Akira Horiguchi

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Welcome!

About

I (he/him/his) am a Postdoctoral Associate of the Department of Statistical Science at Duke University, working with Professors Li Ma and Cliburn Chan.

My other relevant identifiers can be found at Research Gate, orcid, Scholars@Duke.

Research interests

My research interests include

  • Bayesian nonparametrics: stick-breaking models, clustering, covariate-dependent mixture models, application to flow cytometry
  • Bayesian regression trees: sensitivity analysis, computer experiments, multiobjective optimization

CV

A PDF file can be created by first clicking on [Expand all] at the top right of the screen and then printing to save as PDF.

Employment

Postdoctoral Associate (2021-current)
Advised by Li Ma and Cliburn Chan, Department of Statistical Science, Duke University
Dean's Distinguished University Fellow (Jan 2020 - Dec 2020)
Graduate School, The Ohio State University
Co-Instructor in Data Science (Feb 2019)
J.P. Morgan Chase
Graduate Research Assistant (May 2018 - Dec 2019)
Nationwide Insurance
Statistical Consultant (Jan 2018 - May 2018)
Department of Statistics, The Ohio State University
Data Visualization Intern (Jun 2016 - Aug 2016)
NORC at the University of Chicago
Dean’s Distinguished University Fellow (Aug 2015 - May 2017)
Graduate School, The Ohio State University

Education

Ph.D. in Statistics, The Ohio State University (Dec 2020)
Advised by Matthew T. Pratola and Thomas J. Santner
M.S. in Statistics, The Ohio State University (May 2017)
B.S. in Mathematics, University of Maryland (May 2015)
Departmental honors and Gemstone honors college citation.

Awards

Travel Award for ISBA 2022 World Meeting
Scientific Committee of ISBA 2022 World Meeting
Student Travel Award for 2019 Joint Statistical Meetings
Quality and Productivity Section, American Statistical Association
Travel Award for 2019 Industrial Math/Stat Modeling Workshop
The Statistical and Applied Mathematical Sciences Institute (SAMSI)
Dean's Distinguished University Fellowship (2015)
Graduate School, The Ohio State University, covers three years
Undergraduate Researcher of the Year (2014)
Office of Undergraduate Studies, University of Maryland

Publications

In preparation

Tree stick breaking for covariate-dependent mixtures.
A. Horiguchi, L. Ma, C. Chan.

Peer-reviewed journal articles

Using BART for Perform Pareto Optimization and Quantify its Uncertainties (2022) with arXiv and code
A. Horiguchi, T. J. Santner, Y. Sun, and M. T. Pratola. Technometrics, Special Issue on Industry 4.0.
Assessing variable activity for Bayesian regression trees (2021) with arXiv and code
A. Horiguchi, M. T. Pratola, and T. J. Santner. Reliability Engineering & Safety System, Special Issue on Sensitivity Analysis of Model Outputs.

Theses

Bayesian Additive Regression Trees: Sensitivity Analysis and Multiobjective Optimization (2020)
A. Horiguchi. OhioLink.
Improving Photovoltaics with High Luminescence Efficiency Quantum Dot Layers (2015)
J. Chen, D. Gagner, K. Griffiths, E. Hitz, A. Horiguchi, R. Joyce, B. Kim, M. Lee, S. Lee, A. Raul, D. Shyu, Z. Siegel, S. Silberholz, and D. Tran. Digital Repository at the University of Maryland.

Open-source projects

Contributed to the Open Bayesian Trees (OpenBT) project (2020-2021)
Helped incorporate capabilities to perform BART-based Sobol' index calculations and Pareto optmization

Presentations

Reference: (P)=Poster (T)=Talk (IT)=Invited Talk

2022

Using BART for Perform Pareto Optimization and Quantify its Uncertainties. [Invited Talk]
Fall Technical Conference. Park City, UT.
Tree stick breaking for covariate-dependent mixtures. (T)
BNP13. Chile.
Using BART for Perform Pareto Optimization and Quantify its Uncertainties. (IT)
ISBA 2022 World Meeting. Montréal, Canada.
Tree stick breaking for covariate-dependent mixtures. (P)
ISBA 2022 World Meeting. Montréal, Canada.

