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<title>InDeX Lab: Innovative Data Exploration Laboratory</title>
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<img class="d-block w-100" src="photos/Nima1.jpg" alt="Nima Shahbazi's Dissertation Defense">
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<h5>Oct 2025 -- Nima Shahbazi's Dissertation Defense</h5>
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<h5>May 2024 -- End of Year Party</h5>
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<h5>May 2025 -- End of Year Party</h5>
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<div class="card-header">About</div>
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Innovative Data Exploration Laboratory (InDeX Lab) is an academic research group directed by Dr. <a href="https://www.cs.uic.edu/~asudeh/">A. Asudeh</a>, at the Computer Science department of the University of Illinois Chicago.
<br>At InDeX Lab, <b>we design Efficient, Accurate, and Scalable Algorithms for Data Science and AI.</b>
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<div class="card-header">Highlights</div>
<div class="card-body" style="height: 80pt; overflow-y: scroll;">
<ul style="line-height:1.7">
<li><span>2025:</span>
<br>- <b>nine full papers</b>, including papers in <b>KDD</b> (two papers), <b>ICML</b>, <b>VLDB</b>, , <b>AACL</b>, and <b>EDBT</b>.
<br>- <b>Nima Shahbazi</b> successfully defended his PhD disseration.
</li>
<li><span>2024:</span> <b>nine full papers</b> in <b>SIGMOD</b>, <b>VLDB</b>, <b>NAACL</b>, <b>VLDB Journal</b> (two papers), <b>EDBT</b> (two papers), <b>ICKG</b>, and one invited paper in <b>Data Engineering Bulletin</b>; and one Demo paper in VLDB.
</li>
<li><span>2023:</span> <b>three full papers</b> in <b>VLDB</b>, <b>KDD</b>, and <b>ALGORITHMICA</b>, one survey paper in <b>ACM COMPUTING SURVEYS</b>, one tutorial in <b>WSDM</b>, and two workshop papers in Fairness workshop at SDM'23.
</li>
<li><span>2022:</span> <b>five full papers</b> (two in <b>VLBD</b>, one in <b>ICDE</b>, and two journal papers in ACM <b>TODS</b> and Expert Systems) and presented one tutorial (in SIGMOD), in collaboration with University of Rochester, UTA, U-M, and Google Brain.
</li>
<li><span>2021:</span>
<br>- <b>Four full papers</b> (two in <b>SIGMOD</b>, one in <b>VLBD</b>, and one in <b>ICDE</b>), one invited paper in <b>Data Engineering Bulletin</b>, and one workshop paper (in SIGMOD-DEEM), in collaboration with the <a target="_blank" href="https://dbgroup.eecs.umich.edu/">DBGroup@UM</a>, <a target="_blank" href="https://chu-data-lab.cc.gatech.edu/">ChuDataLab@GaTech</a>, University of Rochester, Politecnico di Torino, and Google Research (Structured Data Group).
<br>- <b>Google's Research Scholar award</b> for our work on Cherry-picked Trendlines.
<br>- <b><a target="_blank" href="https://cacm.acm.org/">Communications of the ACM</a></b> featured our work on <a target="_blank" href="https://cacm.acm.org/magazines/2021/2/250087-scalable-signal-reconstruction-for-a-broad-range-of-applications/fulltext">"Signal Reconstruction at Scale"</a> as its <b>Research Highlight</b> in February 2021, 64.2: 106-115.
<br>- <b><a target="_blank" href="http://wp.sigmod.org/">ACM SIGMOD Blog</a></b> featured our article <a href="http://wp.sigmod.org/?p=3174" target="_blank">"Enabling Responsible Data Science in Practice"</a>, Jan. 2021.
</li>
<li><span>2020:</span> <b>five papers in VLBD 2020</b> (three full reseach papers, one tutorial, and one demo), 1 demo paper in SIGMOD 2020, and 1 invited paper in VLBDJ (Special Issue on <b>Best of VLDB'18</b>), in collaboration with the <a target="_blank" href="https://dbgroup.eecs.umich.edu/">DBGroup@UM</a> and <a target="_blank" href="http://dbxlab.uta.edu/">DBXLAB@UTA</a>.
</li>
<li><span>2019:</span>
<br>- <b>Three papers in SIGMOD 2019</b> (two full reseach papers and one demo), <b>2 papers in VLDB 2019</b> (one full research paper and one demo), 1 full research paper in ICDE 2019, 1 in PAKDD 2019, 1 demo paper in CIKM 2019, and 1 <b>invited paper in Data Engineering Bulletin</b>.
