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<div id="content" class="content">
<header>
<h1 class="title">Akira Horiguchi (he/him)</h1>
</header>
<div id="outline-container-orgd7cb831" class="outline-2">
<h2 id="orgd7cb831">Welcome!</h2>
<div class="outline-text-2" id="text-orgd7cb831">
<p>
I am a Visiting Assistant Professor at the Department of Statistics at University of California, Davis.
<b>Starting Fall 2026, I will be an Assistant Professor at the Department of Mathematics and Statistics at Georgetown University.</b>
</p>
<ul class="org-ul">
<li>Previously I was a Postdoctoral Associate of the Department of Statistical Science at Duke University, working with Professors <a href="https://codas.uchicago.edu/people/li-ma/">Li Ma</a> and <a href="https://scholars.duke.edu/person/cliburn.chan">Cliburn Chan</a>.</li>
<li>I obtained my Ph.D. and M.S. in Statistics from The Ohio State University, advised by Professors <a href="https://stat.indiana.edu/about/core-faculty/core-faculty/pratola-matthew.html">Matthew T. Pratola</a> and <a href="https://www.asc.ohio-state.edu/santner.1/">Thomas J. Santner</a>.</li>
<li>I obtained my B.S. in Mathematics from the University of Maryland.</li>
</ul>
<p>
Here are my <a href="https://github.com/ahoriguchi/ahoriguchi.github.io/blob/master/CV0.pdf">CV</a> and other identifiers: <a href="https://www.researchgate.net/profile/Akira-Horiguchi-2">Research Gate</a>, <a href="https://orcid.org/0000-0002-7523-9636">orcid</a>, <a href="https://statistics.ucdavis.edu/people/akira-horiguchi">UC Davis</a>, <a href="https://github.com/ahoriguchi/ahoriguchi.github.io/blob/master/headshot.JPG">headshot</a>, <a href="https://github.com/ahoriguchi/ahoriguchi.github.io/blob/master/shortbio.txt">short bio</a>.
</p>
</div>
</div>
<div id="outline-container-org8359568" class="outline-2">
<h2 id="org8359568">Research</h2>
<div class="outline-text-2" id="text-org8359568">
<p>
My research interests are motivated by the modern statistical challenges in scientific applications such as climate science, computer experiments, and biomedical science, and involve nonparametric statistical approaches that quantify uncertainty in a principled way.
Specific research areas include:
</p>
<ul class="org-ul">
<li><b>Multi-level study designs</b>: minimax rates, adaptive estimation, application to biomedical science</li>
<li><b>Bayesian nonparametrics</b>: stick-breaking models, model-based clustering, covariate-dependent mixture models, application to flow cytometry</li>
<li><b>Computer experiments</b>: sensitivity analysis, Pareto optimization, Bayesian regression trees, uncertainty quantification</li>
</ul>
<p>
Selected works:
</p>
<dl class="org-dl">
<dt><a href="https://www3.stat.sinica.edu.tw/statistica/fp/SS-2024-0318.html#about">Estimating Shapley Effects in Big-Data Emulation and Regression Settings using Bayesian Additive Regression Tree</a> (2026+) with <a href="https://arxiv.org/pdf/2304.03809">arXiv</a> and <a href="https://bitbucket.org/mpratola/openbt/src/master/">code</a></dt><dd><b>A. Horiguchi</b> and M.T. Pratola. <i>Statistica Sinica, to appear.</i></dd>
<dt><a href="https://projecteuclid.org/journals/bayesian-analysis/advance-publication/A-Tree-Perspective-on-Stick-Breaking-Models-in-Covariate-Dependent/10.1214/24-BA1462.full">A tree perspective on stick-breaking models in covariate-dependent mixtures</a> (2025, Editor-selected discussion paper) with <a href="https://arxiv.org/abs/2208.02806">arXiv</a> and <a href="https://github.com/ahoriguchi/treeSB">code</a></dt><dd><b>A. Horiguchi</b>, C. Chan, and L. Ma. <i>Bayesian Analysis.</i></dd>
<dt><a href="https://www.tandfonline.com/doi/pdf/10.1080/10618600.2025.2572325">Efficient Decision Trees for Tensor Regressions</a> (2025) with <a href="https://arxiv.org/abs/2408.01926">arXiv</a> and <a href="https://github.com/hrluo/TensorDecisionTreeRegressor">code</a></dt><dd>H. Luo, <b>A. Horiguchi</b>, L. Ma. <i>Journal of Computational and Graphical Statistics.</i></dd>
<dt><a href="https://www.tandfonline.com/doi/abs/10.1080/00401706.2021.2008504?journalCode=utch20">Using BART for Perform Pareto Optimization and Quantify its Uncertainties</a> (2022) with <a href="https://arxiv.org/abs/2101.02558v2">arXiv</a> and <a href="https://bitbucket.org/mpratola/openbt/wiki/Home">code</a></dt><dd><b>A. Horiguchi</b>, T.J. Santner, Y. Sun, and M.T. Pratola. <i>Technometrics, Special Issue on Industry 4.0.</i></dd>
