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<!DOCTYPE html>
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<title>Home</title>
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<a class="site-title" href="/">Christopher Aicher</a>
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<h2>Bio</h2>
<p><img src="images/christopher_aicher_ski.jpg" alt="Christopher Aicher" align="right" style="height:140px; width:140px; margin:0px 20px">
I am a quantitative researcher at <a href="https://www.citadelsecurities.com/">Citadel Securities</a> in Chicago.
I received a PhD from the <a href="http://www.stat.washington.edu/">Department of Statistics</a> at the University of Washington where I was advised by <a href="https://www.stat.washington.edu/%7Eebfox/">Emily B. Fox</a>.
Prior to my PhD at the University of Washington, I received a BS and MS degree from the <a href="https://www.colorado.edu/amath/">Department of Applied Mathematics</a> at the University of Colorado where I was advised by <a href="http://tuvalu.santafe.edu/%7Eaaronc/">Aaron Clauset</a>.</p>
<h2>Research</h2>
<p>My research interests are in scalable approximate inference methods for machine learning,
specifically using deterministic Bayesian approximations (e.g. Expectation Propagation and Variational Inference)
and stochastic gradient MCMC methods (e.g. SGLD).
My research projects include:</p>
<ul>
<li><a href="https://arxiv.org/abs/1810.09098">Stochastic Gradient Methods for Time Series</a></li>
<li><a href="https://arxiv.org/abs/1905.07473">Adaptive Truncation of Backpropagation in Recurrent Neural Networks</a></li>
<li><a href="https://arxiv.org/abs/1404.0431">Community Detection in Weighted Network Data using Variational Inference</a></li>
<li><a href="https://arxiv.org/abs/1807.07621">Time Series Clustering using Expectation Propagation</a></li>
</ul>
<h2>Experience</h2>
<ul>
<li>Quantitative Researcher, <em>Citadel Securites</em> (<em>03/20-Current</em>)
<ul>
<li>Applying statistical/ML techinques and engineering skills to model markets,
test hypotheses and develop proprietary algorithms.</li>
</ul></li>
<li>Research Intern, <em>Microsoft AI and Research</em> (<em>06/17-09/17</em>)
<ul>
<li>Worked with Consumer Data & Analytics team on short-form text clustering.</li>
<li>Developed an online feature extractor using RNNs and non-parametric clustering.</li>
</ul></li>
<li>Research Scientist Intern, <em>Amazon</em> (<em>06/16-09/16</em>)
<ul>
<li>Worked with the Kindle devices demand planning team on forecasting sales.</li>
<li>Developed a custom R package for prototyping new models.</li>
<li>Tested and integrated quantile random forests to improve short-term forecasting</li>
</ul></li>
<li>Machine Learning Intern, <em>Dato</em> (now <em>Turi/Apple</em>) (<em>06/15-09/15</em>)
<ul>
<li>Researched, developed, and shipped a new itemset mining toolkit as part
of GraphLab Create's machine learning applications library.</li>
</ul></li>
</ul>
<h2>Publications</h2>
<ol class="bibliography"><li><span id="aicher2020scalable"><b>Aicher, C.</b> (2020). <i>Scalable Learning in Latent State Sequence Models</i> [PhD thesis]. University of Washington.</span>
<a href=https://digital.lib.washington.edu/researchworks/handle/1773/45550> [PDF] </a>
<details style="display:inline">
<summary>BibTex</summary>
<pre> @phdthesis{aicher2020scalable,
title = {Scalable Learning in Latent State Sequence Models},
author = {Aicher, Christopher},
school = {University of Washington},
year = {2020},
link = {https://digital.lib.washington.edu/researchworks/handle/1773/45550}
}
</pre>
</details>
</li>
<li><span id="aicher2019tbptt"><b>Aicher, C.</b>, Foti, N. J., & Fox, E. B. (2019). Adaptively Truncating Backpropagation Through Time to Control Gradient Bias. <i>Uncertainty in Artificial Intelligence</i>.</span>
<a href=https://arxiv.org/abs/1905.07473> [PDF] </a>
<a href=https://github.com/aicherc/adaptive_tbptt > [Code] </a>
 
