Variety, Value and Veracity), including the Big Data challenges in scientific
and engineering, social, sensor/IoT/IoE, and multimedia (audio, video, image,
etc.) big data systems and applications. <b><i>Example topics of interest includes but is
not limited to the following</i></b>:<span></span></span></p>
<p class="MsoNormal" style="text-align:justify"><span style="font-family:Verdana"><span> </span></span></p>
<p class="MsoNormal" style="margin-left:0.25in;text-align:justify">1.<span style="font-size:7pt;line-height:normal;font-family:'Times New Roman'"> </span>Big
Data Science and Foundations<span></span></p>
<p class="MsoNormal" style="margin-left:0.75in;text-align:justify">a.<span style="font-size:7pt;line-height:normal;font-family:'Times New Roman'"> </span>Novel
Theoretical Models for Big Data<span></span></p>
<p class="MsoNormal" style="margin-left:0.75in;text-align:justify">b.<span style="font-size:7pt;line-height:normal;font-family:'Times New Roman'"> </span>New
Computational Models for Big Data <span></span></p>
<p class="MsoNormal" style="margin-left:0.75in;text-align:justify">c.<span style="font-size:7pt;line-height:normal;font-family:'Times New Roman'"> </span>Data
and Information Quality for Big Data<span></span></p>
<p class="MsoNormal" style="margin-left:0.75in;text-align:justify">d.<span style="font-size:7pt;line-height:normal;font-family:'Times New Roman'"> </span>New
Data Standards<span></span></p>
<p class="MsoNormal" style="text-align:justify"><span> </span></p>
<p class="MsoNormal" style="margin-left:0.25in;text-align:justify">2.<span style="font-size:7pt;line-height:normal;font-family:'Times New Roman'"> </span>Big
Data Infrastructure<span></span></p>
<p class="MsoNormal" style="margin-left:0.75in">a.<span style="font-size:7pt;line-height:normal;font-family:'Times New Roman'">
</span>Cloud/Grid/Stream Computing for Big Data <span></span></p>
<p class="MsoNormal" style="margin-left:0.75in">b.<span style="font-size:7pt;line-height:normal;font-family:'Times New Roman'">
</span>High Performance/Parallel Computing Platforms for Big Data<span></span></p>
<p class="MsoNormal" style="margin-left:0.75in">c.<span style="font-size:7pt;line-height:normal;font-family:'Times New Roman'">
</span>Autonomic Computing and Cyber-infrastructure, System
Architectures, Design and Deployment<span></span></p>
<p class="MsoNormal" style="margin-left:0.75in">d.<span style="font-size:7pt;line-height:normal;font-family:'Times New Roman'">
</span>Energy-efficient Computing for Big Data<span></span></p>
<p class="MsoNormal" style="margin-left:0.75in">e.<span style="font-size:7pt;line-height:normal;font-family:'Times New Roman'">
</span>Programming Models and Environments for Cluster,
Cloud, and Grid Computing to Support Big Data <span></span></p>
<p class="MsoNormal" style="margin-left:0.75in">f.<span style="font-size:7pt;line-height:normal;font-family:'Times New Roman'">
</span>Software Techniques and Architectures in Cloud/Grid/Stream
Computing<span></span></p>
<p class="MsoNormal" style="margin-left:0.75in">g.<span style="font-size:7pt;line-height:normal;font-family:'Times New Roman'">
</span>Big Data Open Platforms<span></span></p>
<p class="MsoNormal" style="margin-left:0.75in">h.<span style="font-size:7pt;line-height:normal;font-family:'Times New Roman'">
</span>New Programming Models for Big Data beyond
Hadoop/MapReduce, STORM <span></span></p>
<p class="MsoNormal" style="margin-left:0.75in">i.<span style="font-size:7pt;line-height:normal;font-family:'Times New Roman'">
</span>Software Systems to Support Big Data Computing<span></span></p>
<p class="MsoNormal"><span> </span></p>
<p class="MsoNormal" style="margin-left:0.25in">3.<span style="font-size:7pt;line-height:normal;font-family:'Times New Roman'">
</span>Big Data Management<span></span></p>
<p class="MsoNormal" style="margin-left:0.75in">a.<span style="font-size:7pt;line-height:normal;font-family:'Times New Roman'">
</span>Search and Mining of variety of data including
scientific and engineering, social, sensor/IoT/IoE, and multimedia data<span></span></p>