<br>- <b>ACM SIGMOD Research Highlight Award</b> 2019.
</li>
</ul>
</div>
</div>
<div> </div>
<div class="card contentBoxNB">
<div class="card-header">News and Announcements</div>
<div class="card-body" style="height: 400pt; overflow-y: scroll;">
<ul style="line-height:1.7">
<li><i><a href="http://merfanian.github.io/" target="_blank">Mahdi Erfanian</a></i> will be a Research Intern at <b>Microsoft Code | AI team</b> in Summer 2026.</li>
<li><i><a href="https://mohsendehghankar.github.io/" target="_blank">Mohsen Dehghankar</a></a></i> will be a Research Intern at <b>Adobe Research</b> in Summer 2026.</li>
<li>Congratulations to <i><a href="https://neemashahbazi.github.io/" target="_blank">Nima Shahbazi</a></i> on his <i>SIGMOD'26</i> paper, <i>“Fair-Count-Min: Frequency Estimation under Equal Group-wise Approximation Factor”</i>.</li>
<li>Congratulations to <i><a href="https://mohsendehghankar.github.io/" target="_blank">Mohsen Dehghankar</a></i> on his <i>VLDB'26</i> paper, <i>“On Fair Epsilon Net and Geometric Hitting Set”</i>.</li>
<li>Congratulations to <i><a href="https://rishi-advani.com/" target="_blank">Rishi Advani</a></i> and <i><a href="https://mohsendehghankar.github.io/" target="_blank">Mohsen Dehghankar</a></i> on their <i>ICDT'26</i> paper, <i>“Dynamic Necklace Splitting”</i>.</li>
<li><i><a href="http://merfanian.github.io/" target="_blank">Mahdi Erfanian</a></i> will be a Research Intern at <b>Microsoft Code | AI team</b> in Spring 2026.</li>
<li><i><a href="https://neemashahbazi.github.io/" target="_blank">Dr. Nima Shahbazi</a></i> successfully defended his PhD disseration in Oct. 2025. Congratulations! He will join Megagon Labs as a Research Scientist.</li>
<li>Congratulations to <i><a href="https://www.sanaebrahimi.com/" target="_blank">Sana Ebrahimi</a></i> and <i><a href="https://mohsendehghankar.github.io/" target="_blank">Mohsen Dehghankar</a></i> on their <i>AACL'25</i> paper, <i>“An Adversary-Resistant Multi-Agent LLM System via Credibility Scoring”</i>.</li>
<li>Congratulations to <i><a href="https://mohsendehghankar.github.io/" target="_blank">Mohsen Dehghankar</a></i> on his <i>KDD'25</i> paper, <i>“Rank It, Then Ask It: Input Reranking for Maximizing the Performance of LLMs on Symmetric Tasks”</i>.</li>
<li>Congratulations to <i><a href="https://mohsendehghankar.github.io/" target="_blank">Mohsen Dehghankar</a></i> and <i><a href="http://merfanian.github.io/" target="_blank">Mahdi Erfanian</a></i> on their <i>ICML'25</i> paper, <i>“An Efficient Matrix Multiplication Algorithm for Accelerating Inference in Binary and Ternary Neural Networks”</i>.</li>
<li>Congratulations to <i><a href="https://neemashahbazi.github.io/" target="_blank">Nima Shahbazi</a></i> on the <b>“2024-2025 College of Engineering Exceptional Research Promise Award”</b>.</li>
<li><i><a href="https://neemashahbazi.github.io/" target="_blank">Nima Shahbazi</a></i> will be a Research Intern at <b>Microsoft Research, Gray Systems Lab</b> in Summer 2025.</li>
<li>Congratulations to <i><a href="https://mohsendehghankar.github.io/" target="_blank">Mohsen Dehghankar</a></i> on his <i>VLDB'25</i> paper, <i>“Mining the Minoria: Unknown, Under-represented, and Under-performing Minority Groups”</i>.</li>
<li>Congratulations to <i><a href="https://mohsendehghankar.github.io/" target="_blank">Mohsen Dehghankar</a></i> on his <i>KDD'25</i> paper, <i>“Fair Set Cover”</i>.</li>
<li>Congratulations to <i><a href="http://merfanian.github.io/" target="_blank">Mahdi Erfanian</a></i> on his <i>VLDB'24</i> paper, <i>“Chameleon: Foundation Models for Fairness-aware Multi-modal Data Augmentation to Enhance Coverage of Minorities”</i>.</li>
<li>Congratulations to <i><a href="https://www.sanaebrahimi.com/" target="_blank">Sana Ebrahimi</a></i> and <i><a href="https://rishi-advani.com/" target="_blank">Rishi Advani</a></i> on their <i>EDBT'25</i> paper, <i>“Evaluating the Feasibility of Sampling-Based Techniques for Training Multilayer Perceptrons”</i>.</li>
