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Author: asudeh

index.htm

@@ -107,33 +107,33 @@ <h5>May 2025 -- End of Year Party</h5>
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   <div class="card contentBoxNB">
     <div class="card-header">News and Announcements</div>
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+    <div class="card-body" style="height: 400pt; overflow-y: scroll;">
       <ul style="line-height:1.7">
-        <li>Congratulations to <i>Mohsen Dehghankar</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>Mohsen Dehghankar</i> and <i>Mahdi Erfanian</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>Nima Shahbazi</i> on the <b>“2024-2025 College of Engineering Exceptional Research Promise Award”</b>.</li>
-        <li><i>Nima Shahbazi</i> will be a Research Intern at <b>Microsoft Research, Gray Systems Lab</b> in Summer 2025.</li>
-        <li>Congratulations to <i>Mohsen Dehghankar</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>Mohsen Dehghankar</i> on his <i>KDD'25</i> paper, <i>“Fair Set Cover”</i>.</li>
-        <li>Congratulations to <i>Mahdi Erfanian</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>Sana Ebrahimi</i> and <i>Rishi Advani</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>Nima Shahbazi</i> and <i>Mahdi Erfanian</i> on their <i>VLDB'24</i> demo paper, <i>“FairEM360: A Suite for Responsible Entity Matching”</i>.</li>
-        <li>Congratulations to <i>Nima Shahbazi</i> on his <i>VLDB Journal</i> (2024) paper, <i>“Reliability Evaluation of Individual Predictions: A Data-centric Approach”</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://scholar.google.com/citations?user=-RDRcqAAAAAJ" 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>Sana Ebrahimi</i> and <i>Nima Shahbazi</i> on their <i>NAACL'24</i> paper, <i>“Reliability and Equity through Aggregation in Large Language Models”</i>.</li>
-        <li>Congratulations to <i>Nima Shahbazi</i> on his (Megagon) internship project's acceptance in ICDE'24.</li>
-        <li>Congratulations to <i>Nima Shahbazi</i> on his <i>SIGMOD'24</i> paper, <i>“FairHash: A Fair and Memory/Time-efficient Hashmap”</i>.</li>
-        <li><i>Nima Shahbazi</i> will be a Research Scientist Intern at <b>Megagon Labs</b> in Summer 2023.</li>
-        <li>Congratulations to <i>Nima Shahbazi</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>Nima Shahbazi</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>Rishi Advani</i> on his <i>KDD'23</i> paper, <i>“Maximizing Neutrality in News Ordering”</i>.</li>
-        <li>Congratulations to <i>Nima Shahbazi</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><a href="https://scholar.google.com/citations?user=-RDRcqAAAAAJ" 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>Sana Ebrahimi</i> on their <i>VLDB 2022</i> paper, <i>“Maximizing Fair Content Spread via Edge Suggestion in Social Networks”</i>.</li>
+        <li>Congratulations to <i>Ian Swift</i> and <i><a href="https://scholar.google.com/citations?user=-RDRcqAAAAAJ" 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>Nima Shahbazi</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><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>