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---
layout: page
title: Advanced Tools for Data Analytics
subtitle: Workshop at ADCHEM 2018 on July 24<sup>th</sup>, 2018 in Shengyang, China<br>Royal Wanxin Hotel, Room 6F-8
use-site-title: true
permalink: "/"
---
{% assign members = site.members | sort: 'weight' %}
<div class="container-fluid" role="main">
<div class="row">
<div class="container-fluid col-sm-3" id="guest_organizers" style="margin-top: 20px;">
<!-- IMAGES AND BIOS -->
<h4 class="navlink"><u><a href="#overview">Workshop Overview</a></u></h4>
<h4 class="navlink"><u><a href="#guests">Guest Speakers</a></u></h4>
<h4 class="navlink"><u><a href="#organizers">Organizers</a></u></h4>
<h4 class="navlink"><u><a href="#courseplan">Course Plan</a></u></h4>
<h4 class="navlink"><u><a href="#schedule">Schedule</a></h4></u>
<h4 class="navlink"><u><a href="#software">Software</a></h4></u>
<h4 class="navlink"><u><a href="#abstracts">Abstracts</a></u></h4>
</div>
<div class="container-fluid col-sm-8">
<h2 class="post-title" style="font-weight: 800">Description</h2>
<p class="post-entry" style="text-align: left"> This workshop will introduce the essential machine learning algorithms and software tools for graduate students, experienced researchers and engineers working in the industry. Elementary knowledge of probability and statistics is required to attend this workshop. The workshop will also feature at least two confirmed guest speakers with decades of experience in data analytics. Prof. Sirish L. Shah of University of Alberta and Prof. Richard D. Braatz of Massachusetts Institute of Technology have both agreed to speak during the workshop. We have also invited an industrial guest speaker.</p>
</div>
</div>
<br>
<h2 class="post-title" style="font-weight: 800" name="overview"><a name="overview"></a>Workshop Overview</h2><hr>
<p class="post-entry" style="text-align: left">We are currently at the cusp of what is considered the fourth industrial revolution. This revolution is driven by the ubiquitous cyber-physical systems, algorithmic developments in artificial intelligence, gargantuan computing power, inexpensive memory and the gigantic volumes of data that are being collected. The process industries are in possession of treasure troves of heterogenous data that is gravely under utilized. The competitive global environment, and the ever increasing demands on energy, environment and quality are subjecting these industries to a high level of economic pressure. The incredible volumes of data that they already possess are poised to provide a level of automation and efficiency never seen before and thus alleviate the economic and competitive pressures.<br><br>Process industries have been using data analytics in various forms for more than three decades. In particular, statistical techniques such as principal component analysis (PCA), partial least squares (PLS), canonical variate analysis (CVA) and time series methods for modeling such as maximum likelihood estimation, prediction error methods have been extensively applied on industrial data. The recent developments in machine learning and artificial intelligence provide a new opening for using process data on large scale problems. However, in order to successfully apply machine learning methods to process data, researchers require not only a high level understanding of the algorithms but also strong programming knowledge in packages such as Python, TensorFlow, Keras and Jupyter.</p>
<h2 class="post-title" style="font-weight: 800"><a name="guests"></a>Guest Speakers</h2><hr>
<div class="row biorow">
<div class="container-fluid col-sm-3" id="guest_organizers">
<!-- IMAGES AND BIOS -->
<img alt="Dr. Richard Braatz" src="img/braatz.jpg" class="onscroll-image-fade-in img-team bioimg">
<h4><b>Dr. Richard Braatz</b><br></h4>
</div>
<div class="container-fluid col-sm-9">
<p class="post-entry biography">Dr. Richard D. Braatz is the Edwin R. Gilliland Professor of Chemical Engineering at the Massachusetts Institute of Technology (MIT) where he does research in applied mathematics and control theory and their application to chemical and biological systems. He received an MS and PhD from the California Institute of Technology and was the Millennium Chair and Professor at the University of Illinois at Urbana-Champaign and a Visiting Scholar at Harvard University before moving to MIT. He has consulted or collaborated with more than 20 companies including IBM, United Technologies Corporation, Novartis, and Abbott Laboratories. Honors include the Donald P. Eckman Award from the American Automatic Control Council, the Curtis W. McGraw Research Award from the Engineering Research Council, and the AIChE Computing in Chemical Engineering Award. He is a Fellow of the Institute of Electrical and Electronics Engineers, International Federation of Automatic Control, and the American Association for the Advancement of Science. For more information, see Dr. Braatz's <a href="https://cheme.mit.edu/profile/richard-d-braatz/">page</a>.</p>
