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12  Saurabh  Sihag
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65     <span class="font-weight-bold">Saurabh</span>  Sihag
66    </h1>
67     <p class="desc">Assistant Professor at <a href="https://www.albany.edu/" target="_blank" rel="noopener noreferrer">University at Albany</a>.</p>
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131      <p>I am an Assistant Professor in the Electrical and Computer Engineering department at <a href="https://www.albany.edu/" target="_blank" rel="noopener noreferrer">State University of New York at Albany</a>. Previously, I was a postdoctoral researcher working with <a href="https://alelab.seas.upenn.edu/" target="_blank" rel="noopener noreferrer">Dr. Alejandro Ribeiro</a> at the University of Pennsylvania.  Broadly, I study statistical inference and machine learning approaches over graph models, both from theoretic and algorithmic perspectives with applications in network neuroscience. I had received the PhD degree in Electrical Engineering at Rensselaer Polytechnic Institute in Dec., 2020, where I was advised by <a href="https://www.isg-rpi.com/" target="_blank" rel="noopener noreferrer">Dr. Ali Tajer</a>. My PhD thesis on Statistical Learning and Inference over Networks can be accessed <a href="https://www.proquest.com/docview/2501490179" target="_blank" rel="noopener noreferrer">here</a>.</p>
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133<p>My research is focused on developing novel principles of machine learning and statistical inference using concepts from signal processing, learning  theory, information theory, and graph theory. I have worked on a variety of research problems that include statistical learning of graph models (<a href="http://proceedings.mlr.press/v130/varici21a.html" target="_blank" rel="noopener noreferrer">AISTATS</a>, <a href="https://proceedings.neurips.cc/paper/2019/file/e025b6279c1b88d3ec0eca6fcb6e6280-Paper.pdf" target="_blank" rel="noopener noreferrer">NeurIPS</a>), state estimation in signal processing (<a href="https://ieeexplore.ieee.org/abstract/document/9057614" target="_blank" rel="noopener noreferrer">T-IT</a>, <a href="https://ieeexplore.ieee.org/abstract/document/8338162" target="_blank" rel="noopener noreferrer">JSTSP</a>, <a href="https://ieeexplore.ieee.org/abstract/document/8747440" target="_blank" rel="noopener noreferrer">SPL</a>), graph signal processing analyses of multimodal brain imaging data (<a href="https://ieeexplore.ieee.org/abstract/document/9044786" target="_blank" rel="noopener noreferrer">TSIPN</a>) and association of neuroimaging features with blood biomarkers (<a href="https://www.nature.com/articles/s43856-021-00065-5" target="_blank" rel="noopener noreferrer">NatCommsMed</a>), and adaptive graph-constrained group testing (<a href="https://ieeexplore.ieee.org/abstract/document/9658194" target="_blank" rel="noopener noreferrer">

133TSP</a>). Currently, I am investigating statistical inference using graph neural networks (<a href="https://proceedings.neurips.cc/paper_files/paper/2022/hash/6cb00ce1a21a090a3dae04cebebd8341-Abstract-Conference.html" target="_blank" rel="noopener noreferrer">VNN</a>), with brain age prediction as a recent application (<a href="https://arxiv.org/abs/2305.18370" target="_blank" rel="noopener noreferrer">VNN_Brain_Age</a>). A complete list of my research publications is available <a href="https://scholar.google.com/citations?user=T8D94-QAAAAJ" target="_blank" rel="noopener noreferrer">here</a>.</p>
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141  <h2>news</h2>
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144      <table class="table table-sm table-borderless">
145    <tr>
146          <th scope="row">April, 2026</th>
147          <td>
148            
149              Paper on 'Brain Age Gap Progression in Individuals at Risk for Neurodegeneration' accepted at International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC) 2026. 
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158          <th scope="row">April, 2026</th>
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161              Served as a Reviewer for NIH Special Emphasis Panel on Biomarker Studies in Neuroscience.
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169          <th scope="row">April, 2026</th>
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172              Presented tutorial on 'Learning with Covariance Matrices: Foundations and Applications to Network Neuroscience' at IEEE International Symposium on Biomedical Imaging, 2026 (<a href="/assets/pdf/VNN_Tutorial_ISBI.pdf" target="blank">Slides</a>).
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182          <th scope="row">August-September, 2025</th>
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185              Presented tutorials on 'Learning with Covariance Matrices: Foundations and Applications to Network Neuroscience' at IEEE MLSP, 2025 (<a href="/assets/pdf/VNN_Tutorial_MLSP_final.pdf" target="blank">Slides</a>) and EUSIPCO, 2025 (<a href="/assets/pdf/VNN_Tutorial_EUSIPCO_compressed.pdf" target="blank">Slides</a>).
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189          </td>
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192        <tr>
193          <th scope="row">September, 2025</th>
194          <td>
195            
196              Presented a talk on 'Disentangling Neurodegeneration with Brain Age Gap Prediction Models' at TU Delft (<a href="/assets/pdf/VNN_Tutorial_Delft.pdf" target="blank">Slides</a>).
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206         <tr>
207          <th scope="row">July, 2025</th>
208          <td>
209            
210              Paper on 'Disentangling Neurodegeneration with Brain Age Gap Prediction Models: A Graph Signal Processing Perspective' accepted at IEEE Signal Processing Magazine. Accepted paper available <a href="/assets/pdf/MSP3596731.pdf" target="blank">here</a>.
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217        <tr>
218          <th scope="row">January, 2025</th>
219          <td>
220            
221              Paper on 'Explainable Brain Age Gap Prediction in Neurodegenerative Conditions Using coVariance Neural Networks' accepted at ISBI, 2025.
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228          <th scope="row">May, 2024</th>
229          <td>
230            
231              Paper on 'Neural Tangent Kernels Motivate Cross-Covariance Graphs in Neural Networks' accepted at ICML, 2024.
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237         <tr>
238          <th scope="row">March, 2024</th>
239          <td>
240            
241              Paper on 'Transferability of coVariance Neural Networks' accepted at IEEE Journal of Selected Topics in Signal Processing (JSTSP) Special Series on AI in Signal & Data Science.
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247         <tr>
248          <th scope="row">September, 2023</th>
249          <td>
250            
251              Paper on 'Explainable Brain Age Prediction using coVariance Neural Networks' accepted at NeurIPS, 2023.
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257         <tr>
258          <th scope="row">June, 2023</th>
259          <td>
260            
261              Paper on 'Learning Graph Structure from Convolutional Mixtures' accepted at Transactions on Machine Learning Research.
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263            
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268         <tr>
269          <th scope="row">June, 2023</th>
270          <td>
271            
272              Paper on 'Predicting Brain Age using Transferable coVariance Neural Networks' was presented at ICASSP, 2023.
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279          <th scope="row">Sep. 14, 2022</th>
280          <td>
281            
282              Paper on 'coVariance Neural Networks' was accepted at NeurIPS, 2022.
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