2021

A flexible regression model for flow cytometry data. (T)
Duke Center for Human Systems Immunology (CHSI) Virtual Symposium.
Using BART for Multiobjective Optimization of Multiple Noisy Objectives. (T)
Quality and Productivity Research Conference. Tallahassee, FL.
Assessing variable activity for Bayesian regression trees. (T)
ISBA 2021 World Meeting. (Moved online due to COVID-19.)

2020

Assessing variable activity for Bayesian regression trees. (T)
13th International Conference of the ERCIM WG on Computational and Methodological Statistics. (Moved online due to COVID-19.)
Assessing variable activity for Bayesian regression trees. (T)
Joint Statistical Meetings. Philadelphia, PA. (Moved online due to COVID-19.)
Assessing variable activity for Bayesian regression trees. (T)
Spring Research Conference, Oakland University. Rochester, MI. (Cancelled due to COVID-19.)

2019

Comparing Variance-Based and Count Methods for Assessing Variable Activity in Bayesian Additive Regression Trees. (P)
Graduate Student Poster Session, Department of Statistics, The Ohio State University. Columbus, OH.
Comparing Variance-Based and Count Methods for Assessing Variable Activity in Bayesian Additive Regression Trees. (P)
Joint Statistical Meetings. Denver, CO.

2015

Increasing Solar Cell Efficiency with a Spin-Coated Layer of Quantum Dots in PLMA. (T)
Team Thesis Conference, University of Maryland. College Park, MD.

2014

Transcription Factors and Cascade Network. (T)
Summer Undergraduate Research Symposium, The Ohio State University. Columbus, OH.
Increasing Solar Cell Efficiency with a Spin-Coated Layer of Quantum Dots in PLMA. (P)
Undergraduate Research Day, University of Maryland. College Park, MD.

2013

No-Analog Communities in Space and Time. (T)
NIMBioS Undergraduate Conference, University of Tennessee. Knoxville, TN.

Teaching

The Ohio State University

BUSMGT 7256: Tools for Data Analysis. Co-instructor (SP19).
This course is designed to introduce students to commonly used software programs in data science and improve students’ problem solving skills and logical thought processes. Students will be exposed to R, SAS, and SPSS.
STAT 5760: Statistical Consulting Support from the SCS. Teaching assistant (SP18).
Graduate or undergraduate students enrolled in this course will work with a graduate student consultant employed by the Statistical Consulting Service (SCS) for the purpose of making progress on their thesis or dissertation.
STAT 6301: Probability for Statistical Inference. Grader (AU17).
Introduction to probability, random variables, and distribution theory; intended primarily for students in Master of Applied Statistics (MAS) degree program.
STAT 5302: Intermediate Data Analysis II. Grader (AU17).
The second course in a two-semester sequence in data analysis covering simple linear regression (inference, model diagnostics), multiple regression models, variable selection, model selection, two-way ANOVA, mixed effects model.

Service

Reviewed for the Journal of the American Statistical Association (2022)
Member of ad hoc committee on junior awards and support offered by the International Society for Bayesian Analysis (2021)
Presented research at the first student-led Student Research Seminar (2020)
Presented research to prospective graduate students at Graduate Information Day (GID) at OSU (2020)
Panelist of the funding and internship session for GID at OSU (2018, 2019)
Volunteered at math booth for Maryland Day (2014, 2015)
President of Pi Mu Epsilon Math Honor Society, UMD chapter (2013-2014)

Resources

Below are (free!) resources that I've found useful in doing research.

Writing

Mathematical Writing by Donald E. Knuth, Tracy Larrabee, and Paul M. Roberts
A quick read.
The Science of Scientific Writing by George Gopen
A classic.

Computing

Whichever language(s) you use, it helps to learn a little more than last time.

Advanced R by Hadley Wickham
The "Foundations" and "Functional programming" sections seem to be universally helpful to any Statistics PhD student who mainly uses R.

Author: Akira Horiguchi

Created: 2022-07-24 Sun 13:27

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