</div>
</div>
<div class="row biorow">
<div class="container-fluid col-sm-3" id="guest_organizers">
<!-- IMAGES AND BIOS -->
<img alt="Dr. Sirish Shah" src="img/shah.jpg" class="onscroll-image-fade-in img-team bioimg">
<h4><b>Dr. Sirish Shah</b><br></h4>
</div>
<div class="container-fluid col-sm-9">
<p class="post-entry biography">Dr. Sirish L. Shah has been with the University of Alberta since 1978, where he held the NSERC-Matrikon-Suncor-iCORE Senior Industrial Research Chair in Computer Process Control from 2000 to 2012. He is the recipient of the Albright & Wilson Americas Award in 1989, the Killam Professor in 2003, the D.G. Fisher Award for significant contributions in the field of systems and control, the ASTECH award in 2011 and the 2015-IEEE Transition to Practice Award. He has held visiting appointments at Oxford University and Balliol College as a SERC fellow, Kumamoto University (Japan) as a senior research fellow of the Japan Society for the Promotion of Science (JSPS), the University of Newcastle, Australia, IIT-Madras India and the National University of Singapore. The main areas of his current research are process and performance monitoring, analysis and rationalization of alarm systems. He has co-authored three books, the first titled, Performance Assessment of Control Loops: Theory and Applications, a second titled ‘Diagnosis of Process Nonlinearities and Valve Stiction: Data Driven Approaches”, and a more recent monograph on “Capturing connectivity and causality in complex industrial processes”. He is emeritus professor at the University of Alberta, a fellow of the Canadian Academy of Engineering and the Chemical Institute of Canada. For more information, visit <a href="http://www.eche.ualberta.ca/~slshah/">Sirish Shah's page</a>.</p>
</div>
</div>
<h2 class="post-title" style="font-weight: 800"><a name="organizers"></a>Organizers</h2><hr>
<div class="row biorow">
<div class="container-fluid col-sm-3" id="guest_organizers">
<!-- IMAGES AND BIOS -->
<img alt="Dr. Bhushan Gopaluni" src="img/bhushan.jpg" class="onscroll-image-fade-in img-team bioimg">
<h4 style="padding-right: 15px"><b>Dr. Bhushan Gopaluni</b><br></h4>
</div>
<div class="container-fluid col-sm-9">
<p class="post-entry biography" style="vertical-align: center;">Dr. Bhushan Gopaluni is a professor in the department of chemical and biological engineering and an Associate Dean for Education and Professional Development in the faculty of Applied Science at the University of British Columbia. He is also an associate faculty in the Institute of Applied Mathematics, the Institute for Computing, Information and Cognitive Systems, Pulp and Paper Center and the Clean Energy Research Center. He is currently an associate editor for Journal of Process Control, The Journal of Franklin Institute, guest editor for Process Control Special Series in the Canadian Journal of Chemical Engineering. He received a Ph.D. from the University of Alberta in 2003 and a Bachelor of Technology from the Indian Institute of Technology, Madras in 1997 both in the filed of chemical engineering. From 2003 to 2005 he worked as an engineering consultant at Matrikon Inc. (now Honeywell Process Solutions) during which he had designed and commissioned multivariable controllers in British Columbia’s pulp and paper industry, and had implemented numerous controller performance monitoring projects in the Oil & Gas and other chemical industries. He is one of the leading experts on data analytics for process industry and has authored over 110 refereed articles in reputed international Journals and conferences. His publications have been recognized through best paper awards and keynote presentations. He is also the recipient of the prestigious Killam Teaching Prize and the Dean’s service medal from the University of British Columbia. For more information, visit the <a href="https://dais.chbe.ubc.ca/">DAIS page</a>.</p>
</div>
</div>
<div class="row biorow">
<div class="container-fluid col-sm-3" id="guest_organizers">
<!-- IMAGES AND BIOS -->
<img alt="Lee Rippon" src="img/lee.jpg" class="onscroll-image-fade-in img-team bioimg">
<h4><b>Lee Rippon</b><br></h4>
</div>
<div class="container-fluid col-sm-9">
