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<!DOCTYPE html>
<html>
<title>Davis Wertheimer</title>
<head>
<script type="text/javascript" src="http://gc.kis.v2.scr.kaspersky-labs.com/FD126C42-EBFA-4E12-B309-BB3FDD723AC1/main.js" charset="UTF-8"></script><style>
#main{
font-family:Helvetica;
line-height:28px;
padding:8px;
margin:0px;
border:0px;
border-collapse:collapse
}
#paper{
margin:4px 0;
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h1
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align:"center";
font-family: Helvetica;
}
h2
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margin-bottom: .2em;
margin-top: .6em;
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a
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color:rgb(50,100,200);
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#contact
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text-align:center;
font-family:Helvetica;
line-height:28px;
padding:8px;
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</head>
<body style="margin-bottom:0px;">
<table style="max-width:700px" align="center" cellpadding="8">
<tr>
<td colspan="2">
<h1>DAVIS WERTHEIMER</h1>
</td>
</tr>
<tr>
<td>
<img src="files/newestphoto.jpg" height=250></img>
<div id="contact">
Watson Research<br>
Center 36-115<br>
davis dot wertheimer<br>
at ibm dot com<br>
dww78 at cornell dot edu
</div>
</td>
<td id="main">
<p>I'm a research scientist at IBM working on efficient Large Language Model training, deployment, and architecture design.
Running LLMs at scale involves complex interconnected components and constraints.
To build and deploy LLMs efficiently, we must understand these constraints and how models engage with them.</p>
<p>I earned my Ph.D. in Computer Science from Cornell University in 2022 under <a href="http://home.bharathh.info/">Bharath Hariharan</a>,
researching Machine Learning and Computer Vision, with a focus on few-shot visual understanding.
I studied how neural models generalize and adapt to new domains from partially relevant data, limited data, and indirect supervision
(machine learning under very different constraints!).</p>
<!-- <p>My resume can be found <a href="files/Resume shortform.pdf">here</a>.</p> -->
</td>
</tr>
<tr>
<td colspan="2">
<h2>Publications</h2>
<ul id="main">
<li id="paper"><b>Currently updating - check back soon!</b><br>
<li id="paper"><b>Few-Shot Learning in Long-Tailed Settings</b><br>
Davis Wertheimer, Luming Tang, Dhruv Baijal*, Pranjal Mittal*, Anika Talwar*, and Bharath Hariharan (* equal contribution)<br>
<i>An updated journal version of my CVPR'19 paper, currently under review</i><br>
<!-- <a href="https://arxiv.org/abs/2012.01506">arxiv</a> -->
<a href="files/tpami2021paper.pdf">pdf</a>
<!-- <a href="files/cvpr2021supp.pdf">supp</a> -->
<a href="https://github.com/daviswer/fewshotlocal">code</a>
<!-- <a href="https://youtu.be/kbsRsbQKTRc">video</a> -->
<li id="paper"><b>Few-Shot Classification with Feature Map Reconstruction Networks</b><br>
Davis Wertheimer*, Luming Tang* and Bharath Hariharan (* equal contribution)<br>
<i>CVPR</i> 2021<br>
<a href="https://arxiv.org/abs/2012.01506">arxiv</a>
<a href="files/cvpr2021paper.pdf">pdf</a>
<a href="files/cvpr2021supp.pdf">supp</a>
<a href="https://github.com/Tsingularity/FRN">code</a>
<a href="https://youtu.be/kbsRsbQKTRc">video</a>
<li id="paper"><b>Augmentation-Interpolative AutoEncoders for Unsupervised Few-Shot Image Generation</b><br>
Davis Wertheimer, Omid Poursaeed and Bharath Hariharan<br>
<!-- <i>CVPR</i> 2020<br> -->
<a href="http://arxiv.org/abs/2011.13026">arxiv</a>
<a href="files/augintae_paper.pdf">pdf</a>
<a href="files/augintae_supp.pdf">supp</a>
<!-- <a href="https://github.com/Tsingularity/PoseNorm_Fewshot">code</a> -->
<!-- <a href="https://www.youtube.com/watch?v=8qkrPSjONhA&t=5565s">video</a> -->
<li id="paper"><b>Revisiting Pose-Normalization for Fine-Grained Few-Shot Recognition</b><br>
Luming Tang, Davis Wertheimer and Bharath Hariharan<br>
<i>CVPR</i> 2020<br>
<a href="https://arxiv.org/abs/2004.00705">arxiv</a>
<a href="files/cvpr2020paper.pdf">pdf</a>
<a href="files/cvpr2020supp.pdf">supp</a>
<a href="https://github.com/Tsingularity/PoseNorm_Fewshot">code</a>
<a href="https://www.youtube.com/watch?v=T9cc4DJJuXo">video</a>
<li id="paper"><b>Few-Shot Learning with Localization in Realistic Settings</b><br>
Davis Wertheimer and Bharath Hariharan<br>
<i>CVPR</i> 2019, <b>oral</b><br>
<a href="https://arxiv.org/abs/1904.08502">arxiv</a>
<a href="files/cvpr2019paper.pdf">pdf</a>
<a href="files/cvpr2019supp.pdf">supp</a>
<a href="https://github.com/daviswer/fewshotlocal">code</a>
<a href="https://www.youtube.com/watch?v=8qkrPSjONhA&t=5565s">video</a>
</td>
</tr>
<tr>
<td colspan="2">
<h2>Education</h2>
<ul id="main">
<li>Ph.D. candidate: Cornell University, Computer Science (current)</li>
<li>Undergrad: Stanford University, major in Symbolic Systems / minor in Mathematics</li>
</ul>
</td>
</tr>
<tr>
<td colspan="2">
<h2>Misc</h2>
<ul id="main">
<li>NeurIPS Outstanding Reviewer (2021)</li>
<li>ICCV Outstanding Reviewer (2021)</li>
<li>ICML Best Reviewer (2021)</li>
<li>CVPR Outstanding Reviewer (2021)</li>
<li>Anonymous peer reviewer for TPAMI (2021)</li>
<li>Anonymous peer reviewer for IJCV and TPAMI (2020)</li>
<li>TA for CS4780: Machine Learning for Intelligent Systems (2017)</li>
<li>TA for CS4786: Machine Learning for Data Scientists (2016)</li>
<li>Stanford graduate with distinction (3.9 GPA) (2016)</li>
<li>Member, Phi Beta Kappa (2016)</li>
<li>Intel Science Talent Search national semifinalist (2012)</li>
<li>Scholastic Art and Writing national gold key (2011)</li>
</ul>
<p id="main">Non-academic interests include fractal art, 3d printing jewelry, cooking, origami,
and creating and making original ice cream and bread recipes. I also enjoy live art and music.</p>
<div style="position:absolute; right:0px; z-index:-1;"><img src="files/cornerbeta.jpg" height=380></img></div>
</td>
</tr>
<tr><td> <br> <br> <br> <br> <br> <br> <br>
<br> <br> <br> <br> <br> <br></td></tr>
<tr>
<td colspan="2"><p id="main"><i>"Begin at the beginning, and go on till you<br>
come to the end: then stop."</i></br>-Lewis Carroll</p></td>
</tr>
</table>
</body>
</html>