<li>Congratulations to <i><a href="https://neemashahbazi.github.io/" target="_blank">Nima Shahbazi</a></i> and <i><a href="http://merfanian.github.io/" target="_blank">Mahdi Erfanian</a></i> on their <i>VLDB'24</i> demo paper, <i>“FairEM360: A Suite for Responsible Entity Matching”</i>.</li>
<li>Congratulations to <i><a href="https://neemashahbazi.github.io/" target="_blank">Nima Shahbazi</a></i> on his <i>VLDB Journal</i> (2024) paper, <i>“Reliability Evaluation of Individual Predictions: A Data-centric Approach”</i>.</li>
<li>Our (invited) paper <a href="http://sites.computer.org/debull/A24mar/A24MAR-CD.pdf#page=5" target="_blank">Coverage-based Data-centric Approaches for Responsible and Trustworthy AI</a> was published in Data Engineering Bulletin Vol. 48(1), March 2024.</li>
<li>Congratulations to <i><a href="https://www.sanaebrahimi.com/" target="_blank">Sana Ebrahimi</a></i> and <i><a href="https://neemashahbazi.github.io/" target="_blank">Nima Shahbazi</a></i> on their <i>NAACL'24</i> paper, <i>“Reliability and Equity through Aggregation in Large Language Models”</i>.</li>
<li>Congratulations to <i><a href="https://neemashahbazi.github.io/" target="_blank">Nima Shahbazi</a></i> on his (Megagon) internship project's acceptance in ICDE'24.</li>
<li>Congratulations to <i><a href="https://neemashahbazi.github.io/" target="_blank">Nima Shahbazi</a></i> on his <i>SIGMOD'24</i> paper, <i>“FairHash: A Fair and Memory/Time-efficient Hashmap”</i>.</li>
<li><i><a href="https://neemashahbazi.github.io/" target="_blank">Nima Shahbazi</a></i> will be a Research Scientist Intern at <b>Megagon Labs</b> in Summer 2023.</li>
<li>Congratulations to <i><a href="https://neemashahbazi.github.io/" target="_blank">Nima Shahbazi</a></i> on his <i>VLDB'23</i> paper, <i>“Through the Fairness Lens: Experimental Analysis and Evaluation of Entity Matching”</i>.</li>
<li>Congratulations to <i>Melika Mousavi</i> and <i><a href="https://neemashahbazi.github.io/" target="_blank">Nima Shahbazi</a></i> on their <i>EDBT'24</i> paper, <i>“Data Coverage for Detecting Representation Bias in Image Data Sets: A Crowdsourcing Approach”</i>.</li>
<li>Congratulations to <i><a href="https://rishi-advani.com/" target="_blank">Rishi Advani</a></i> on his <i>KDD'23</i> paper, <i>“Maximizing Neutrality in News Ordering”</i>.</li>
<li>Congratulations to <i><a href="https://neemashahbazi.github.io/" target="_blank">Nima Shahbazi</a></i> on his <i>ACM COMPUTING SURVEYS (CSUR)</i> paper, <i>“A Survey on Techniques for Identifying and Resolving Representation Bias in Data”</i>.</li>
<li>Congratulations to <i>Khanh Duy Nguyen</i> on his <i>SDM</i> workshop paper, <i>“PopSim: An Individual-level Population Simulator for Equitable Allocation of City Resources”</i>.</li>
<li>Congratulations to <i>Ian Swift</i> and <i><a href="https://www.sanaebrahimi.com/" target="_blank">Sana Ebrahimi</a></i> on their <i>VLDB 2022</i> paper, <i>“Maximizing Fair Content Spread via Edge Suggestion in Social Networks”</i>.</li>
<li><a href="tutorial22.htm">Here</a> are the slides and other information about our SIGMOD'22 tutorial on "Responsible Data Integration: Next-generation Challenges".</li>
<li>Congratulations to <i>Ian Swift</i> on his <i>ICDE 2022</i> paper, <i>“Fairness-Aware Range Queries for Selecting Unbiased Data”</i>.</li>
<li>A big Thank you to <i>Google</i> for supporting our work on Cherry-picked Trendlines with the <i>Research Scholar award!</i></li>
<li>Congratulations to <i><a href="https://neemashahbazi.github.io/" target="_blank">Nima Shahbazi</a></i> on his <i>SIGMOD 2021</i> paper, <i>“Identifying Insufficient Data Coverage for Ordinal Continuous-Valued Attributes”</i>.<a target="_blank" href="assets/cov21.pdf">[paper]</a><a target="_blank" href="assets/cov21-SIGMODPresentation-20.pdf">[slides]</a><a target="_blank" href="https://youtu.be/R-jXjsL0hxg">[video]</a></li>