<p class="post-entry biography">Lee Rippon is a PhD student studying Chemical and Biologial Engineering (CHBE) at UBC. He also holds BASc and MASc degrees from UBC in CHBE where his research experience includes applications of compressive sensing, adaptive control, system identification and process monitoring on sheet and film processes. His current research interests include applying statistical machine learning techniques to historical process data to perform fault detection, isolation, and diagnosis in a kraft process. For more information, visit the <a href="https://dais.chbe.ubc.ca/">DAIS page</a>.</p>
</div>
</div>
<div class="row biorow">
<div class="container-fluid col-sm-3" id="guest_organizers">
<!-- IMAGES AND BIOS -->
<img alt="Yiting Tsai" src="img/yt_new.png" class="onscroll-image-fade-in img-team bioimg">
<h4><b>Yiting Tsai</b><br></h4>
</div>
<div class="container-fluid col-sm-9">
<p class="post-entry biography">Yiting Tsai has finished both his BASc and MASc degrees at CHBE. His interests are process control and statistical modeelling of time-series data. His current PhD research focuses on the application of Machine Learning techniques to design smart controllers, which identify and predict process faults ahead of time and apply appropriate control actions to prevent such faults. For more information, visit the <a href="https://dais.chbe.ubc.ca/">DAIS page</a></p>
</div>
</div>
<div class="row biorow">
<div class="container-fluid col-sm-3" id="guest_organizers">
<!-- IMAGES AND BIOS -->
<img alt="Dr. Aditya Tulsyan" src="img/aditya2.png" class="onscroll-image-fade-in img-team bioimg">
<h4><b>Dr. Aditya Tulsyan</b><br></h4>
</div>
<div class="container-fluid col-sm-9">
<p class="post-entry biography">Dr. Aditya Tulsyan is currently a Senior Engineer at Amgen. Prior to joining Amgen in 2016, Aditya was a Postdoctoral Associate in the Process Systems Engineering Laboratory at the Massachusetts Institute of Technology. He received his Ph.D. in Computer Process Control from the University of Alberta, Canada in 2013. He has held research positions at the National University of Singapore, University of British Columbia and the Indian Institute of Technology, Kharagpur. His research interests are in systems engineering, statistical machine learning, signal processing and Bayesian inference. For more information, visit <a href="http://www.mit.edu/~tulsyan/Home.html">Dr. Tulsyan's page</a>.</p>
</div>
</div>
<h2 class="post-title" style="font-weight: 800"><a name="courseplan"></a>Course Plan</h2><hr>
<div class="row">
<div class="container-fluid">
<p class="post-entry" style="padding:10px">Starting with an elementary introduction to statistics and probability, we will develop various regression, classification, dimensionlity reduction and advanced learning algorithms that are of interest to engineers. In addition, various widely-used machine learning software packages will be introduced. Registrants will solve exercises and receive take-away software code to implement these algorithms. The following is a general outline of the course:</p><br>
</div>
</div>
<div class="row">
<ol>
<div class="container-fluid col-sm-6">
<li class="post-entry courseplan" style="padding-right: 15px"><b>Basics of probability and statistics, underfitting, overfitting and bias-variance tradeoff</b></li><br>
<li class="post-entry courseplan"><b>Classification Algorithms</b>
<ul>
<li class="post-entry courseplan">Support Vector Machines</li>
<li class="post-entry courseplan">Naive Bayes Classifier</li>
</ul>
</li><br>
<li class="post-entry courseplan"><b>Regression Algorithms</b>
<ul>
<li class="post-entry courseplan">Linear Least Squares</li>
<li class="post-entry courseplan">Kernel Regression</li>
</ul>
</li><br>
</div>
<div class="container-fluid col-sm-6">
<li class="post-entry courseplan"><b>
Dimensionality Reduction Algorithms</b>
<ul>
<li class="post-entry courseplan">Principal Component Analysis (PCA)</li>
<li class="post-entry courseplan">Partial Least Squares (PLS)</li>
<li class="post-entry courseplan">Isometric Mapping (ISOMAP)</li>
</ul>
</li><br>
<li class="post-entry courseplan"><b>Advanced Learning Algorithms</b>
<ul>
<li class="post-entry courseplan">Deep Learning</li>
<li class="post-entry courseplan">Recurrent Neural Networks</li>
<li class="post-entry courseplan">Gaussian Processes</li>
</ul>
</li><br>
<li class="post-entry courseplan"><b>Applications in the Process Industry</b></li><br>
</div>
</ol>
</div>
<h2 class="post-title" style="font-size: 18pt; vertical-align: top">Learning Outcomes</h2>
<p class="post-entry">By the end of this workshop, registrants will be able to:
<ul>
<li>identify and solve classification, regression and dimensionality reduction problems</li>
<li>work with softwares such as Python, TensorFlow, and Keras</li>
