About

Joshua Villarreal is an Assistant Professor (Clinical) in the Department of Pharmacy at the University of Washington School of Pharmacy. He received his PharmD degree from the University of Arizona and a Masters in Public Administration from the University of Washington. Dr. Villarreal completed his PGY1 Residency at the Hospital of the University of Pennsylvania. His professional interests encompass a variety of pharmacy practice areas, including inpatient care as a general medicine pharmacist, ambulatory care in cardiology, administrative and community care as a Tribal Medicine specialist with the Muckleshoot Indian Tribe, and critical care as an emergency medicine pharmacist at the University of Washington Medical Center. He currently holds a combined role with the University of Washington Medicine’s Department of Pharmacy and the School of Pharmacy, serving as Lead Clinical Educator. In this capacity, he coordinates efforts among pharmacy preceptors and faculty to develop students’ skills and knowledge in pharmacy practice. His contributions include advancing pharmacy education through his leadership in clinical training and his research, which includes publications on health professions enrichment programs and faculty support frameworks.

Research topics

• Political Science
• Sociology
• Nursing
• Medical education
• Psychology
• Medicine
• Social Science
• Computer Science
• Pedagogy

Selected publications

• Machine learning-informed 3+1 sterile neutrino global fits using posterior density estimation of electron disappearance data

  The European Physical Journal C · 2026-04-01

  articleOpen access1st authorCorresponding

  Abstract Global analyses of particle physics data are integral for validating and scrutinizing published results of experiments. Global fits of anomalous oscillation data which search for one or more eV-scale sterile neutrinos are particularly challenging both to evaluate and to reconcile in the global picture. Fits (especially joint ones) to oscillation data suffer from significant computational burdens, such as likelihood intractability, making traditional Markov Chain-Monte Carlo all but impossible. Given evidence both supporting and challenging beyond Standard Model physics across neutrino experiments of various baselines, energies, and detection techniques, the global search for sterile neutrinos requires additional tools in order to determine whether sterile neutrinos remain a viable solution to unexplained anomalies. Furthermore, both a Bayesian and frequentist interpretation of sterile neutrino data is needed for a complete assessment of longstanding tensions in the field. Techniques from the machine learning subfield of simulation-based inference have a natural application to such a problem. In this contribution, we illustrate some of the outstanding questions of the global picture of light sterile neutrinos by focusing on experiments searching with the disappearance of electron (anti)neutrinos, and look to posterior density estimation strategies to craft answers, including comparisons to a machine-learning-based frequentist approach.

  PublisherOA PDFDOI

• A frequentist simulation-based inference treatment of sterile neutrino global fits

  Machine Learning Science and Technology · 2025-09-05 · 2 citations

  articleOpen access1st authorCorresponding

  Abstract A critical challenge in particle physics is combining results from diverse experimental setups that measure the same physical quantity to enhance precision and statistical power, a process known as a global fit. Global fits of sterile neutrino searches, hunts for additional neutrino oscillation frequencies and amplitudes, present an intriguing case study. In such a scenario, the key assumptions underlying Wilks’ theorem, a cornerstone of most classic frequentist analyses, do not hold. The method of Feldman and Cousins, a trials-based approach which does not assume Wilks’ theorem, becomes computationally prohibitive for complex or intractable likelihoods. To bypass this limitation, we borrow a technique from simulation-based inference (SBI) to estimate likelihood ratios for use in building trials-based confidence intervals, speeding up test statistic evaluations by a factor <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" overflow="scroll"> <mml:mrow> <mml:mo>&gt;</mml:mo> <mml:mstyle scriptlevel="0"/> <mml:mstyle scriptlevel="0"/> <mml:mstyle scriptlevel="0"/> <mml:msup> <mml:mn>10</mml:mn> <mml:mn>4</mml:mn> </mml:msup> </mml:mrow> </mml:math> per grid point, resulting in a faster, but approximate, frequentist fitting framework. Applied to a subset of sterile neutrino search data involving the disappearance of muon-flavor (anti)neutrinos, our method leverages machine learning (ML) to compute frequentist confidence intervals while significantly reducing computational expense. In addition, the SBI-based approach holds additional value by recognizing underlying systematic uncertainties that the Wilks approach does not. Thus, our method allows for more robust ML-based analyses critical to performing accurate but computationally feasible global fits. This allows, for the first time, a global fit to sterile neutrino data without assuming Wilks’ theorem. While we demonstrate the utility of such a technique studying sterile neutrino searches, it is applicable to both single-experiment and global fits of all kinds.

