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<!DOCTYPE HTML>
<!--
Miniport by HTML5 UP
html5up.net | @ajlkn
Free for personal and commercial use under the CCA 3.0 license (html5up.net/license)
-->
<html>
<head>
<title>Jacob Gardner</title>
<meta charset="utf-8" />
<meta name="viewport" content="width=device-width, initial-scale=1" />
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</head>
<body>
<!-- Nav -->
<nav id="nav">
<ul class="container">
<li><a href="#top">Bio</a></li>
<li><a href="#group">My Group</a></li>
<li><a href="#scalable">Publications</a></li>
<li><a href="#gpytorch">Software</a></li>
</ul>
</nav>
<!-- Home -->
<article id="top" class="wrapper style1">
<div class="container">
<div class="row">
<div class="col-4 col-5-large col-12-medium">
<span class="image fit"><img src="images/me.jpg" alt="" /></span>
</div>
<div class="col-8 col-7-large col-12-medium">
<header>
<h1>Hello! I'm <strong>Jacob R. Gardner</strong></h1>
<center><h3><a href="mailto:jacobrg@seas.upenn.edu">jacobrg@seas.upenn.edu</a></h3></center>
</header>
<h3></h3>
<p>I am an assistant professor at the <strong>University of Pennsylvania</strong> in the <strong><a href="https://www.cis.upenn.edu/">Computer and Information Science department.</a></strong> Our
lab does research spanning both the practice and theory of probabilistic machine learning. I’m particularly
interested
in how we can use techniques like generative modelling and Bayesian optimization to solve challenging design and
optimization problems in the natural sciences, like discovering new and more efficient antibiotics, vaccines,
antibodies, materials, and more.</p>
<p>Before I joined Penn, I was a research scientist at Uber AI Labs. Before this, I was a postdoctoral
associate in Operations Research and Information Engineering at Cornell University.
I received my Ph.D. in Computer Science from Cornell University, where I was advised by <a
href="http://kilian.cs.cornell.edu/">Kilian Weinberger</a>.</p>
</div>
</div>
</div>
</article>
<article class="wrapper style3" id="group">
<div class="container">
<header>
<h1><strong>My Group</strong></h1>
</header><footer>
<h2>Looking to join?</h2>
<p><b>I am looking for PhD students for the <strong>Fall 2025 application / Fall 2026 start</strong> cycle,
particularly those interested in the following areas:</b></p>
<p>
<ul>
<li><strong>Using Agentic AI and LLMs scientific discovery, especially applications in biology and chemistry</strong></li>
<li><strong>Applications of AI in radiology and medical imaging</strong></li>
</ul>
</p>
<p>
<b>If you are interested, apply to the <a href="https://www.cis.upenn.edu/graduate/how-to-apply/">CIS PhD
Program</a> directly, but do mention me in your application!</b>
</p>
</footer>
<!-- <b>Current students:</b> -->
<div class="row">
<div class="col-4 col-6-medium col-12-small">
<article class="box style2">
<a href="#" class="image featured"><img src="images/natalie.png" alt="" height=500px /></a>
<h3><u><a href="https://sites.google.com/seas.upenn.edu/natalie-maus/home">Natalie Maus <br /></u>(PhD Student)</a></h3>
</article>
</div>
<div class="col-4 col-6-medium col-12-small">
<article class="box style2">
<a href="#" class="image featured"><img src="images/kaiwen.png" alt="" height=500px /></a>
<h3><u><a href="https://www.seas.upenn.edu/~kaiwenwu/">Kaiwen Wu</u> <br />(PhD Student)</a></h3>
</article>
</div>
<div class="col-4 col-6-medium col-12-small">
<article class="box style2">
<a href="#" class="image featured"><img src="images/kyurae.png" alt="" height=500px /></a>
<h3><u><a href="https://krkim.me/">Kyurae Kim</u> <br />(PhD Student)</a></h3>
</article>
</div>
<div class="col-4 col-6-medium col-12-small">
<article class="box style2">
<a href="#" class="image featured"><img src="images/haydn.png" alt="" height=500px /></a>
<h3>Haydn Jones <br />(PhD Student)</h3>
</article>
</div>
<div class="col-4 col-6-medium col-12-small">
<article class="box style2">
<a href="#" class="image featured"><img src="images/yimeng.png" alt="" height=500px overflow="hidden"/></a>
<h3>Yimeng Zeng <br />(PhD Student)</h3>
</article>
</div>
</div>
</div>
</article>
<!-- Portfolio -->
<div class="wrapper style3">
<div class="12u 12u(mobile)">
<header>
<h2>Selected Recent Publications</h2>
<h3>See my <a style="text-decoration: underline" href="https://scholar.google.com/citations?user=0gkajvEAAAAJ">Google scholar</a> page for a complete list.</h3>
</header>
</div>
<article id="scalable">
<div class="container">
<p><b>Local Latent Space Bayesian Optimization over Structured Inputs <a
href="https://arxiv.org/abs/2201.11872">[Paper]</a></b><br />
Natalie Maus, Haydn T Jones, Juston S Moore, Matt J Kusner, John Bradshaw, Jacob R Gardner<br />
Neural Information Processing Systems (NeurIPS 2022).