<p class="MsoNormal" style="margin-left:0.75in">b.<span style="font-size:7pt;line-height:normal;font-family:'Times New Roman'">
</span>Algorithms and Systems for Big DataSearch<span></span></p>
<p class="MsoNormal" style="margin-left:0.75in">c.<span style="font-size:7pt;line-height:normal;font-family:'Times New Roman'">
</span>Distributed, and Peer-to-peer Search<span></span></p>
<p class="MsoNormal" style="margin-left:0.75in">d.<span style="font-size:7pt;line-height:normal;font-family:'Times New Roman'">
</span>Big Data Search Architectures, Scalability and Efficiency<span></span></p>
<p class="MsoNormal" style="margin-left:0.75in">e.<span style="font-size:7pt;line-height:normal;font-family:'Times New Roman'">
</span>Data Acquisition, Integration, Cleaning, and Best Practices<span></span></p>
<p class="MsoNormal" style="margin-left:0.75in">f.<span style="font-size:7pt;line-height:normal;font-family:'Times New Roman'">
</span>Visualization Analytics for Big Data <span></span></p>
<p class="MsoNormal" style="margin-left:0.75in">g.<span style="font-size:7pt;line-height:normal;font-family:'Times New Roman'">
</span>Computational Modeling and Data Integration <span></span></p>
<p class="MsoNormal" style="margin-left:0.75in">h.<span style="font-size:7pt;line-height:normal;font-family:'Times New Roman'">
</span>Large-scale Recommendation Systems and Social Media
Systems<span></span></p>
<p class="MsoNormal" style="margin-left:0.75in">i.<span style="font-size:7pt;line-height:normal;font-family:'Times New Roman'">
</span>Cloud/Grid/Stream Data Mining- Big Velocity Data<span></span></p>
<p class="MsoNormal" style="margin-left:0.75in">j.<span style="font-size:7pt;line-height:normal;font-family:'Times New Roman'">
</span>Link and Graph Mining<span></span></p>
<p class="MsoNormal" style="margin-left:0.75in">k.<span style="font-size:7pt;line-height:normal;font-family:'Times New Roman'">
</span>Semantic-based Data Mining and Data
Pre-processing<span></span></p>
<p class="MsoNormal" style="margin-left:0.75in">l.<span style="font-size:7pt;line-height:normal;font-family:'Times New Roman'">
</span>Mobility and Big Data<span></span></p>
<p class="MsoNormal" style="margin-left:0.75in">m.<span style="font-size:7pt;line-height:normal;font-family:'Times New Roman'">
</span>Multimedia and Multi-structured Data- Big
Variety Data<span></span></p>
<p class="MsoNormal" style="margin-left:0.5in"><span> </span></p>
<p class="MsoNormal"><span> </span></p>
<p class="MsoNormal" style="margin-left:0.25in">4.<span style="font-size:7pt;line-height:normal;font-family:'Times New Roman'">
</span>Big Data Search and Mining<span></span></p>
<p class="MsoNormal" style="margin-left:0.75in">a.<span style="font-size:7pt;line-height:normal;font-family:'Times New Roman'">
</span>Social Web Search and Mining<span></span></p>
<p class="MsoNormal" style="margin-left:0.75in">b.<span style="font-size:7pt;line-height:normal;font-family:'Times New Roman'">
</span>Web Search<span></span></p>
<p class="MsoNormal" style="margin-left:0.75in">c.<span style="font-size:7pt;line-height:normal;font-family:'Times New Roman'">
</span>Algorithms and Systems for Big Data Search<span></span></p>
<p class="MsoNormal" style="margin-left:0.75in">d.<span style="font-size:7pt;line-height:normal;font-family:'Times New Roman'">
</span>Distributed, and Peer-to-peer Search<span></span></p>
<p class="MsoNormal" style="margin-left:0.75in">e.<span style="font-size:7pt;line-height:normal;font-family:'Times New Roman'">
</span>Big Data Search Architectures, Scalability and Efficiency<span></span></p>
<p class="MsoNormal" style="margin-left:0.75in">f.<span style="font-size:7pt;line-height:normal;font-family:'Times New Roman'">
</span>Data Acquisition, Integration, Cleaning, and Best Practices<span></span></p>
<p class="MsoNormal" style="margin-left:0.75in">g.<span style="font-size:7pt;line-height:normal;font-family:'Times New Roman'">
</span>Visualization Analytics for Big Data <span></span></p>
<p class="MsoNormal" style="margin-left:0.75in">h.<span style="font-size:7pt;line-height:normal;font-family:'Times New Roman'">