<details style="display:inline">
<summary>BibTex</summary>
<pre> @article{aicher2019tbptt,
title = {Adaptively Truncating Backpropagation Through Time to Control Gradient Bias},
author = {Aicher, Christopher and Foti, Nicholas J. and Fox, Emily B.},
journal = {Uncertainty in Artificial Intelligence},
year = {2019},
month = may,
link = {https://arxiv.org/abs/1905.07473},
code = {https://github.com/aicherc/adaptive_tbptt}
}
</pre>
</details>
</li>
<li><span id="aicher2019nonlinear"><b>Aicher, C.</b>, Putcha, S., Nemeth, C., Fearnhead, P., & Fox, E. B. (2019). Stochastic Gradient MCMC for Nonlinear State Space Models. <i>ArXiv Preprint ArXiv:1901.10568</i>.</span>
<a href=https://arxiv.org/abs/1901.10568> [PDF] </a>
<a href=https://github.com/aicherc/sgmcmc_ssm_code > [Code] </a>
 
<details style="display:inline">
<summary>BibTex</summary>
<pre> @article{aicher2019nonlinear,
title = {Stochastic Gradient MCMC for Nonlinear State Space Models},
author = {Aicher, Christopher and Putcha, Srshti and Nemeth, Christopher and Fearnhead, Paul and Fox, Emily B.},
journal = {arXiv preprint arXiv:1901.10568},
year = {2019},
month = jan,
link = {https://arxiv.org/abs/1901.10568},
code = {https://github.com/aicherc/sgmcmc_ssm_code}
}
</pre>
</details>
</li>
<li><span id="aicher2019stochastic"><b>Aicher, C.</b>, Ma, Y.-A., Foti, N. J., & Fox, E. B. (2019). Stochastic Gradient MCMC for State Space Models. <i>SIAM Journal on Mathematics of Data Science</i>, <i>1</i>(3), 555–587.</span>
<a href=https://arxiv.org/abs/1810.09098> [PDF] </a>
<a href=https://github.com/aicherc/sgmcmc_ssm_code > [Code] </a>
 
<details style="display:inline">
<summary>BibTex</summary>
<pre> @article{aicher2019stochastic,
title = {Stochastic Gradient MCMC for State Space Models},
author = {Aicher, Christopher and Ma, Yi-An and Foti, Nicholas J and Fox, Emily B},
journal = {SIAM Journal on Mathematics of Data Science},
volume = {1},
number = {3},
pages = {555--587},
year = {2019},
publisher = {SIAM},
link = {https://arxiv.org/abs/1810.09098},
code = {https://github.com/aicherc/sgmcmc_ssm_code}
}
</pre>
</details>
</li>
<li><span id="aicher2018approximate"><b>Aicher, C.</b>, & Fox, E. B. (2018). Approximate Collapsed Gibbs Clustering with Expectation Propagation. <i>ArXiv Preprint ArXiv:1807.07621</i>.</span>
<a href=https://arxiv.org/abs/1807.07621> [PDF] </a>
<a href=https://github.com/aicherc/EP_Collapsed_Gibbs > [Code] </a>
 