  PublisherDOI

• The MIST-1 and MIST-2 multicusp ion sources for high-current <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:msubsup> <mml:mrow> <mml:mi>H</mml:mi> </mml:mrow> <mml:mrow> <mml:mn>2</mml:mn> </mml:mrow> <mml:mrow> <mml:mo>+</mml:mo> </mml:mrow> </mml:msubsup> </mml:math> beams

  Journal of Physics G Nuclear and Particle Physics · 2025-10-15

  article

  Abstract We present two iterations of the Multicusp Ion Source Technology at MIT (MIST) sources, designed to fulfill the requirements of the HCHC-XX cyclotron design. The HCHC-XX is a novel compact cyclotron accelerating <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" overflow="scroll"> <mml:msubsup> <mml:mrow> <mml:mi mathvariant="normal">H</mml:mi> </mml:mrow> <mml:mrow> <mml:mn>2</mml:mn> </mml:mrow> <mml:mrow> <mml:mo>+</mml:mo> </mml:mrow> </mml:msubsup> </mml:math> . Beam is injected through a radio-frequency quadrupole buncher-accelerator (embedded in the cyclotron yoke) and utilizes so-called vortex motion during acceleration. If successful, it will deliver 10 mA of protons at 60 MeV in CW mode. This scheme requires a low-emittance, high-current initial beam with high <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" overflow="scroll"> <mml:msubsup> <mml:mrow> <mml:mi mathvariant="normal">H</mml:mi> </mml:mrow> <mml:mrow> <mml:mn>2</mml:mn> </mml:mrow> <mml:mrow> <mml:mo>+</mml:mo> </mml:mrow> </mml:msubsup> </mml:math> purity. We briefly summarize the design and previous results of the MIST-1 ion source and, for the first time, the detailed design of the new and improved MIST-2, including the mechanical, electrical, and control system design. We further show experimental results of using the MIST-2 backplate on the MIST-1 body, present a study using different types of permanent magnets for confinement (including no magnets), and finally, we present first results of the MIST-2 in full operation. In this first commissioning run, we were able to increase the total extracted current from the MIST-2 to 7 mA—a factor of 2 over the MIST-1.

  PublisherDOI

• Doctor for a day: the impact of a health professions enrichment program on self-efficacy

  Frontiers in Medicine · 2025-02-25 · 1 citations

  articleOpen access

  Introduction: Health profession enrichment programs for underrepresented minority students are crucial to supporting students' interest in healthcare careers and improving preparedness for important academic and professional milestones. Doctor for a Day, an enrichment program at the University of Washington School of Medicine, hosts monthly events where underrepresented kindergarten-12th grade students are exposed to careers in medicine by healthcare professionals from diverse backgrounds. Methods: This study investigates to what extent participation in Doctor for a Day programming improves self-efficacy using a survey study of 958 students who attended at least one Doctor for a Day event between 2017 and 2023. Results: Using an evaluation tool composed of six questions, our results demonstrate that participation in Doctor for a Day programming increases self-efficacy and interest in medicine as a career. Analysis of these results found significant differences in responses based on grade level, with students in high school demonstrating the largest improvement in self-efficacy. Discussion: These findings underscore the value of such enrichment programs and offer insights for the development of similar initiatives.

  PublisherOA PDFDOI

• The MIST-1 and MIST-2 multicusp ion sources for high-current H$_2^+$ beams

  ArXiv.org · 2025-07-03

  preprintOpen access

  We present two iterations of the Multicusp Ion Source Technology at MIT (MIST) sources, designed to fulfill the requirements of the HCHC-XX cyclotron design. The HCHC-XX is a novel compact cyclotron accelerating H$_2^+$. Beam is injected through a radio-frequency quadrupole buncher-accelerator (embedded in the cyclotron yoke) and utilizes so-called vortex motion during acceleration. If successful, it will deliver 10 mA of protons at 60 MeV in CW mode. This scheme requires a low-emittance, high-current initial beam with high H$_2^+$ purity. We briefly summarize the design and previous results of the MIST-1 ion source and, for the first time, the detailed design of the new and improved MIST-2, including the mechanical, electrical, and control system design. We further show experimental results of using the MIST-2 backplate on the MIST-1 body, present a study using different types of permanent magnets for confinement (including no magnets), and finally, we present first results of the MIST-2 in full operation. In this first commissioning run, we were able to increase the total extracted current from the MIST-2 to 7 mA - a factor of 2 over the MIST-1.

  PublisherOA PDF

• Machine Learning-Informed 3+1 Sterile Neutrino Global Fits using Posterior Density Estimation of Electron Disappearance Data

  ArXiv.org · 2025-12-05

  preprintOpen access1st authorCorresponding

  Global analyses of particle physics data are integral for validating and scrutinizing published results of experiments. Global fits of anomalous oscillation data which search for one or more eV-scale sterile neutrinos are particularly challenging both to evaluate and to reconcile in the global picture. Fits (especially joint ones) to oscillation data suffer from significant computational burdens, such as likelihood intractability, making traditional Markov Chain-Monte Carlo all but impossible. Given evidence both supporting and challenging beyond Standard Model physics across neutrino experiments of various baselines, energies, and detection techniques, the global search for sterile neutrinos requires additional tools in order to determine whether sterile neutrinos remain a viable solution to unexplained anomalies. Furthermore, both a Bayesian and frequentist interpretation of sterile neutrino data is needed for a complete assessment of longstanding tensions in the field. Techniques from the machine learning subfield of simulation-based inference have a natural application to such a problem. In this contribution, we illustrate some of the outstanding questions of the global picture of light sterile neutrinos by focusing on experiments searching with the disappearance of electron (anti)neutrinos, and look to posterior density estimation strategies to craft answers, including comparisons to a machine-learning-based frequentist approach.

