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<!DOCTYPE html>
<!--[if lt IE 8 ]><html class="no-js ie ie7" lang="en"> <![endif]-->
<!--[if IE 8 ]><html class="no-js ie ie8" lang="en"> <![endif]-->
<!--[if (gte IE 8)|!(IE)]><!--><html class="no-js" lang="en"> <!--<![endif]-->
<head>
<!--- Basic Page Needs
================================================== -->
<meta charset="utf-8">
<title>Wei Zheng</title>
<meta name="description" content="">
<meta name="author" content="">
<!-- Mobile Specific Metas
================================================== -->
<meta name="viewport" content="width=device-width, initial-scale=1, maximum-scale=1">
<!-- CSS
================================================== -->
<link rel="stylesheet" href="css/default.css">
<link rel="stylesheet" href="css/layout.css">
<link rel="stylesheet" href="css/media-queries.css">
<link rel="stylesheet" href="css/magnific-popup.css">
<!-- Script
================================================== -->
<script src="js/modernizr.js"></script>
<!-- Favicons
================================================== -->
<link rel="shortcut icon" href="favicon.png" >
</head>
<body>
<!-- Header
================================================== -->
<header id="home">
<nav id="nav-wrap">
<a class="mobile-btn" href="#nav-wrap" title="Show navigation">Show navigation</a>
<a class="mobile-btn" href="#" title="Hide navigation">Hide navigation</a>
<ul id="nav" class="nav">
<li class="current"><a class="smoothscroll" href="#home">Home</a></li>
<li><a class="smoothscroll" href="#portfolio">Projects</a></li>
<li><a class="smoothscroll" href="#resume">Resume</a></li>
<li><a class="smoothscroll" href="#about">About</a></li>
</ul> <!-- end #nav -->
</nav> <!-- end #nav-wrap -->
<div class="row banner">
<div class="banner-text">
<h1 class="responsive-headline" style="font-size: 40px">I'm Wei Zheng</h1>
<br>
<br>
<h3 style="font-size: 20px">
A programmer, a electronic device enthusiast and a video games lover
</h3>
<br>
<br>
<ul class="social">
<li><a target="_blank" href="https://github.com/IvanWeiZ"><i class="fa fa-lg fa-github"></i></a></li>
<li><a target="_blank" href="https://www.linkedin.com/in/wei-ivan-zheng"><i class="fa fa-lg fa-linkedin"></i></a></li>
<li><a target="_blank" href="mailto:w27zheng@uwaterloo.ca"><i class="fa fa-lg fa-envelope"></i></a></li>
</ul>
</div>
</div>
<p class="scrolldown">
<a class="smoothscroll" href="#portfolio"><i class="icon-down-circle"></i></a>
</p>
</header> <!-- Header End -->
<!-- Portfolio Section
================================================== -->
<section id="portfolio">
<div class="row">
<div class="twelve columns collapsed">
<h1>Check Out Some of My Works.</h1>
<!-- portfolio-wrapper -->
<div id="portfolio-wrapper" class="bgrid-quarters s-bgrid-thirds cf">
<div class="columns portfolio-item">
<div class="item-wrap">
<a href="#modal-01" title="">
<img alt="" src="images/1.png">
<div class="overlay">
<div class="portfolio-item-meta">
<h5>Act-Function-ConvNets</h5>
<p>Evaluation of Activation Functions on Convolutional Networks</p>
</div>
</div>
<div class="link-icon"><i class="icon-plus"></i></div>
</a>
</div>
</div>
<div class="columns portfolio-item">
<div class="item-wrap">
<a href="#modal-02" title="">
<img alt="" src="images/2.png">
<div class="overlay">
<div class="portfolio-item-meta">
<h5>HARU</h5>
<p>Human activity and schedule recognition using sensors and GPS on smartphones</p>
</div>
</div>
<div class="link-icon"><i class="icon-plus"></i></div>
</a>
</div>
</div>
<div class="columns portfolio-item">
<div class="item-wrap">
<a href="#modal-03" title="">
<img alt="" src="images/3.jpg">
<div class="overlay">
<div class="portfolio-item-meta">
<h5>Containment-Join-Data</h5>
<p>Evaluated set similarity join techniques using the containment similarity index on open data to find joinable tables at scale</p>
</div>
</div>
<div class="link-icon"><i class="icon-plus"></i></div>
</a>
</div>
</div>
<div class="columns portfolio-item">
<div class="item-wrap">
<a href="#modal-04" title="">
<img alt="" src="images/portfolio/black.png">
<div class="overlay">
<div class="portfolio-item-meta">
<h5>Quadris</h5>
<p>Tetris developed in C++ </p>
</div>
</div>
<div class="link-icon"><i class="icon-plus"></i></div>
</a>
</div>
</div>
<div class="columns portfolio-item">
<div class="item-wrap">
<a href="#modal-05" title="">
<img alt="" src="images/portfolio/black.png">
<div class="overlay">
<div class="portfolio-item-meta">
<h5>Text Generation On Grammar Variational Autoencoder</h5>
<p>Applied GVAE to generate syntactically valid sentences by considering English as a context free grammar. </p>
