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<html>
<meta
name="viewport"
content="width=device-width, initial-scale=1"
>
<head>
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<body>
<div class="page">
<div class="header">
<table class="header-table">
<tr>
<td>
<img class="profile" src="https://homes.cs.washington.edu/~rahuln/img/profile_new.jpg">
</td>
<td class="info">
<h3>Rahul Nadkarni</h3>
<p>
Ph.D. CSE, University of Washington, 2026<br>
M.S. CSE, University of Washington, 2017<br>
B.S. EECS & Bioengineering, UC Berkeley, 2015
</p>
</td>
<td>
<i class="fa fa-envelope" aria-hidden="true"></i>rahulsnadkarni [at] gmail.com<br>
<i class="fa fa-graduation-cap" aria-hidden="true"></i><a href="https://scholar.google.com/citations?hl=en&user=JTaIfZMAAAAJ&view_op=list_works&sortby=pubdate">Google Scholar</a><br>
<i class="fa fa-linkedin" aria-hidden="true"></i><a href="https://www.linkedin.com/in/rahulsnadkarni">LinkedIn</a><br>
<i class="fa fa-github-alt" aria-hidden="true"></i><a href="https://github.com/rahuln">GitHub</a>
</td>
</tr>
</table>
</div>
<hr>
<div class="content">
<div class="about">
<!--
<strong style="color: red;">I am on the job market for industry
research positions! Please reach out if you think I would be a
good fit.</strong><br><br>
-->
<p>I recently received my Ph.D. in Computer Science & Engineering from the <a
href="https://www.cs.washington.edu/">Paul G. Allen School</a> at the <a
href="https://www.washington.edu/">University of
Washington</a>, where I was very fortunate to be advised by <a
href="https://nasmith.github.io/">Noah Smith</a>
and a member of the <a
href="https://noahs-ark.github.io/">ARK research group</a>.
My research interests are broadly in the areas of
<strong>natural language processing</strong> and
<strong>machine learning</strong>. Topics I've worked on
include scientific knowledge graph completion, domain
adaptation, model merging, instruction-following performance
prediction, and factual knowledge acquisition through
pretraining.
</p>
<p>
In the past, I worked with <a
href="https://homes.cs.washington.edu/~ebfox/">Emily Fox</a> on
statistical machine learning methods for time series applied to
neuroimaging data. I was fortunate to have been funded by an
<a
href="https://depts.washington.edu/dataweb/education/IGERT/overview.html">IGERT
fellowship in Big Data and Data Science</a> from 2017-2019. My
undergraduate research was in the <a
href="http://carmenalab.org/">Brain-Machine Interface Systems
Lab</a> at <a href="http://www.berkeley.edu/">UC Berkeley</a>,
advised by <a
href="https://www2.eecs.berkeley.edu/Faculty/Homepages/carmena.html">Jose
Carmena</a>.
</p>
</div>
<div class="publications">
<h3>Publications</h3>
<h4>Natural Language Processing</h4>
<table class="publications">
<tr>
<td class="last">
<img class="pubimg hide-mobile" src="img/2025-preprint.png">
</td>
<td class="last">
<b>Rewriting History: A Recipe for Interventional Analyses to Study Data Effects on Model Behavior</b><br>
<u>Rahul Nadkarni</u>, Yanai Elazar*, Hila Gonen*, Noah A. Smith<br>
<em>Transactions of the Association for Computational Linguistics (TACL, to appear)</em>, 2026<br>
<a class="btn btn-default btn-xs" href="https://www.arxiv.org/pdf/2510.14261"><i class="fa fa-file-pdf-o" aria-hidden="true"></i>paper</a>
<a class="btn btn-default btn-xs" href="https://www.arxiv.org/abs/2510.14261"><i class="fa fa-folder-open-o" aria-hidden="true"></i>arXiv</a>
<a class="btn btn-default btn-xs" href="https://github.com/rahuln/pretrain-intervention"><i class="fa fa-code" aria-hidden="true"></i>code</a>
</td>
</tr>
<tr>
<td class="last">
<img class="pubimg hide-mobile" src="img/2024-preprint.png">
</td>
<td class="last">
<b>Third-Party Language Model Performance Prediction from Instruction</b><br>