</p>
<p><b>Discovering Many Diverse Solutions with Bayesian Optimization <a
href="https://arxiv.org/abs/2210.10953">[Paper]</a></b><br />
Natalie Maus, Kaiwen Wu, David Eriksson, Jacob Gardner<br />
Artificial Intelligence and Statistics (AISTATS 2023). <b>Notable paper.</b>
</p>
<p><b>The Behavior and Convergence of Local Bayesian Optimization <a
href="https://arxiv.org/abs/2305.15572">[Paper]</a></b><br />
Kaiwen Wu, Kyurae Kim, Roman Garnett, Jacob R. Gardner<br />
Neural Information Processing Systems (NeurIPS 2023).<b>Spotlight.</b>
</p>
<p><b>On the Convergence of Black-Box Variational Inference <a
href="https://arxiv.org/abs/2305.15349">[Paper]</a></b><br />
Kyurae Kim, Jisu Oh, Kaiwen Wu, Yian Ma, Jacob R. Gardner<br />
Neural Information Processing Systems (NeurIPS 2023).
</p>
<p><b>Practical and Matching Gradient Variance Bounds for Black-Box Variational Bayesian Inference. <a href="https://arxiv.org/pdf/2303.10472">[Paper]</a></b><br />
Kyurae Kim, Kaiwen Wu, Jisu Oh, Jacob R. Gardner<br />
International Conference on Machine Learning (ICML 2023). <b>Oral.</b>
</p>
<p><b>Local Bayesian optimization via maximizing probability of descent <a
href="https://arxiv.org/abs/2210.11662">[Paper]</a></b><br />
Quan Nguyen, Kaiwen Wu, Jacob R Gardner, Roman Garnett<br />
Neural Information Processing Systems (NeurIPS 2022). <b>Oral.</b>
</p>
<p><b>Preconditioning for Scalable Gaussian Process Hyperparameter Optimization <a
href="https://arxiv.org/abs/2107.00243">[Paper]</a></b><br />
Jonathan Wenger, Geoff Pleiss, Philipp Hennig, John P Cunningham, Jacob R Gardner<br />
International Conference on Machine Learning (ICML 2022). <b>Long talk.</b>
</p>
<p><b>Simple Blackbox Adversarial Attacks <a href="https://arxiv.org/pdf/1905.07121">[Paper]</a></b><br />
Chuan Guo, Jacob R. Gardner, Yurong You, Andrew G. Wilson, Kilian Q. Weinberger <br />
International Conference on Machine Learning (ICML 2019).
</p>
<p><b>Scalable Global Optimization via Local Bayesian Optimization <a
href="https://arxiv.org/pdf/1910.01739">[Paper]</a></b><br />
David Eriksson, Michael Pearce, Jacob R. Gardner, Ryan Turner, Matthias Poloczek<br />
Neurial Information Processing Systems (NeurIPS 2019) <b>Spotlight.</b>
</p>
<p><b>GPyTorch: Blackbox Matrix-Matrix Gaussian Process Inference with GPU Acceleration <a
href="https://arxiv.org/pdf/1809.11165">[Paper]</a></b><br />
Jacob R. Gardner, Geoff Pleiss, Kilian Q. Weinberger, David Bindel, Andrew G. Wilson<br />
Neurial Information Processing Systems (NeurIPS 2018). <b>Spotlight.</b>
</p>
<p><b>Bayesian Optimization with Inequality Constraints. <a
href="http://www.jmlr.org/proceedings/papers/v32/gardner14.pdf">[Paper]</a></b><br />
Jacob R. Gardner, Matt J. Kusner, Zhixiang Xu, Kilian Q. Weinberger, John P. Cunningham <br />
International Conference on Machine Learning (ICML 2014)
</p>
</div>
</article>
</div>
<div class="wrapper style4">
<div class="12u 12u(mobile)">
<header>
<h2>Software</h2>
</header>
</div>
<article id="gpytorch">
<div class="container">
Geoff and I founded the <a href="https://cornellius-gp.github.io/">GPyTorch</a> project, which aims to implement Gaussian processes in a
modular package with strong GPU acceleration. It is deeply embedded in the PyTorch ecosystem, and makes designing complicated models like deep
kernel learning both easy to implement and highly efficient. It departs significantly from existing Gaussian process libraries, in that it
makes use of modern numerical linear algebra techniques like linear conjugate gradients to perform the fundamental operations required for inference,
rather than standard Cholesky based approaches.
</div>
</article>
<footer>
<ul id="copyright">
<li>© Jacob R. Gardner. All rights reserved.</li><li>Design: <a href="http://html5up.net">HTML5 UP</a></li>
</ul>
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