<details style="display:inline">
<summary>BibTex</summary>
<pre> @article{aicher2018approximate,
title = {Approximate Collapsed Gibbs Clustering with Expectation Propagation},
author = {Aicher, Christopher and Fox, Emily B.},
journal = {arXiv preprint arXiv:1807.07621},
year = {2018},
month = jul,
link = {https://arxiv.org/abs/1807.07621},
code = {https://github.com/aicherc/EP_Collapsed_Gibbs}
}
</pre>
</details>
</li>
<li><span id="simonen2018embodied">Simonen, K., Huang, M., <b>Aicher, C.</b>, & Morris, P. (2018). Embodied Carbon as a Proxy for the Environmental Impact of Earthquake Damage Repair. <i>Energy and Buildings</i>.</span>
<a href=https://www.sciencedirect.com/science/article/pii/S0378778817319710> [PDF] </a>
<details style="display:inline">
<summary>BibTex</summary>
<pre> @article{simonen2018embodied,
title = {Embodied Carbon as a Proxy for the Environmental Impact of Earthquake Damage Repair},
author = {Simonen, K and Huang, M and Aicher, C and Morris, P},
journal = {Energy and Buildings},
year = {2018},
month = jan,
link = {https://www.sciencedirect.com/science/article/pii/S0378778817319710}
}
</pre>
</details>
</li>
<li><span id="aicherc2016scalable"><b>Aicher, C.</b>, & Fox, E. B. (2016). Scalable Clustering of Correlated Time Series Using Expectation Propagation. <i>SIGKDD Workshop on MiLeTS</i>.</span>
<a href=/pdf/aicherc2016scalable.pdf> [PDF] </a>
<a href=https://github.com/aicherc/EP_Collapsed_Gibbs > [Code] </a>
 
<details style="display:inline">
<summary>BibTex</summary>
<pre> @article{aicherc2016scalable,
title = {Scalable Clustering of Correlated Time Series Using Expectation Propagation},
author = {Aicher, Christopher and Fox, Emily B.},
journal = {SIGKDD Workshop on MiLeTS},
year = {2016},
code = {https://github.com/aicherc/EP_Collapsed_Gibbs}
}
</pre>
</details>
</li>
<li><span id="aicher2015learning"><b>Aicher, C.</b>, Jacobs, A. Z., & Clauset, A. (2015). Learning Latent Block Structure in Weighted Networks. <i>Journal of Complex Networks</i>, <i>3</i>(2), 221–248.</span>
<a href=http://arxiv.org/abs/1404.0431> [PDF] </a>
<a href=http://tuvalu.santafe.edu/%7Eaaronc/wsbm/ > [Code] </a>
 
<details style="display:inline">
<summary>BibTex</summary>
<pre> @article{aicher2015learning,
title = {Learning Latent Block Structure in Weighted Networks},
author = {Aicher, Christopher and Jacobs, Abigail Z and Clauset, Aaron},
journal = {Journal of Complex Networks},
volume = {3},
number = {2},
pages = {221--248},
year = {2015},
publisher = {Oxford University Press},
link = {http://arxiv.org/abs/1404.0431},
code = {http://tuvalu.santafe.edu/%7Eaaronc/wsbm/}
}
</pre>
</details>
</li>
<li><span id="aicher2013adapting"><b>Aicher, C.</b>, Jacobs, A. Z., & Clauset, A. (2013). Adapting the Stochastic Block Model to Edge-Weighted Networks. <i>ICML Workshop on Structured Learning</i>.</span>
<a href=http://arxiv.org/abs/1305.5782> [PDF] </a>
<a href=http://tuvalu.santafe.edu/%7Eaaronc/wsbm/ > [Code] </a>
 
<details style="display:inline">
<summary>BibTex</summary>
<pre> @article{aicher2013adapting,
title = {Adapting the Stochastic Block Model to Edge-Weighted Networks},
author = {Aicher, Christopher and Jacobs, Abigail Z and Clauset, Aaron},
journal = {ICML Workshop on Structured Learning},
year = {2013},
link = {http://arxiv.org/abs/1305.5782},
code = {http://tuvalu.santafe.edu/%7Eaaronc/wsbm/}
}
</pre>
</details>
</li></ol>
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<h2>Teaching</h2>
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<li>Teaching Assistant (University of Washington)
<ul>
<li>Statistical Methods in Engineering and Science (STAT 390)</li>
</ul></li>
<li>Learning Assistant (University of Colorado)
<ul>
<li>Applied Probability (APPM 3570)</li>
<li>Mathematical Statistics (APPM 4570)</li>
<li>Matrix Methods and Applications (APPM 3310)</li>
<li>Calculus 2 (APPM 1360)</li>
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