<u>Rahul Nadkarni</u>, Yizhong Wang, Noah A. Smith<br>
<em>arXiv</em>, 2024<br>
<a class="btn btn-default btn-xs" href="https://arxiv.org/pdf/2403.12413.pdf"><i class="fa fa-file-pdf-o" aria-hidden="true"></i>paper</a>
<a class="btn btn-default btn-xs" href="https://arxiv.org/abs/2403.12413"><i class="fa fa-folder-open-o" aria-hidden="true"></i>arXiv</a>
<a class="btn btn-default btn-xs" href="https://github.com/rahuln/instr-perf-pred"><i class="fa fa-code" aria-hidden="true"></i>code</a>
</td>
</tr>
<tr>
<td class="last">
<img class="pubimg hide-mobile" src="img/2022-binder-preprint.png">
</td>
<td class="last">
<b>Binding Language Models in Symbolic Languages</b><br>
Zhoujun Cheng*, Tianbao Xie*, Peng Shi, Chengzu Li, <u>Rahul Nadkarni</u>, Yushi Hu, Caiming Xiong, Dragomir Radev, Mari Ostendorf, Luke Zettlemoyer, Noah A. Smith, Tao Yu<br>
<em>International Conference on Learning Representations (ICLR)</em>, 2023<br>
<a class="btn btn-default btn-xs" href="https://arxiv.org/pdf/2210.02875.pdf"><i class="fa fa-file-pdf-o" aria-hidden="true"></i>paper</a>
<a class="btn btn-default btn-xs" href="https://arxiv.org/abs/2210.02875"><i class="fa fa-folder-open-o" aria-hidden="true"></i>arXiv</a>
<a class="btn btn-default btn-xs" href="https://github.com/xlang-ai/Binder"><i class="fa fa-code" aria-hidden="true"></i>code</a>
</td>
</tr>
<tr>
<td class="last">
<img class="pubimg hide-mobile" src="img/2021-akbc.png">
</td>
<td class="last">
<b>Scientific Language Models for Biomedical Knowledge Base Completion: An Empirical Study</b><br>
<u>Rahul Nadkarni</u>, David Wadden, Iz Beltagy, Noah A. Smith, Hannaneh Hajishirzi, and Tom Hope<br>
<em>Automated Knowledge Base Construction (AKBC)</em>, 2021<br>
<a class="btn btn-default btn-xs" href="https://arxiv.org/pdf/2106.09700.pdf"><i class="fa fa-file-pdf-o" aria-hidden="true"></i>paper</a>
<a class="btn btn-default btn-xs" href="https://arxiv.org/abs/2106.09700"><i class="fa fa-folder-open-o" aria-hidden="true"></i>arXiv</a>
<a class="btn btn-default btn-xs" href="https://github.com/rahuln/lm-bio-kgc"><i class="fa fa-code" aria-hidden="true"></i>code</a>
<a class="btn btn-default btn-xs" href="https://www.youtube.com/watch?v=6fW7WwD_5t4"><i class="fa fa-film" aria-hidden="true"></i>video</a>
</td>
</tr>
</table>
<h4>Machine Learning & Neuroscience</h4>
<table class="publications">
<tr>
<td class="last">
<img class="pubimg hide-mobile" src="img/2024-jne.png">
</td>
<td class="last">
<b>Using a linear dynamic system to measure functional connectivity from M/EEG</b><br>
Jordan Drew, Nicholas Foti, <u>Rahul Nadkarni</u>, Eric Larson, Emily Fox, Adrian KC Lee<br>
<em>Journal of Neural Engineering</em>, 21 (2024)<br>
<a class="btn btn-default btn-xs" href="https://iopscience.iop.org/article/10.1088/1741-2552/ad5cc1/meta"><i class="fa fa-link" aria-hidden="true"></i>paper</a>
</td>
</tr>
<tr>
<td class="last">
<img class="pubimg hide-mobile" src="img/brain.png">
</td>
<td class="last">
<b>Dynamic functional connectivity in auditory attention task</b><br>
Jordan Drew, Eric Larson, Nicholas Foti, <u>Rahul Nadkarni</u>, Emily Fox, Adrian KC Lee<br>
<em>The Journal of the Acoustical Society of America</em>, 2021<br>
<a class="btn btn-default btn-xs" href="https://asa.scitation.org/doi/abs/10.1121/10.0007922"><i class="fa fa-link" aria-hidden="true"></i>abstract</a>
</td>
</tr>
<tr>
<td class="last">
<img class="pubimg hide-mobile" src="img/2019-neurips-lmrl.png">
</td>
<td class="last">
<b>A hierarchical state-space model with Gaussian process dynamics for functional connectivity estimation</b><br>
<u>Rahul Nadkarni</u>, Nicholas J. Foti, Adrian KC Lee, and Emily B. Fox<br>
<em>NeurIPS Workshop on Learning Meaningful Representations of Life</em>, 2019<br>