</span>Computational Modeling and Data Integration <span></span></p>
<p class="MsoNormal" style="margin-left:0.75in">i.<span style="font-size:7pt;line-height:normal;font-family:'Times New Roman'">
</span>Large-scale Recommendation Systems and Social Media
Systems<span></span></p>
<p class="MsoNormal" style="margin-left:0.75in">j.<span style="font-size:7pt;line-height:normal;font-family:'Times New Roman'">
</span>Cloud/Grid/StreamData Mining- Big Velocity Data<span></span></p>
<p class="MsoNormal" style="margin-left:0.75in">k.<span style="font-size:7pt;line-height:normal;font-family:'Times New Roman'">
</span>Link and Graph Mining<span></span></p>
<p class="MsoNormal" style="margin-left:0.75in">l.<span style="font-size:7pt;line-height:normal;font-family:'Times New Roman'">
</span>Semantic-based Data Mining and Data
Pre-processing<span></span></p>
<p class="MsoNormal" style="margin-left:0.75in">m.<span style="font-size:7pt;line-height:normal;font-family:'Times New Roman'">
</span>Mobility and Big Data<span></span></p>
<p class="MsoNormal" style="margin-left:0.75in;text-align:justify">n.<span style="font-size:7pt;line-height:normal;font-family:'Times New Roman'"> </span>Multimedia
and Multi-structured Data- Big Variety Data<span></span></p>
<p class="MsoNormal" style="margin-left:0.75in;text-align:justify"><span> </span></p>
<p class="gmail-MsoListParagraphCxSpFirst" style="margin-left:0.25in">5.<span style="font-size:7pt;line-height:normal;font-family:'Times New Roman'">
</span><span lang="EN-GB">Big
Data Security, Privacy and Trust</span><span></span></p>
<p class="gmail-MsoListParagraphCxSpMiddle" style="margin-left:0.75in">a.<span style="font-size:7pt;line-height:normal;font-family:'Times New Roman'"> </span><span lang="EN-GB">Intrusion Detection for Gigabit
Networks </span><span></span></p>
<p class="gmail-MsoListParagraphCxSpMiddle" style="margin-left:0.75in">b.<span style="font-size:7pt;line-height:normal;font-family:'Times New Roman'"> </span><span lang="EN-GB">Anomaly and APT Detection
in Very Large Scale Systems</span><span></span></p>
<p class="gmail-MsoListParagraphCxSpMiddle" style="margin-left:0.75in">c.<span style="font-size:7pt;line-height:normal;font-family:'Times New Roman'"> </span><span lang="EN-GB">High Performance Cryptography </span><span></span></p>
<p class="gmail-MsoListParagraphCxSpMiddle" style="margin-left:0.75in">d.<span style="font-size:7pt;line-height:normal;font-family:'Times New Roman'"> </span><span lang="EN-GB">Visualizing Large Scale Security
Data</span><span></span></p>
<p class="gmail-MsoListParagraphCxSpMiddle" style="margin-left:0.75in"><span class="gmail-apple-style-span">e.<span style="font-size:7pt;line-height:normal;font-family:'Times New Roman'">
</span></span><span class="gmail-apple-style-span">Threat
Detection using Big Data Analytics<span></span></span></p>
<p class="gmail-MsoListParagraphCxSpMiddle" style="margin-left:0.75in"><span lang="EN-GB">f.<span style="font-size:7pt;line-height:normal;font-family:'Times New Roman'">
</span></span><span lang="EN-GB">Privacy
Threats of Big Data<span></span></span></p>
<p class="gmail-MsoListParagraphCxSpMiddle" style="margin-left:0.75in"><span lang="EN-GB">g.<span style="font-size:7pt;line-height:normal;font-family:'Times New Roman'">
</span></span><span lang="EN-GB">Privacy
Preserving Big Data Collection/Analytics<span></span></span></p>
<p class="gmail-MsoListParagraphCxSpMiddle" style="margin-left:0.75in"><span lang="EN-GB">h.<span style="font-size:7pt;line-height:normal;font-family:'Times New Roman'">
</span></span><span lang="EN-GB">HCI
Challenges for Big Data Security & Privacy<span></span></span></p>
<p class="gmail-MsoListParagraphCxSpMiddle" style="margin-left:0.75in"><span lang="EN-GB">i.<span style="font-size:7pt;line-height:normal;font-family:'Times New Roman'">
</span></span><span lang="EN-GB">User
Studies for any of the above<span></span></span></p>
<p class="gmail-MsoListParagraphCxSpMiddle" style="margin-left:0.75in">j.<span style="font-size:7pt;line-height:normal;font-family:'Times New Roman'"> </span><span lang="EN-GB">Sociological Aspects of Big Data
