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<!doctype html>
<html lang="en">
<head>
<!-- Required meta tags -->
<meta charset="utf-8">
<meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no">
<!-- Bootstrap CSS -->
<link rel="stylesheet" href="https://stackpath.bootstrapcdn.com/bootstrap/4.2.1/css/bootstrap.min.css" integrity="sha384-GJzZqFGwb1QTTN6wy59ffF1BuGJpLSa9DkKMp0DgiMDm4iYMj70gZWKYbI706tWS" crossorigin="anonymous">
<link rel="icon" href="imgs/InDeXLab.gif"/>
<title>Past Projects</title>
<link rel="stylesheet" type="text/css" href="ppl.css" />
<link rel="stylesheet" type="text/css" href="style.css" />
<script type="text/javascript" src="js/myobjects.js"></script>
<script type="text/javascript" src="js/indexstart.js"></script>
<script type="text/javascript">
function init() {
header();
}
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</head>
<body onload='init()'>
<div id="headerDiv"></div>
<!-- Your code starts here -->
<div class="container">
<div class="jumbotron jumbotron-fluid text-center" style="padding-block-start: 10pt;">
<img id="dataequity" class="roundphoto" src="imgs/Equity.png"/>
<h3 class="display-4">Data Equity Systems</h3>
<p class="lead">Projects related to Responsible Data Science and Algorithmic Fairness</p>
<img style="width: 90%;" src="pastprojects/imgs/fairness-overview.png"/>
<p><span class="label label-warning">Fig 1. Bias and Fairness in Data Analytics Pipeline</span></p>
<hr class="my-4">
<h1>Algorithmic Fairness</h1>
<div class="row">
<div class="col-sm-4"><div class="contentBox col-sm-10 text-center">
<h4><a href="pastprojects/fairranking.htm">Responsible Ranking</a></h4>
<p><i>Fairness and Stability</i></p>
<p><img src="pastprojects/assets/stability1.jpg" height="250pt"/></p>
</div></div>
<div class="col-sm-4"><div class="contentBox col-sm-10 text-center">
<h4><a href="fcs.htm">Fairness in Social Networks</a></h4>
<p><i>Equitable Content Spread</i></p>
<p><img src="pastprojects/assets/f-socialnet.jpg" height="250pt"/></p>
</div></div>
<div class="col-sm-4"><div class="contentBox col-sm-10 text-center">
<h4><a href="pastprojects/fml.htm">Fair Machine Learning</a></h4>
<p><i>In-processing Approaches</i></p>
<p><img src="pastprojects/assets/fml.jpg" height="250pt"/></p>
</div></div>
</div>
<div> </div>
<div class="row">
<div class="col-sm-4"><div class="contentBox col-sm-10 text-center">
<h4><a href="pastprojects/fmc.htm">Fair Max k-Cover</a></h4>
<p><i>.</i></p>
<p><img src="pastprojects/assets/fmc.png" height="220pt"/></p>
</div></div>
</div>
<div> </div>
<h1>Bias in Data</h1>
<div class="row">
<div class="col-sm-4"><div class="contentBox col-sm-10 text-center">
<h4><a href="pastprojects/coverage.htm">Representation Bias</a></h4>
<p><i>Data Coverage</i></p>
<p><img src="pastprojects/assets/coverage.png" height="250pt"/></p>
</div></div>
<div class="col-sm-4"><div class="contentBox col-sm-10 text-center">
<h4><a href="pastprojects/fdi.htm">Fairness-aware Data Integration</a></h4>
<p><i>Tailoring Data Distributions</i></p>
<p><img src="pastprojects/assets/fdi.png" height="220pt"/></p>
</div></div>
<div class="col-sm-4"><div class="contentBox col-sm-10 text-center">
<h4><a href="pastprojects/fqa.htm">Fairness-aware Query Answering</a></h4>
<p><img src="pastprojects/assets/frq.png" height="240pt"/></p>
</div></div>
</div>
<div class="text-left" style="padding: 1%;">
<h3>More Resources:</h3>
<ul>
<li>(Blog Post) Abolfazl Asudeh. <a target="_blank" href="http://wp.sigmod.org/?p=3174">Enabling Responsible Data Science in Practice</a>. <i>ACM SIGMOD Blog</i>, Jan. 2021.</li>
<li>(Tutorial) Fatemeh Nargesian, Abolfazl Asudeh, H. V. Jagadish. <a href="tutorial22.htm">Responsible Data Integration: Next-generation Challenges</a>. SIGMOD, 2022, ACM.</li>
<li>(Tutorial) Abolfazl Asudeh, HV Jagadish. <a target="_blank" href="tutorial20.htm">Fairly Evaluating and Scoring Items in a Data Set</a>. <i>PVLDB</i>, 2020, VLDB Endowment.</li>
<li>(Survey) Nima Shahbazi, Yin Lin, Abolfazl Asudeh, HV Jagadish. <a href="https://arxiv.org/abs/2204.07682" target="_blank" rel="noopener">Data-Centric Distrust Quantification for Responsible AI: When Data-driven Outcomes Are Not Reliable.</a> CoRR, 2022.</li>
<li>(Invited Paper) Abolfazl Asudeh, HV Jagadish, Julia Stoyanovich. <a target="_blank" href="http://sites.computer.org/debull/A19sept/issue1.htm">Towards Responsible Data-driven Decision Making in Score-Based Systems</a>. <i>Data Engineering Bulletin</i>, Vol. 42(3), pages 76--87, 2019, Special Issue on Fairness, Diversity, and Transparency in Data Systems.</li>
</ul>
</p>
</div>
</div>
<div class="jumbotron jumbotron-fluid text-center" style="padding-block-start: 10pt;">
<img id="factchecking" class="roundphoto" src="imgs/factke.jpg"/>
<h3 class="display-4"><a href="FactChecking.htm">Computational Fact Checking and Bias in Data Presentation</a></h3>
<p class="lead">Projects related to Computational Fact Checking</p>
<hr class="my-4">
<div class="text-left" style="padding: 1%;">
<p>
In this project, we particularly are interested to detect mileading information and statements made by <i>cherry-picking data</i>.