</div>
</div>
<div class="link-icon"><i class="icon-plus"></i></div>
</a>
</div>
</div>
<div class="columns portfolio-item">
<div class="item-wrap">
<a href="#modal-06" title="">
<img alt="" src="images/portfolio/black.png">
<div class="overlay">
<div class="portfolio-item-meta">
<h5>Selective Single Image Super Resolution Using Semantic Segmentation</h5>
<p>Single image super resolution on Cityscapes dataset using deep CNN</p>
</div>
</div>
<div class="link-icon"><i class="icon-plus"></i></div>
</a>
</div>
</div>
</div> <!-- portfolio-wrapper end -->
</div> <!-- twelve columns end -->
<!-- Modal Popup
==================================================-->
<div id="modal-01" class="popup-modal mfp-hide">
<!-- <img class="scale-with-grid" src="images/portfolio/modals/m-coffee.jpg" alt="" /> -->
<div class="description-box">
<h4>Act-Function-ConvNets</h4>
<p>The choice of activation function may have a significant impact on the training speed and prediction accuracy of neural network classifiers. Many new activation functions claim to hold performance advantages over commonly used activation function ReLU. In this study, we attempt to verify the results by conducting exploratory experimentation on various activation functions including Swish, ReLU, Leaky ReLU, ELU, and Tanh. We conclude desired properties of an activation function. Additionally, we propose a new activation function based on characteristics from Swish and ReLU. Experiments were performed using models of varying complexity including fully connected networks, simple CNNs, ResNet, and WRN. We use MNIST, SVHN, CIFAR-10 and CIFAR-100 in our experiments. Our findings show that for network dimensions and problem difficulties tested, Swish did not show any considerable advantage over ReLU while our newly proposed activation function is competitive with ReLU in several cases.</p>
<span class="categories"><i class="fa fa-tag"></i>Deep Learning</span>
</div>
<div class="link-box">
<a target="_blank" href="https://github.com/IvanWeiZ/Act-Function-ConvNets">GitHub</a>
<a style="margin-left: 24px" target="_blank" href="https://github.com/IvanWeiZ/Act-Function-ConvNets/blob/master/evaluation-activation-functions.pdf">Report</a>
<a class="popup-modal-dismiss">Close</a>
</div>
</div><!-- modal-01 End -->
<div id="modal-02" class="popup-modal mfp-hide">
<!-- <img class="scale-with-grid" src="imagses/portfolio/modals/m-console.jpg" alt="" /> -->
<div class="description-box">
<h4>HARU</h4>
<p>Equipped with many sensors, mobile phones bring much convenience to people’s daily life. We propose HARU, which performs accurate activity recognition based on continuous collected accelerometer data, integrates the results with GPS data, and visually displays users daily life track. With real-time monitoring, HARU enables plentiful possibilities to live in a more intelligent way such as site recommendation and calorie consumption summary.</p>
<span class="categories"><i class="fa fa-tag"></i>Machine Learning, Mobile Computing, Cloud Computing</span>
</div>
<div class="link-box">
<a target="_blank" href="https://github.com/IvanWeiZ/HARU">GitHub</a>
<a style="margin-left: 24px" target="_blank" href="https://github.com/IvanWeiZ/HARU/blob/master/haru.pdf">Report</a>
<a class="popup-modal-dismiss">Close</a>
</div>
</div><!-- modal-02 End -->
<div id="modal-03" class="popup-modal mfp-hide">
<!-- <img class="scale-with-grid" src="images/portfolio/modals/m-judah.jpg" alt="" /> -->
<div class="description-box">
<h4>Set Similarity Join Techniques On The Containment Similarity</h4>
<p>In this work, we investigate set similarity join techniques on the containment similarity function, finding all pairs of sets for which containment similarity score is above a given threshold. We implement existing set similarity join algorithms on the containment similarity function. We show the performances of all algorithms on datasets with different characteristics across a wide set of thresholds. The containment function gives different result compare to the Jaccard function due to unique characteristics of the containment function. Moreover, the containment similarity function produces a huge number candidate pairs and output pairs. The prefix filters and the length filters are the two fundamental filters, and they do not work well on the containment function. Algorithms with complex filters can relief the inefficiency of the two filters.</p>
<span class="categories"><i class="fa fa-tag"></i>Database</span>
</div>
<div class="link-box">
<a target="_blank" href="https://github.com/IvanWeiZ/Containment-Join-Data">GitHub</a>