</ul></p>
<br>
<h2 class="post-title" style="font-weight: 800"><a name="schedule"></a>Schedule</h2><br>
<div class="row">
<div class="container-fluid col-sm-4">
<p class="post-entry schedulecoltext">8:30AM - 9:30AM</p>
</div>
<div class="container-fluid col-sm-4">
<p class="post-entry tabletext"><b>Richard Braatz</b></p>
</div>
<div class="container-fluid col-sm-4">
<p class="post-entry tabletext">Big Data Analytics</p>
</div>
</div><hr>
<div class="row">
<div class="container-fluid col-sm-4">
<p class="post-entry schedulecoltext">9:30AM - 10:30AM</p>
</div>
<div class="container-fluid col-sm-4">
<p class="post-entry tabletext"><b>Bhushan Gopaluni</b></p>
</div>
<div class="container-fluid col-sm-4">
<p class="post-entry tabletext">Classification</p>
</div>
</div><hr>
<div class="row">
<div class="container-fluid col-sm-4">
<p class="post-entry schedulecoltext">10:30AM - 11:30AM</p>
</div>
<div class="container-fluid col-sm-4">
<p class="post-entry tabletext"><b>Yiting Tsai</b></p>
</div>
<div class="container-fluid col-sm-4">
<p class="post-entry tabletext">Regression</p>
</div>
</div><hr>
<div class="row">
<div class="container-fluid col-sm-4">
<p class="post-entry schedulecoltext">11:30AM - 12:30PM</p>
</div>
<div class="container-fluid col-sm-4">
<p class="post-entry tabletext"><b>Bhushan Gopaluni, Yiting Tsai</b></p>
</div>
<div class="container-fluid col-sm-4">
<p class="post-entry tabletext">Dimensionality Manipulation</p>
</div>
</div><hr>
<div class="row">
<div class="container-fluid col-sm-4">
<p class="post-entry schedulecoltext">12:30PM - 1:30PM</p>
</div>
<div class="container-fluid col-sm-4">
<p class="post-entry tabletext"><b>Lunch</b></p>
</div>
<div class="container-fluid col-sm-4">
<p class="post-entry tabletext"></p>
</div>
</div><hr>
<div class="row">
<div class="container-fluid col-sm-4">
<p class="post-entry schedulecoltext">1:30PM - 2:30PM</p>
</div>
<div class="container-fluid col-sm-4">
<p class="post-entry tabletext"><b>Sirish Shah</b></p>
</div>
<div class="container-fluid col-sm-4">
<p class="post-entry tabletext">Alarm Management</p>
</div>
</div><hr>
<div class="row">
<div class="container-fluid col-sm-4">
<p class="post-entry schedulecoltext">2:30PM - 5:30PM</p>
</div>
<div class="container-fluid col-sm-4">
<p class="post-entry tabletext"><b>Bhushan Gopaluni, Aditya Tulsyan, Yiting Tsai</b></p>
</div>
<div class="container-fluid col-sm-4">
<p class="post-entry tabletext">Advanced Learning Algorithms</p>
</div>
</div>
<br>
<h2 class="post-title" style="font-weight: 800" name="software"><a name="software"></a>Software</h2><hr>
<p class="post-entry" style="text-align: left">This workshop will be using Python code in Jupyter notebooks. If you would like to follow along with the code during the workshop, it is recommended that you install the required software.<br><br>For a guide to installation, follow the <b>TensorFlow</b> pages provided below. The TensorFlow page contains instructions on setting up the environment properly.<br></p>
<h3 class="post-title" style="font-weight:700">Mac Installation</h3><hr>
<div class="row">
<div class="container-fluid col-sm-4">
<p class="software-entry"><a href="https://tensorflow.org/install/install_mac">TensorFlow</a></p>
</div>
<div class="container-fluid col-sm-4">
<p class="software-entry"><a href="https://python.org/downloads/mac-osx/">Python</a></p>
</div>
<div class="container-fluid col-sm-4">
<p class="software-entry"><a href="https://conda.io/docs/user-guide/install/macos.html">Anaconda</a></p>
</div>
</div>
<h3 class="post-title" style="font-weight:700">Windows Installation</h3><hr>
<div class="row">
<div class="container-fluid col-sm-4">
<p class="software-entry"><a href="https://tensorflow.org/install/install_windows">TensorFlow</a></p>
</div>
<div class="container-fluid col-sm-4">
<p class="software-entry"><a href="https://python.org/downloads/windows/">Python</a></p>
</div>
<div class="container-fluid col-sm-4">
<p class="software-entry"><a href="https://conda.io/docs/user-guide/install/windows.html">Anaconda</a></p>
</div>
</div>
<h3 class="post-title" style="font-weight:700">Ubuntu Installation</h3><hr>
<div class="row">
<div class="container-fluid col-sm-6">
<p class="software-entry"><a href="https://tensorflow.org/install/install_linux">TensorFlow</a></p>
</div>
<div class="container-fluid col-sm-6">
<p class="software-entry"><a href="https://conda.io/docs/user-guide/install/linux.html">Anaconda</a></p>
</div>
</div>
<div class="row">
<h2 class="post-title" style="font-weight: 800"><a name="abstracts"></a>Abstracts</h2><hr>
<div class="posts-list">
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