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<div id="content" class="content">
<header>
<h1 class="title">Akira Horiguchi (he/him)</h1>
</header>
<div id="outline-container-org81794bb" class="outline-2">
<h2 id="org81794bb">Welcome!</h2>
<div class="outline-text-2" id="text-org81794bb">
<p>
I am a Visiting Assistant Professor at the Department of Statistics at University of California, Davis.
</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://www2.stat.duke.edu/~lm186/">Li Ma</a> and <a href="https://scholars.duke.edu/person/cliburn.chan">Cliburn Chan</a>.</li>
<li>I obtained my Ph.D. in Statistics from The Ohio State University, advised by Professors <a href="http://www.matthewpratola.com/">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-orgb9960d6" class="outline-2">
<h2 id="orgb9960d6">Research</h2>
<div class="outline-text-2" id="text-orgb9960d6">
<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://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/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://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> <b><b>(2025, Editor-selected discussion paper)</b></b></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">Effficient Decision Trees for Tensor Regressions</a> <b><b>(2025)</b></b></dt><dd>H. Luo, <b>A. Horiguchi</b>, L. Ma. <i>Journal of Computational and Graphical Statistics, accepted.</i></dd>
<dt><a href="https://arxiv.org/abs/2304.03809">Estimating Shapley Effects in Big-Data Emulation and Regression Settings using Bayesian Additive Regression Trees</a> <b><b>(2025)</b></b></dt><dd><b>A. Horiguchi</b> and M. T. Pratola. <i>Statistica Sinica, accepted.</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>
</dl>
</div>
</div>
<div id="outline-container-org5789c62" class="outline-2">
<h2 id="org5789c62">Teaching</h2>
<div class="outline-text-2" id="text-org5789c62">
</div>
<div id="outline-container-org9789fa9" class="outline-3">
<h3 id="org9789fa9">University of California, Davis</h3>
<div class="outline-text-3" id="text-org9789fa9">
<ul class="org-ul">
<li>Fall 2025: <a href="https://ahoriguchi.github.io/teaching/STA141A_2025Q4/website.html">STA 141A (Fundamentals of Statistical Data Science)</a>, <a href="https://ahoriguchi.github.io/teaching/STA35C_2025Q4/website.html">STA 35C (Statistical Data Science III)</a></li>
<li>Spring 2025: <a href="https://ahoriguchi.github.io/teaching/STA141A/website.html">STA 141A (Fundamentals of Statistical Data Science)</a>, <a href="https://ahoriguchi.github.io/teaching/STA35B/website.html">STA 35B (Statistical Data Science II)</a></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-org0d1eedd" class="outline-3">
<h3 id="org0d1eedd">The Ohio State University</h3>
<div class="outline-text-3" id="text-org0d1eedd">
<ul class="org-ul">
<li>Spring 2019: BUSMGT 7256 (Tools for Data Analysis)</li>
</ul>
</div>
</div>
</div>
<div id="outline-container-org198dc42" class="outline-2">
<h2 id="org198dc42">Resources</h2>
<div class="outline-text-2" id="text-org198dc42">
<p>
Below are (free!) tools I've found useful.
</p>
</div>
<div id="outline-container-org3310135" class="outline-3">
<h3 id="org3310135">Writing</h3>
<div class="outline-text-3" id="text-org3310135">
<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-orge409643" class="outline-3">
<h3 id="orge409643">Computing</h3>
<div class="outline-text-3" id="text-orge409643">
<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-org7d056c9" class="outline-3">
<h3 id="org7d056c9">Research</h3>
<div class="outline-text-3" id="text-org7d056c9">
<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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