<li>Congratulations to <i>Matteo Corain</i> on his <i>ICDE 2021</i> paper, <i>“DBSCOUT: A density-based method for scalable outlier detection in very large datasets”</i>. This paper is the outcome of his joint MS thesis with Politecnico di Torino (Italy), co-adviced by Dr. <a target="_blank" href="https://dl.acm.org/profile/81100349712">Paolo Garza</a>.</li>
</ul>
</div>
</div>
<p></p>
<div class="card contentBoxNB">
<div class="card-header"> 📌 Pinned Systems and Repositories 📌</div>
<div class="card-body">
<div class="row no-gutters">
<div class="col-md-3 text-center">
<!--<img class="person abol card-img"/>-->
<p></p>
<img width="90%" src="imgs/needlelogo.png"/>
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<div class="card-body">
<b>Needle🪡🔍</b> is a <i>deployment-ready</i> open-source image retrieval database with high accuracy that can handle <b>complex queries in natural language</b>.
It is <strong>Fast</strong>, <strong>Efficient</strong>, and <strong>Precise</strong>, outperforming state-of-the-art methods.
Born from high-end research, Needle is designed to be accessible to everyone while delivering top-notch performance.
Whether you are a researcher, developer, or an enthusiast, Needle opens up innovative ways to explore your image datasets. ✨
<div class="project-links">
<a href="https://www.cs.uic.edu/~indexlab/Needle" class="btn project-btn" target="_blank">
🌐 Visit Project
</a>
<a href="https://github.com/UIC-InDeXLab/Needle" class="btn github-btn" target="_blank">
🛠️ View on GitHub
</a>
<a href="https://arxiv.org/abs/2412.00639" class="btn publication-btn" target="_blank">
📄 Read Publication (Preprint)
</a>
<a href="https://www.youtube.com/watch?v=n-SXX_ry9-0" class="btn youtube-btn" target="_blank">
▶️ Watch on YouTube
</a>
</div>
<p class="install-instructions">
📖 <strong>Detailed installation instructions:</strong>
<a href="https://www.cs.uic.edu/~indexlab/Needle/getting-started.html" target="_blank" class="install-link">
Getting Started
</a>. </p>
</div>
</div>
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<p></p>
<img width="90%" src="imgs/rsr.png"/>
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<div class="card-body col-md-9">
<h6><b>RSR 🧮</b>: Efficient Matrix Multiplication for Accelerating Inference in Binary and Ternary Neural Networks</h6>
This project aims to provide a fast and efficient approach to low-bit matrix multiplication.
The code repository implements Redundant Segment Reduction (RSR), a fast matrix multiplication algorithm designed for matrices in binary and ternary networks.
The RSR method optimizes computation efficiency by a log(n) factor, making it particularly useful for applications in low-bit deep learning and efficient inference.
The codebase provides ready-to-use <b>C++</b> and <b>NumPy</b>-based implementations, as well as <b>PyTorch</b> implementations with both <b>CPU</b> and <b>GPU</b> support, enabling scalable and optimized matrix operations in deep learning environments.
It includes sample experiments on various `1.58bit` models and LLMs.✨
<div class="project-links">
<a href="https://github.com/UIC-InDeXLab/RSR/" class="btn github-btn" target="_blank">
🛠️ View on GitHub
</a>
<a href="https://arxiv.org/abs/2411.06360" class="btn publication-btn" target="_blank">
📄 Read Publication (Preprint)
</a>
</div>
</div>
</div>
</div>
</div>
<div> </div>
<div class="card contentBoxNB">
<div class="card-header">Sponsors</div>
<div class="card-body">
<br />
<a href="https://www.nsf.gov/" target="_blank"><img src="imgs/nsf.png" width="20%" alt="National Science Foundation" /></a>
<a href="https://research.google/" target="_blank"><img src="imgs/Research-at-google.png" width="25%" alt="Google Research" /></a>
<a href="https://www.cloudbank.org" target="_blank"><img src="imgs/cloudbank.png" width="15%" alt="CloudBank" /></a>
</div>
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