Privacy</span><span></span></p>
<p class="gmail-MsoListParagraphCxSpMiddle" style="margin-left:0.75in">k.<span style="font-size:7pt;line-height:normal;font-family:'Times New Roman'"> </span><span lang="EN-GB">Trust management in IoT and other
Big Data Systems</span><span></span></p>
<p class="gmail-MsoListParagraphCxSpLast" style="margin-left:0.25in;text-align:justify"><span> </span></p>
<p class="MsoNormal" style="text-align:justify"><span> </span></p>
<p class="gmail-MsoListParagraphCxSpFirst" style="margin-left:0.25in;text-align:justify">6.<span style="font-size:7pt;line-height:normal;font-family:'Times New Roman'"> </span>Big
Data Applications<span></span></p>
<p class="gmail-MsoListParagraphCxSpMiddle" style="margin:0in 0in 0.05in 0.75in;line-height:15.6pt"><span style="color:black">a.<span style="font-size:7pt;line-height:normal;font-family:'Times New Roman'"> </span></span><span style="color:black">Complex Big Data Applications in Science,
Engineering, Medicine, Healthcare, Finance, Business, Law, Education,
Transportation, Retailing, Telecommunication<span></span></span></p>
<p class="gmail-MsoListParagraphCxSpMiddle" style="margin:0in 0in 0.05in 0.75in;line-height:15.6pt"><span style="color:black">b.<span style="font-size:7pt;line-height:normal;font-family:'Times New Roman'"> </span></span><span style="color:black">Big Data Analytics in Small Business Enterprises (SMEs),<span></span></span></p>
<p class="gmail-MsoListParagraphCxSpMiddle" style="margin:0in 0in 0.05in 0.75in;line-height:15.6pt"><span style="color:black">c.<span style="font-size:7pt;line-height:normal;font-family:'Times New Roman'"> </span></span><span style="color:black">Big Data Analytics in Government, Public Sector and Society in General<span></span></span></p>
<p class="gmail-MsoListParagraphCxSpMiddle" style="margin:0in 0in 0.05in 0.75in;line-height:15.6pt"><span style="color:black">d.<span style="font-size:7pt;line-height:normal;font-family:'Times New Roman'"> </span></span><span style="color:black">Real-life Case Studies of Value Creation through Big Data Analytics<span></span></span></p>
<p class="gmail-MsoListParagraphCxSpMiddle" style="margin:0in 0in 0.05in 0.75in;line-height:15.6pt"><span style="color:black">e.<span style="font-size:7pt;line-height:normal;font-family:'Times New Roman'"> </span></span><span style="color:black">Big Data as a Service<span></span></span></p>
<p class="gmail-MsoListParagraphCxSpMiddle" style="margin:0in 0in 0.05in 0.75in;line-height:15.6pt"><span style="color:black">f.<span style="font-size:7pt;line-height:normal;font-family:'Times New Roman'"> </span></span><span style="color:black">Big Data Industry Standards<span></span></span></p>
<p class="gmail-MsoListParagraphCxSpLast" style="margin:0in 0in 0.05in 0.75in;line-height:15.6pt"><span style="font-family:Verdana">g.<span style="font-size:7pt;line-height:normal;font-family:'Times New Roman'">
</span></span><span style="color:black">Experiences with Big Data Project Deployments</span><span style="font-family:Verdana"><span></span></span></p>
<p class="MsoNormal" style="text-align:justify"><span style="font-family:Verdana"><span> </span></span></p>
<p class="MsoNormal"><b><span style="font-size:14pt">INDUSTRIAL Track<span></span></span></b></p>
<p class="MsoNormal" style="text-align:justify">The Industrial Track solicits
papers describing implementations of Big Data solutions relevant to industrial
settings. The focus of industry track is on papers that address the practical,
applied, or pragmatic or new research challenge issues related to the use of Big
Data in industry. We accept full papers (up to 10 pages) and extended
abstracts (2-4 pages).<span></span></p>
<p class="MsoNormal" style="text-align:justify"><i><span style="font-size:11.5pt"><span> </span></span></i></p>
<p class="MsoNormal" style="text-align:justify"><b><span style="font-size:14pt">Student
Travel Award<span></span></span></b></p>
<p class="MsoNormal" style="text-align:justify">IEEE
Big Data 2017 will offer<b> student travel </b>to student authors (including
post-docs) <span></span></p>