<a style="margin-left: 24px" target="_blank" href="https://github.com/IvanWeiZ/Containment-Join-Data/blob/master/set-similarity-join.pdf">Report</a>
<a class="popup-modal-dismiss">Close</a>
</div>
</div><!-- modal-03 End -->
<div id="modal-04" class="popup-modal mfp-hide">
<!-- <img class="scale-with-grid" src="images/portfolio/modals/m-judah.jpg" alt="" /> -->
<div class="description-box">
<h4>Quadris</h4>
<p>Tetris developed in C++</p>
<span class="categories"><i class="fa fa-tag"></i>Game</span>
</div>
<div class="link-box">
<a target="_blank" href="https://github.com/IvanWeiZ/Quadris">GitHub</a>
<a class="popup-modal-dismiss">Close</a>
</div>
</div><!-- modal-03 End -->
<div id="modal-05" class="popup-modal mfp-hide">
<!-- <img class="scale-with-grid" src="images/portfolio/modals/m-judah.jpg" alt="" /> -->
<div class="description-box">
<h4>Text Generation On Grammar Variational Autoencoder</h4>
<p>Despite the big success of generative models on continuous data like images, those models on complex discrete data such as English sentences still have challenges, as the current methods often produce syntactically invalid outputs. Inspired by the results of the Grammar VAE (GVAE) on generating molecules and arithmetic expressions, we apply GVAE to generate sentences by considering English as a context free grammar. GVAE ensures the generated string is always valid by directly encoding and decoding to and from parse tree of the sentences. We show that GVAE generates valid English sentences, and it learns a smoother and more coherent latent space compared to Character VAE. GVAE also has a very high reconstruction rate with nearby points in the latent space decode to the similar outputs.</p>
<span class="categories"><i class="fa fa-tag"></i>Deep Learning, NLP</span>
</div>
<div class="link-box">
<!-- <a target="_blank" href="https://github.com/IvanWeiZ/Containment-Join-Data">GitHub</a> -->
<!-- <a style="margin-left: 24px" target="_blank" href="https://github.com/IvanWeiZ/Containment-Join-Data/blob/master/set-similarity-join.pdf">Report</a> -->
<a class="popup-modal-dismiss">Close</a>
</div>
</div><!-- modal-05 End -->
<div id="modal-06" class="popup-modal mfp-hide">
<!-- <img class="scale-with-grid" src="images/portfolio/modals/m-judah.jpg" alt="" /> -->
<div class="description-box">
<h4>Selective Single Image Super Resolution Using Semantic Segmentation</h4>
<p>Single image super resolution is an image enhancement task that improves low resolution image to high resolution image by adding high-frequency information. The technique improves the sharpness of the image during upsampling. Current methods apply super resolution to the whole image. However, most of the time we only want to get a higher resolution of the foreground of the image like objects or instances. We propose a method to apply a deep convolution neural network to selected instance classes of the image. The model takes semantic segmentation of the image as input in addition to the low resolution image and applies super resolution on the selected objects. Our model has been tested on the Cityscapes and PASCAL VOC2012 dataset. On both datasets, our model improves the super resolution processing speed by over 1.85 times. Also, we show that incorporating semantic information is beneficial for image quality in terms of PSNR. We believe that this work shows excellent potential for using semantic information in future super resolution tasks.</p>
<span class="categories"><i class="fa fa-tag"></i>Deep Learning, Computer Vision</span>
</div>
<div class="link-box">
<!-- <a target="_blank" href="https://github.com/IvanWeiZ/Containment-Join-Data">GitHub</a> -->
<!-- <a style="margin-left: 24px" target="_blank" href="https://github.com/IvanWeiZ/Containment-Join-Data/blob/master/set-similarity-join.pdf">Report</a> -->
<a class="popup-modal-dismiss">Close</a>
</div>
</div><!-- modal-06 End -->
</div> <!-- row End -->
</section> <!-- Portfolio Section End-->
<!-- Resume Section
================================================== -->
<section id="resume">
<!-- Education ==================================================-->
<div class="row education">
<div class="three columns header-col">
<h1><span>Education</span></h1>
</div>
<div class="nine columns main-col">
<div class="row item">
<div class="twelve columns">
<h4>Master of Science in Applied Computing</h3>
<p class="info">University of Toronto<span>•</span> Sep 2017 - Dec 2018 (expected)</p>
<p style="font-weight:bold;margin: 0">Courses</p>
<ul.circle>
<li>Machine Learning and Data Mining (A+)</li>
<li>Research Topics in Database Management-Open Data Science (A+)</li>