<dt><a href="https://www.sciencedirect.com/science/article/abs/pii/S0951832020308784">Assessing variable activity for Bayesian regression trees</a> (2021) with <a href="https://arxiv.org/abs/2005.13622">arXiv</a> and <a href="https://bitbucket.org/mpratola/openbt/wiki/Home">code</a></dt><dd><b>A. Horiguchi</b>, M.T. Pratola, and T.J. Santner. <i>Reliability Engineering & Safety System, Special Issue on Sensitivity Analysis of Model Outputs.</i></dd>
<dt><a href="https://arxiv.org/abs/2310.02968">Sampling depth trade-off in function estimation under a two-level design</a></dt><dd><b>A. Horiguchi</b>, L. Ma, and B.T. Szabó.</dd>
<dt><a href="https://arxiv.org/abs/2601.20254">Wavelet Tree Ensembles for Triangulable Manifolds</a></dt><dd>H. Luo, <b>A. Horiguchi.</b>, and L. Ma.</dd>
<dt><a href="https://arxiv.org/abs/2506.15690">LLM Web Dynamics: Tracing Model Collapse in a Network of LLMs</a></dt><dd>T. Wang, <b>A. Horiguchi</b>, L. Pang, and C.E. Preibe.</dd>
<dt><a href="https://arxiv.org/abs/2509.23002">Unsupervised Conformal Inference: Bootstrapping and Alignment to Control LLM Uncertainty</a></dt><dd>L. Pang, L. Huang, J. Lin, T. Wang, <b>A. Horiguchi</b>, A. Aue, and C.E. Priebe.</dd>
</dl>
</div>
</div>
<div id="outline-container-orgc753384" class="outline-2">
<h2 id="orgc753384">Teaching</h2>
<div class="outline-text-2" id="text-orgc753384">
</div>
<div id="outline-container-orgc8359df" class="outline-3">
<h3 id="orgc8359df">University of California, Davis</h3>
<div class="outline-text-3" id="text-orgc8359df">
<ul class="org-ul">
<li>Spring 2026: <a href="https://ahoriguchi.github.io/teaching/STA141A_2026Q2/website.html">STA 141A (Fundamentals of Statistical Data Science)</a>, <a href="https://ahoriguchi.github.io/teaching/STA35B_2026Q2/website.html">STA 35B (Statistical Data Science II)</a></li>
<li>Fall 2025: STA 141A (Fundamentals of Statistical Data Science), STA 35C (Statistical Data Science III)</li>
<li>Spring 2025: STA 141A (Fundamentals of Statistical Data Science), STA 35B (Statistical Data Science II)</li>
<li>Fall 2024: STA 141A (Fundamentals of Statistical Data Science), STA 35C (Statistical Data Science III)</li>
</ul>
</div>
</div>
<div id="outline-container-orgb0239d6" class="outline-3">
<h3 id="orgb0239d6">The Ohio State University</h3>
<div class="outline-text-3" id="text-orgb0239d6">
<ul class="org-ul">
<li>Spring 2019: BUSMGT 7256 (Tools for Data Analysis)</li>
</ul>
</div>
</div>
</div>
<div id="outline-container-org20acb2e" class="outline-2">
<h2 id="org20acb2e">Resources</h2>
<div class="outline-text-2" id="text-org20acb2e">
<p>
Below are (free!) tools I've found useful.
</p>
</div>
<div id="outline-container-org0722b94" class="outline-3">
<h3 id="org0722b94">Writing</h3>
<div class="outline-text-3" id="text-org0722b94">
<dl class="org-dl">
<dt><a href="https://jmlr.csail.mit.edu/reviewing-papers/knuth_mathematical_writing.pdf">Mathematical Writing</a> by Donald E. Knuth, Tracy Larrabee, and Paul M. Roberts</dt><dd>A quick read.</dd>
<dt><a href="https://georgegopen.com/articles">The Science of Scientific Writing</a> by George Gopen</dt><dd>A classic.</dd>
</dl>
</div>
</div>
<div id="outline-container-orge29cdb2" class="outline-3">
<h3 id="orge29cdb2">Computing</h3>
<div class="outline-text-3" id="text-orge29cdb2">
<dl class="org-dl">
<dt><a href="https://adv-r.hadley.nz/">Advanced R</a> by Hadley Wickham</dt><dd>The sections "Foundations" and "Functional programming" seem to be universally helpful to anyone using R.</dd>
</dl>
</div>
</div>
<div id="outline-container-org9574acd" class="outline-3">
<h3 id="org9574acd">Research</h3>
<div class="outline-text-3" id="text-org9574acd">
<dl class="org-dl">
<dt><a href="https://orgmode.org/">Org Mode</a></dt><dd><a href="https://karl-voit.at/2017/09/23/orgmode-as-markup-only/">Org Mode can do everything Markdown can and also much, much more.</a> I use Org Mode to write my project logs for which <a href="https://orgmode.org/manual/TODO-Basics.html">TODO/DONE tags</a> are handy. I also used Org Mode to create this website.</dd>
<dt><a href="https://quarto.org/">Quarto</a></dt><dd>I use Quarto to share "in-progress" simulation results or data analysis with collaborators.</dd>
<dt><a href="https://www.overleaf.com">Overleaf</a></dt><dd>Like Google Docs, but for LaTeX. It now has a <a href="https://www.overleaf.com/learn/how-to/LaTeX_checklist_for_arXiv_submissions">Overleaf submit to arXiv</a> feature.</dd>
</dl>
</div>
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