<a class="btn btn-default btn-xs" href="pdf/2019-neurips-lmrl-abstract.pdf"><i class="fa fa-file-pdf-o" aria-hidden="true"></i>abstract</a>
<a class="btn btn-default btn-xs" href="pdf/2019-neurips-lmrl-poster.pdf"><i class="fa fa-map-o" aria-hidden="true"></i>poster</a>
</td>
</tr>
<tr>
<td class="last">
<img class="pubimg hide-mobile" src="img/2018-preprint.png">
</td>
<td class="last">
<b>Robust recovery of time-varying functional connectivity in MEG</b><br>
<u>Rahul Nadkarni</u>, Nicholas J. Foti, and Emily B. Fox<br>
<em>tech report</em>, 2018<br>
<a class="btn btn-default btn-xs" href="pdf/2018-preprint.pdf"><i class="fa fa-file-pdf-o" aria-hidden="true"></i>paper</a>
</td>
</tr>
<tr>
<td class="last">
<img class="pubimg hide-mobile" src="img/2017-nips-bigneuro.png">
</td>
<td class="last">
<b>Learning dynamic functional connectivity networks from infant magnetoencephalography data</b><br>
<u>Rahul Nadkarni</u>, Nicholas J. Foti, and Emily B. Fox<br>
<em>NeurIPS BigNeuro Workshop</em>, 2017<br>
<a class="btn btn-default btn-xs" href="pdf/2017-nips-bigneuro-abstract.pdf"><i class="fa fa-file-pdf-o" aria-hidden="true"></i>abstract</a>
<a class="btn btn-default btn-xs" href="pdf/2017-nips-bigneuro-poster.pdf"><i class="fa fa-map-o" aria-hidden="true"></i>poster</a>
</td>
</tr>
<tr>
<td class="last">
<img class="pubimg hide-mobile" src="img/2017-preprint.png">
</td>
<td class="last">
<b>Sparse plus low-rank graphical models of time series to infer functional connectivity from MEG recordings</b><br>
<u>Rahul Nadkarni</u>, Nicholas J. Foti, Adrian KC Lee, and Emily B. Fox<br>
<em>tech report</em>, 2017<br>
<a class="btn btn-default btn-xs" href="pdf/2017-preprint.pdf"><i class="fa fa-file-pdf-o" aria-hidden="true"></i>paper</a>
</td>
</tr>
<tr>
<td class="last">
<img class="pubimg hide-mobile" src="img/2016-kdd-milets.png">
</td>
<td class="last">
<b>Sparse plus low-rank graphical models of time series for functional connectivity in MEG</b><br>
Nicholas J. Foti, <u>Rahul Nadkarni</u>, Adrian KC Lee, and Emily B. Fox<br>
<em>SIGKDD Workshop on Mining and Learning from Time Series</em>, 2016<br>
<a class="btn btn-default btn-xs" href="pdf/2016-kdd-milets-paper.pdf"><i class="fa fa-file-pdf-o" aria-hidden="true"></i>paper</a>
<a class="btn btn-default btn-xs" href="pdf/2016-kdd-milets-slides.pdf"><i class="fa fa-file-powerpoint-o" aria-hidden="true"></i>slides</a>
<a class="btn btn-default btn-xs" href="https://youtu.be/7DsbLaRWcIM"><i class="fa fa-film" aria-hidden="true"></i>talk</a>
</td>
</tr>
</table>
</div>
<div class="professional">
<h3>Professional Experience</h3>
<table>
<tr>
<td>
<img class="companyimg hide-mobile" src="img/facebook-logo.png">
</td>
<td>
<b>Facebook</b><br>
SWE Intern, Machine Learning (Ph.D.)<br>
June – September 2021
</td>
</tr>
<tr>
<td>
<img class="companyimg hide-mobile" src="img/google-logo.png">
</td>
<td>
<b>Google</b><br>
Software Engineering Intern, Ph.D.<br>
June – September 2017
</td>
</tr>
</table>
</div>
<div class="courses">
<h3>Graduate Coursework</h3>
<ul>
<li>Natural Language Processing (CSE 517)</li>
<li>Machine Learning for Big Data (CSE 547)</li>
<li>Computer Vision (CSE 576)</li>
<li>Graphical Models (CSE 515)</li>
<li>Statistical Inference (STAT 512)</li>
<li>Convex Optimization (EE 578)</li>
<li>Online and Adaptive Methods for Machine Learning (CSE 599I)</li>
<li>Computational Neuroscience (CSE 528)</li>
<li>Algorithms (CSE 527)</li>
<li>Databases (CSE 544)</li>
</ul>
</div>
<div class="teaching">
<h3>Teaching Experience</h3>
<ul>
<li>TA, Data Structures and Algorithms (CSE 373) - Autumn 2015, Winter 2016</li>
<li>TA, Introduction to Artificial Intelligence (CSE 415) - Spring 2016, Autumn 2020</li>
</ul>
</div>
</div>
</div>
</body>
</html>