<p class="gmail-MsoListParagraph" style="margin-left:0.75in;text-align:justify"><span> </span></p>
<p class="MsoNormal" style="text-align:justify"><b><span style="font-size:14pt">Journal Publication <span></span></span></b></p>
<p class="MsoNormal" style="text-align:justify">A set of about 10 papers will be selected
for a fast-track review and then published at the IEEE Transactions on Big Data.<span style="font-family:Verdana"><span></span></span></p>
<p class="MsoNormal" style="text-align:justify"><span> </span></p>
<p class="MsoNormal" style="text-align:justify"><b><span lang="DE"> </span></b></p>
<p class="MsoNormal" style="text-align:justify"><b><u>Paper Submission</u>:<span></span></b></p>
<p class="gmail-default">Please submit a
full-length paper (up to <b><span style="color:red">10 page IEEE 2-column format</span></b>) through the online submission system. <span></span></p>
<p class="gmail-default"><a href="https://wi-lab.com/cyberchair/2017/bigdata17/scripts/submit.php?subarea=BigD">https://wi-lab.com/cyberchair/2017/bigdata17/scripts/submit.php?subarea=BigD</a><span></span></p>
<p class="gmail-default">Papers should be formatted to IEEE Computer Society
Proceedings Manuscript Formatting Guidelines (see link to "formatting
instructions" below).<br>
<span style="font-size:9pt;font-family:Arial"><br>
</span><strong>Formatting Instructions</strong><br>
8.5" x 11" (<a href="ftp://pubftp.computer.org/press/outgoing/proceedings/instruct8.5x11x2.doc">DOC</a>,
<a href="ftp://pubftp.computer.org/press/outgoing/proceedings/instruct8.5x11x2.pdf">PDF</a>)
<br>
<a href="ftp://pubftp.computer.org/Press/Outgoing/proceedings/IEEE_CS_Latex8.5x11x2.zip"><strong><span style="color:rgb(0,28,81)">LaTex Formatting Macros</span></strong></a><span></span></p>
<p class="MsoNormal" style="text-align:justify"><b>Important Dates:<span></span></b></p>
<p class="MsoNormal" style="text-align:justify"><span> </span></p>
<p class="MsoNormal" style="text-align:justify">Electronic
submission of full papers: August 20, 2017<span></span></p>
<p class="MsoNormal" style="text-align:justify">Notification
of paper acceptance: Oct 9, 2016<span></span></p>
<p class="MsoNormal" style="text-align:justify">Camera-ready
of accepted papers: Nov 10, 2017<span></span></p>
<p class="MsoNormal" style="text-align:justify">Conference:
Dec 11-14, 2017<span></span></p>
<p class="MsoNormal" style="text-align:justify"><span> </span></p>
<p class="MsoNormal" style="text-align:justify"><span> </span></p>
<h4 style="margin:0in 0in 0.0001pt"><span style="font-family:Lato">Conference Co-Chairs<span></span></span></h4>
<p style="margin:0in 0in 0.0001pt"><strong>Dr.
Ricardo Baeza-Yates</strong><span style="font-family:'Times New Roman'"> :
NTENT, USA<br>
<strong>Prof. Xiaohua Tony Hu</strong> : Drexel University, USA<br>
<strong>Dr. Jeremy Kepner</strong> : MIT Lincoln Laboratory, USA<span></span></span></p>
<p class="MsoNormal" style="text-align:justify"><span> </span></p>
<p class="MsoNormal" style="text-align:justify"><b>PC Co-Chairs<span></span></b></p>
<p class="MsoNormal" style="text-align:justify">Prof.
Jian-Yun Nie, University of Montreal, Canada<span></span></p>
<p class="MsoNormal" style="text-align:justify">Prof.
Zoran Obradovic, Temple University, USA<span></span></p>
<p class="MsoNormal" style="text-align:justify">Prof.
Toyotaro Suzumura, IBM Research, USA<span></span></p>
<p class="MsoNormal" style="text-align:justify"><span> </span></p>
<p class="MsoNormal" style="text-align:justify"><b>Industry and Government Co-Chairs:<span></span></b></p>
<p class="MsoNormal" style="text-align:justify">Dr. Raghunath
Nambiar, CISCO, USA<span></span></p>
<p class="MsoNormal" style="text-align:justify">Dr.
Sudarsan Rachuri, Dept of Energy, USA<span></span></p>
<p class="MsoNormal" style="text-align:justify">Dr. Ghosh
Rumi, BOSCH, USA <span></span></p>
<p class="MsoNormal" style="text-align:justify">Dr. Chonggang
Wang, <span style="color:black">InterDigital, Inc</span>USA<span></span></p>
<p class="MsoNormal" style="text-align:justify">Dr. Hui
Zang, Huawei Research, USA</p>
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