This is popular among politicians since they would like not to be caught blatantly lying.
That is why "<i>A lie which is half a truth is ever the blackest of lies</i>" (A.Tennyson).<br>
But how to computationally detect/measure "half a truth"?
We aim to address this question in this project.
</p>
<p>This project has been supported by the Google Scholar Award.</p>
<p>More details in <a href="FactChecking.htm">Project Page</a>.</p>
</div>
</div>
<div class="jumbotron jumbotron-fluid text-center" style="padding-block-start: 10pt;">
<img class="roundphoto" src="imgs/orca.JPG"/>
<h3 class="display-4"><a href="">Orca</a></h3>
<p class="lead">Projects related to Computation at Scale</p>
<hr class="my-4">
<div class="text-left" style="padding: 1%;">
<p> Scalability has always been a challenge in CS.<br/>
Just like Orcas who find smart ways to hunt the giants (watch these: <a href="https://www.youtube.com/watch?v=9DG8zCIBNh0" target="_blank">[1]</a>, <a href="https://www.youtube.com/watch?v=ZPjkHFD3bpA" target="_blank">[2]</a>),
we aim to design efficient and accurate algorithms that can solve problems at scale.
</p>
<p>More details in <a href="FactChecking.htm" >Project Page</a>.</p>
</div>
</div>
<div class="jumbotron jumbotron-fluid text-center" style="padding-block-start: 10pt;">
<img id="ranking" class="roundphoto" src="imgs/CH.jpg"/>
<h3 class="display-4"><a href="">Ranking & Representatives</a></h3>
<p class="lead">Projects related to Ranking and Top-k query processing; Skyline and Regret-Minimizing sets</p>
<hr class="my-4">
<div class="text-left" style="padding: 1%;">
<p>
Evaluating objects, ranking them, and selecting the <i>"best"</i>, is the key to decision making and a fundumental CS problem.
However, almost as critical it is, ranking has always been controversial.
Probably the main reason is that the concept of "best" lies in the eyes of the beholder and there can be many criteria involoved.
Examples range from simple daily tasks such as booking a hotel or choosing a photo to post, to evaluating candidates for college admission, ranking universities, you name it.
</p>
<p>
Finding novel, efficient, and accurate technical solutions for addressing the challenging problems in this area is our focus here.
</p>
<p>More details in <a href="ranking.htm">Project Page</a>.</p>
</div>
</div>
<div class="jumbotron jumbotron-fluid text-center" style="padding-block-start: 10pt;">
<img class="roundphoto" src="imgs/ML.png"/>
<h3 class="display-4"><a href="">AI&ML</a></h3>
<p class="lead">Projects related to AI and machine learning</p>
<hr class="my-4">
<div class="text-left" style="padding: 1%;">
<p>We specifically target data prepration and leveraging data management techniques for machine learning.
Some of our projects in this area include creating nutritional labels for dataset, efficient construction of ad-hoc ML models, and fairness in ML.</p>
<p>More details in <a href="ml.htm" >Project Page</a>.</p>
</div>
</div>
<div class="jumbotron jumbotron-fluid text-center" style="padding-block-start: 10pt;">
<img class="roundphoto" src="imgs/HDB.jpg"/>
<h3 class="display-4"><a href="">Web DataX</a></h3>
<p class="lead">Projects related to Web Data Exploration </p>
<hr class="my-4">
<div class="text-left" style="padding: 1%;">
<p>Web databases, also known as deep web or hidden web databases, cover a large portion of web.
This includes shopping websites such as Amazon and eBay,
service search sites such as Expedia and Google Flights,
recommendation websites, and P2P marketplaces such as Airbnb and Craigslist.
With minor differences, such systems have a typical structure that enforce a ranked retrieval interface.<br>
Our projects in this category enable efficient data exploration and flexible query answering and recommendation in this environment.
</p>
<p>More details in <a href="webdb.htm" >Project Page</a>.</p>
</div>
</div>
</div>
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