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naganandy · Feb 16, 2024 · 610cdf3 · 610cdf3
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diff --git a/cv.pdf b/cv.pdf
diff --git a/iisc.jpg b/iisc.jpg
diff --git a/index.html b/index.html
@@ -52,7 +52,7 @@ <h2>Ph.D.</h2>
          <br></br> 
          2016-2021
          <br></br> 
-         Advisor: <a href="https://talukdar.net/" target="_blank" rel="noopener noreferrer">Prof. Partha Talukdar</a>
+         Advisor: <a href="https://parthatalukdar.github.io/" target="_blank" rel="noopener noreferrer">Prof. Partha Talukdar</a>
          <br></br>
          Thesis: <a href = "https://etd.iisc.ac.in/handle/2005/5560" target="_blank" rel="noopener noreferrer">Deep Learning over Hypergraphs</a>
          </p>
@@ -133,7 +133,7 @@ <h2><u>Key Publications</u></h2>
     <div class="project-info">
         <h2>HyperGCN: A New Method for Training Graph Convolutional Networks on Hypergraphs</h2>
         <p>
-             In <a href="https://papers.nips.cc/paper_files/paper/2019/hash/1efa39bcaec6f3900149160693694536-Abstract.html" target="_blank" rel="noopener noreferrer">NeurIPS'19</a>| <a href="https://github.com/malllabiisc/HyperGCN" target="_blank" rel="noopener noreferrer">code</a>| <a href="https://github.com/malllabiisc/HyperGCN/blob/master/slides/HyperGCN.pdf" target="_blank" rel="noopener noreferrer">slides</a>
+             In <a href="https://papers.nips.cc/paper_files/paper/2019/hash/1efa39bcaec6f3900149160693694536-Abstract.html" target="_blank" rel="noopener noreferrer">Proceedings of NeurIPS'19</a>| <a href="https://github.com/malllabiisc/HyperGCN" target="_blank" rel="noopener noreferrer">code</a>| <a href="https://github.com/malllabiisc/HyperGCN/blob/master/slides/HyperGCN.pdf" target="_blank" rel="noopener noreferrer">slides</a>
         </p>
         <p>Innovative and effective extenion of graph neural networks to hypergraphs, proven by extensive real-world testing.
         </p>
@@ -148,7 +148,7 @@ <h2>HyperGCN: A New Method for Training Graph Convolutional Networks on Hypergra
     <div class="project-info">
         <h2>Neural Message Passing for Multi-Relational Ordered and Recursive Hypergraphs</h2>
         <p>
-             In <a href="https://proceedings.neurips.cc//paper_files/paper/2020/hash/217eedd1ba8c592db97d0dbe54c7adfc-Abstract.html" target="_blank" rel="noopener noreferrer">NeurIPS'20</a>| <a href="https://github.com/naganandy/G-MPNN-R" target="_blank" rel="noopener noreferrer">code</a>
+             In <a href="https://proceedings.neurips.cc//paper_files/paper/2020/hash/217eedd1ba8c592db97d0dbe54c7adfc-Abstract.html" target="_blank" rel="noopener noreferrer">Proceedings of NeurIPS'20</a>| <a href="https://github.com/naganandy/G-MPNN-R" target="_blank" rel="noopener noreferrer">code</a>
         </p>
         <p>Frameworks that extend Message Passing Neural Networks to effectively handle multi-relational and recursive structures in real-world learning.
         </p>
@@ -163,7 +163,7 @@ <h2>Neural Message Passing for Multi-Relational Ordered and Recursive Hypergraph
     <div class="project-info">
         <h2>A Convex Formulation for Graph Convolutional Training: Two Layer Case</h2>
         <p>
-             In <a href="https://ieeexplore.ieee.org/abstract/document/10027696" target="_blank" rel="noopener noreferrer">ICDM'22</a>| <a href="https://drive.google.com/file/d/1sdsxeca6-LITiyISdpkBUVNqO0aJ9_TC/view?usp=drive_link" target="_blank" rel="noopener noreferrer">code</a>
+             In <a href="https://ieeexplore.ieee.org/abstract/document/10027696" target="_blank" rel="noopener noreferrer">Proceedings of ICDM'22</a>| <a href="https://drive.google.com/file/d/1sdsxeca6-LITiyISdpkBUVNqO0aJ9_TC/view?usp=drive_link" target="_blank" rel="noopener noreferrer">code</a>
         </p>
         <p>A convex approach to train two-layer ReLU-based Graph Neural Networks, ensuring global optimality in a field where theoretical understandings of optimisation have been limited.
         </p>
@@ -178,7 +178,7 @@ <h2>A Convex Formulation for Graph Convolutional Training: Two Layer Case</h2>
     <div class="project-info">
         <h2>GAINER: Graph Machine Learning with Node-specific Radius for Classification of Texts</h2>
         <p>
-             In <a href="https://2024.eacl.org/program/main-accepted/#long-papers" target="_blank" rel="noopener noreferrer">EACL'24</a>
+             To Appear In <a href="https://2024.eacl.org/program/main-accepted/#long-papers" target="_blank" rel="noopener noreferrer">Proceedings of EACL'24</a>
         </p>
         <p>Node-specific message passing radii in Graph Machine Learning for NLP, enhancing model flexibility validated by testing on several NLP tasks.
         </p>
@@ -193,7 +193,7 @@ <h2>GAINER: Graph Machine Learning with Node-specific Radius for Classification
     <div class="project-info">
         <h2>EMNLP Tutorial on Graph-based Deep Learning in Natural Language Processing</h2>
         <p>
-             In <a href="https://www.aclweb.org/anthology/D19-2006/" target="_blank" rel="noopener noreferrer">EMNLP'19</a>| <a href = "https://vimeo.com/439776761" target="_blank" rel="noopener noreferrer">video</a>| <a href = "https://shikhar-vashishth.github.io/assets/pdf/emnlp19_tutorial.pdf" target="_blank" rel="noopener noreferrer">slides</a>
+             In <a href="https://www.aclweb.org/anthology/D19-2006/" target="_blank" rel="noopener noreferrer">Proceedings of EMNLP'19</a>| <a href = "https://vimeo.com/439776761" target="_blank" rel="noopener noreferrer">video</a>
         </p>
         <p>A summary of various Graph Neural Network models in NLP covering a broad range of NLP tasks such as relation extraction, question answering.
         </p>