  PublisherOA PDFDOI

• Promoting Self-Efficacy: Exploring Student Pharmacists’ Confidence in Practice-Based Skills Through an APPE Readiness Course

  American Journal of Pharmaceutical Education · 2025-11-01

  articleOpen access
  PublisherDOI

• Neural networks as effective surrogate models of radio-frequency quadrupole particle accelerator simulations

  Machine Learning Science and Technology · 2024-04-03 · 3 citations

  articleOpen access1st authorCorresponding

  Abstract Radio-frequency quadrupoles (RFQs) are multi-purpose linear particle accelerators that simultaneously bunch and accelerate charged particle beams. They are ubiquitous in accelerator physics, especially as injectors to higher-energy machines, owing to their impressive efficiency. The design and optimization of these devices can be lengthy due to the need to repeatedly perform high-fidelity simulations. Several recent papers have demonstrated that machine learning can be used to build surrogate models (fast-executing replacements of computationally costly beam simulations) for order-of-magnitude computing time speedups. However, while these pilot studies are encouraging, there is room to improve their predictive accuracy. Particularly, beam summary statistics such as emittances (an important figure of merit in particle accelerator physics) have historically been challenging to predict. For the first time, we present a surrogate model trained on 200 000 samples that yields <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" overflow="scroll"> <mml:mrow> <mml:mo>&lt;</mml:mo> </mml:mrow> </mml:math> 6% mean average percent error for the predictions of all relevant beam output parameters from defining RFQ design parameters, solving the problem of poor emittance predictions by identifying and including hidden variables which were not accounted for previously. These surrogate models were made possible by using the Julia language and GPU computing; we briefly discuss both. We demonstrate the utility of surrogate modeling by performing a multi-objective optimization using our best model as a callback in the objective function to select an optimal RFQ design. We consider trade-offs in RFQ performance for various choices of Pareto-optimal design variables—common issues for any multi-objective optimization scheme. Lastly, we make recommendations for input data preparation, selection, and neural network architectures that pave the way for future development of production-capable surrogate models for RFQs and other particle accelerators.

  PublisherOA PDFDOI

• Let Me Be Your Guide! Supporting Student Success in APPEs with a Faculty Guidance Team

  American Journal of Pharmaceutical Education · 2024-09-01

  articleOpen access
  PublisherDOI

• A human mesh-centered approach to action recognition in the operating room

  Artificial Intelligence Surgery · 2024-06-30 · 6 citations

  articleOpen access

  Aim: Video review programs in hospitals play a crucial role in optimizing operating room workflows. In scenarios where split-seconds can change the outcome of a surgery, the potential of such programs to improve safety and efficiency is profound. However, leveraging this potential requires a systematic and automated analysis of human actions. Existing methods predominantly employ manual methods, which are labor-intensive, inconsistent, and difficult to scale. Here, we present an AI-based approach to systematically analyze the behavior and actions of individuals from operating rooms (OR) videos. Methods: We designed a novel framework for human mesh recovery from long-duration surgical videos by integrating existing human detection, tracking, and mesh recovery models. We then trained an action recognition model to predict surgical actions from the predicted temporal mesh sequences. To train and evaluate our approach, we annotated an in-house dataset of 864 five-second clips from simulated surgical videos with their corresponding actions. Results: Our best model achieves an F1 score and the area under the precision-recall curve (AUPRC) of 0.81 and 0.85, respectively, demonstrating that human mesh sequences can be successfully used to recover surgical actions from operating room videos. Model ablation studies suggest that action recognition performance is enhanced by composing human mesh representations with lower arm, pelvic, and cranial joints. Conclusion: Our work presents promising opportunities for OR video review programs to study human behavior in a systematic, scalable manner.

  PublisherDOI

Frequent coauthors

• Kumhee Ro

  Centre for Nursing Innovation

  14 shared

• Daniel Winklehner

  Massachusetts Institute of Technology

  3 shared

• Mo‐Kyung Sin

  Seattle University

  3 shared

• G. A. Shumilina

  2 shared

• J. M. Conrad

  2 shared

• Jeffrey K. Jopling

  Johns Hopkins Medicine

  2 shared

• Antonio Montefusco

  Azienda Ospedaliera Citta' della Salute e della Scienza di Torino

  1 shared

• David A. Townes

  University of Washington

  1 shared

Labs

• Joshua Villarreal LabPI

Education

• Other

  University of Arizona

• Other

  University of Washington