<li>Advanced Topics in Mobile and Pervasive Computing: Cloud Computing (A+)</li>
<li>Human-Computer Interaction (A+)</li>
<li>Current Algorithms and Techniques in Machine Learning (A+)</li>
<li>Machine Learning in Computer Vision (A+)</li>
</ul>
</div>
</div> <!-- item end -->
<br>
<div class="row item">
<div class="twelve columns">
<h4>Bachelor of Mathematics</h3>
<p class="info">University of Waterloo<span>•</span> <em class="date">Sep 2012 - Apr 2017</em></p>
<p class="info">Double Major in Computer Science <span>&</span> Financial Analysis and Risk Management</p>
<ul.circle>
<li>
Graduated with distinction and achieved over 90% average in upper-year computer science courses
</li>
</ul.circle>
<br>
</div>
</div> <!-- item end -->
</div> <!-- main-col end -->
</div> <!-- End Education -->
<!-- Work
================================================== -->
<div class="row work">
<div class="three columns header-col">
<h1><span>Work</span></h1>
</div>
<div class="nine columns main-col">
<div class="row item">
<div class="twelve columns">
<h4>Data Scientist Intern</h3>
<p class="info">Loblaw Digital<span>•</span> <em class="date">May 2018 - Current</em></p>
<p>
</p>
</div>
</div> <!-- item end -->
<div class="row item">
<div class="twelve columns">
<h4>Research Assistant (Part-time)</h3>
<p class="info">University of Waterloo<span>•</span> <em class="date">Jan 2017 - Sep 2017</em></p>
<p>
</p>
</div>
</div> <!-- item end -->
<div class="row item">
<div class="twelve columns">
<h4>Research Assistant (Part-time)</h3>
<p class="info">University of Waterloo<span>•</span> <em class="date">Sep 2016 - Dec 2016</em></p>
<p>
</p>
</div>
</div> <!-- item end -->
<div class="row item">
<div class="twelve columns">
<h4>Research Associate Co-op</h3>
<p class="info">BMO Capital Markets<span>•</span> <em class="date">Dec 2015 - Apr 2016</em></p>
<p>
</p>
</div>
</div> <!-- item end -->
<div class="row item">
<div class="twelve columns">
<h4>Fixed Income and Money Market Analyst Co-op</h3>
<p class="info">BMO Capital Markets<span>•</span> <em class="date">Sep 2015 - Dec 2015</em></p>
<p>
</p>
</div>
</div> <!-- item end -->
<div class="row item">
<div class="twelve columns">
<h4>Business Analyst-Trading Co-op</h3>
<p class="info">Scotiabank<span>•</span> <em class="date">Jan 2015 - May 2015</em></p>
<p>
</p>
</div>
</div> <!-- item end -->
<div class="row item">
<div class="twelve columns">
<h4>Electronic File Analyst Co-op</h3>
<p class="info">Manulife<span>•</span> <em class="date">May 2014 - Dec 2014</em></p>
<p>
</p>
</div>
</div> <!-- item end -->
<div class="row item">
<div class="twelve columns">
<h4>Developer - Information System Co-op</h3>
<p class="info">Manulife<span>•</span> <em class="date">Sep 2013 - Jan 2014</em></p>
<p>
</p>
</div>
</div> <!-- item end -->
</div> <!-- main-col end -->
</div> <!-- End Work -->
<div class="row work">
<div class="three columns header-col">
<h1><span>TAship</span></h1>
</div>
<div class="nine columns main-col">
<div class="row item">
<div class="twelve columns">
<h4>CSC373 Algorithm Design, Analysis and Complexity</h3>
<p class="info">University of Toronto<span>•</span> <em class="date">Fall 2018</em></p>
</div>
</div> <!-- item end -->
<div class="row item">
<div class="twelve columns">
<h4>CSC443 Database Systems Technology</h3>
<p class="info">University of Toronto<span>•</span> <em class="date">Winter 2018</em></p>
</div>
</div> <!-- item end -->
<div class="row item">
<div class="twelve columns">
<h4>MATH128</h3>
<p class="info">University of Waterloo<span>•</span> <em class="date">Winter 2017</em></p>
</div>
</div> <!-- item end -->
<div class="row item">
<div class="twelve columns">
<h4>MATH135</h3>
<p class="info">University of Waterloo<span>•</span> <em class="date">Fall 2017</em></p>
</div>
</div> <!-- item end -->
</div> <!-- main-col end -->
</div> <!-- End Work -->
<!-- Skills
==================================================-->
</section> <!-- Resume Section End-->
<!-- About Section
================================================== -->
<section id="about">
<div class="row">
<div class="two columns">
</div>
<div class="nine columns main-col">
<h2>About Me</h2>
<p>I grew up in Fuzhou China, and I immigrated to Canada in 2006. I went to Albert Campbell Collegiate Institute and Port Royal Public School.</p>
<h2>Resume and transcript available upon request.</h2>
</div> <!-- end .main-col -->
</div>
</section> <!-- About Section End-->
<!-- footer
================================================== -->
<footer>
<div class="row">
<div class="twelve columns">
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