-
Notifications
You must be signed in to change notification settings - Fork 0
/
index.html
227 lines (196 loc) · 8.92 KB
/
index.html
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
184
185
186
187
188
189
190
191
192
193
194
195
196
197
198
199
200
201
202
203
204
205
206
207
208
209
210
211
212
213
214
215
216
217
218
219
220
221
222
223
224
225
226
227
<!DOCTYPE html>
<html lang="en">
<head>
<link rel="stylesheet" href="index.css">
<title>Naganand Yadati</title>
<meta charset="UTF-8">
<meta name="viewport" content="width=device-width, initial-scale=1">
<style>
body {
font-family: Arial, Helvetica, sans-serif;
}
</style>
</head>
<body>
<script src="index.js"></script>
<h1><a href = "https://naganandy.github.io/cv.pdf" target="_blank" rel="noopener noreferrer">Naganand Yadati</a></h1>
<div class="affiliation">
<p>
<h2>Postdoctoral Research Fellow</h2>
<a href="https://www.comp.nus.edu.sg/" target="_blank" rel="noopener noreferrer">School of Computing</a>
<br></br>
<a href="https://www.nus.edu.sg/" target="_blank" rel="noopener noreferrer">National University of Singapore</a>
<br></br>
Advisor: <a href="https://www.comp.nus.edu.sg/~arnab/" target="_blank" rel="noopener noreferrer">Prof. Arnab Bhattacharyya</a>
</p>
</div>
<br></br>
<div class="tab">
<button class="tablinks" onclick="openTab(event, 'Education')">Education</button>
<button class="tablinks" onclick="openTab(event, 'ResearchFocus')">Research</button>
<button class="tablinks" onclick="openTab(event, 'KeyPublications')">Publications</button>
</div>
<br></br>
<div id="Education" class="tabcontent">
<br></br>
<div class="project">
<div class="project-image">
<img src="iisc.png" alt="iisc_logo">
</div>
<div class="project-info">
<p>
<h2>Doctor of Philosophy (Ph.D.)</h2>
<a href="https://www.csa.iisc.ac.in/" target="_blank" rel="noopener noreferrer">Department of Computer Science and Automation</a>
<br></br>
<a href="https://iisc.ac.in/" target="_blank" rel="noopener noreferrer">Indian Institute of Science, Bangalore</a>
<br></br>
2016-2021
<br></br>
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>
</div>
</div>
<br></br>
<div class="project">
<div class="project-image">
<img src="iiitb.png" alt="iiitb_logo">
</div>
<div class="project-info">
<p>
<h2>Master of Technology (M.Tech.)</h2>
<a href="https://www.iiitb.ac.in/" target="_blank" rel="noopener noreferrer">International Institute of Information Technology, Bangalore</a>
<br></br>
2014-2016
<br></br>
Advisor: <a href="https://sites.google.com/view/ashish-choudhury" target="_blank" rel="noopener noreferrer">Prof. Ashish Choudhury</a>
</p>
</div>
</div>
<br></br>
<div class="project">
<div class="project-image">
<img src="rvce.png" alt="rvce_logo">
</div>
<div class="project-info">
<p>
<h2>Bachelor of Engineering (B.E.)</h2>
<a href="https://rvce.edu.in/" target="_blank" rel="noopener noreferrer">Rashtreeya Vidyalaya College of Engineering, Bangalore</a>
<br></br>
2010-2014
</p>
</div>
</div>
</div>
<div id="ResearchFocus" class="tabcontent">
<br></br>
<div class="project">
<div class="project-image">
<img src="gnn.png" alt="gnn">
</div>
<div class="project-info">
<h2>Deep Learning with Emphasis on Graph Neural Networks</h2>
<p>Understanding structured data across varied domains, from social networks and biological systems to knowledge graphs, is becoming increasingly critical.
<br></br>
Traditional deep learning models (e.g., convolutions, transformers) are well-suited to ordered data like images and text but often fall short when dealing with graph-structured data's irregularities and complexities.
<br></br>
This challenge has led to the development of Graph Neural Networks (GNNs), a class of deep learning models tailored to graph data, emphasising the crucial interdependencies between nodes.
</p>
</div>
</div>
<div class="project">
<div class="project-image">
<img src="lrs.png" alt="gnn">
</div>
<div class="project-info">
<h2>Learning on Rich Structures (e.g., causal graphs)</h2>
<p>Motivated by the intricate web of multi-faceted relationships found within various datasets, the research focus delves into machine learning of data rich systems such as causal graphs and hypergraphs.
<br></br>
Causal graphs encapsulate cause-and-effect dynamics and are pivotal in fields that require understanding the underlying mechanisms of observed phenomena, such as epidemiology and economics.
<br></br>
Hypergraphs take this a step further by capturing higher-order relationships, where edges can connect more than two nodes, facilitating a multidimensional analysis of interactions.
</p>
</div>
</div>
</div>
</div>
<br></br>
<div id="KeyPublications" class="tabcontent">
<a href = "https://scholar.google.com/citations?user=-td7hOYAAAAJ" target="_blank" rel="noopener noreferrer"><img src="gs.png" alt="Google Scholar" class="google-scholar-icon"></a>
<br></br>
<br></br>
<br></br>
<h2><u>Key Publications</u></h2>
<br></br>
<div class="project">
<div class="project-image">
<img src="1.png" alt="hypergcn_neurips19">
</div>
<div class="project-info">
<h2>HyperGCN: A New Method for Training Graph Convolutional Networks on Hypergraphs</h2>
<p>
<a href="https://papers.nips.cc/paper_files/paper/2019/hash/1efa39bcaec6f3900149160693694536-Abstract.html" target="_blank" rel="noopener noreferrer">In 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>
</div>
</div>
<div class="project">
<div class="project-image">
<img src="3.png" alt="gmpnnr_neurips20">
</div>
<div class="project-info">
<h2>Neural Message Passing for Multi-Relational Ordered and Recursive Hypergraphs</h2>
<p>
<a href="https://proceedings.neurips.cc//paper_files/paper/2020/hash/217eedd1ba8c592db97d0dbe54c7adfc-Abstract.html" target="_blank" rel="noopener noreferrer">In 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>
</div>
</div>
<div class="project">
<div class="project-image">
<img src="6.png" alt="cvxgnn_icdm22">
</div>
<div class="project-info">
<h2>A Convex Formulation for Graph Convolutional Training: Two Layer Case</h2>
<p>
<a href="https://ieeexplore.ieee.org/abstract/document/10027696" target="_blank" rel="noopener noreferrer">In 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>
</div>
</div>
<div class="project">
<div class="project-image">
<img src="8.png" alt="gainer_eacl24">
</div>
<div class="project-info">
<h2>GAINER: Graph Machine Learning with Node-specific Radius for Classification of Texts</h2>
<p>
To Appear <a href="https://2024.eacl.org/program/main-accepted/#long-papers" target="_blank" rel="noopener noreferrer">In 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>
</div>
</div>
<div class="project">
<div class="project-image">
<img src="t.png" alt="gnn_emnlp19">
</div>
<div class="project-info">
<h2>EMNLP Tutorial on Graph-based Deep Learning in Natural Language Processing</h2>
<p>
<a href="https://www.aclweb.org/anthology/D19-2006/" target="_blank" rel="noopener noreferrer">In 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>
</div>
</div>
</div>
</body>
<footer>
<p><small>© 2024 Naganand</small></p>
</footer>
</html>