REU Site: Biomedical Engineering in Simulations, Imaging, and Modeling (BME-SIM)

东卡罗莱纳大学的生物医学工程模拟、成像和建模（BME-SIM）本科生研究体验（REU）网站为30名代表性不足和服务不足的学生提供了真实的研究体验，其中包括女性、代表性不足的少数民族学生和家庭机构提供有限研究机会的学生。让这些学生接触创新研究将提高他们对研究生院的认识；从而提高申请率，并最终提高STEM研究生院的招生渠道。通过增加STEM专业人员的多样性来扩大参与，可以促进科学的进步。作为研究经验的补充，BME-SIM计划解决了工程的社会层面；从而促进更包容的环境和归属感的增加。这是通过项目创新的领导力发展项目和促进积极的师徒关系来实现的。REU计划的目标将通过多学科方法来实现，将模拟、成像和建模作为一个共同的主线。建模跨越尺度和融合不同学科的能力使这种方法处于理解生物机制、系统动力学和最终改善健康的前沿。为了支持这一领域的发展，学生必须擅长计算建模；这是本科课程中很少涉及的领域。因此，BME-SIM计划填补了这一劳动力缺口，同时提高了生理系统建模知识和培训下一代研究人员。BME-SIM REU的目标是为30名学生提供真实的研究经验，特别是代表性不足和服务不足的人群，提高对研究生院的认识和申请。在课程结束时，学生应该达到以下五个结果：1)提高对研究过程的理解；2)研究生院意识增强；3)强化STEM职业发展路径；4)作为工程师/科学家的更多认同；5)提高领导能力。该项目将通过与家庭机构导师合作以及在生物医学工程学会年会上重新联系，吸引参与者进行超过10周的夏季研究体验。为了确保申请者的多样性，学生招募将从合作机构开始，包括历史上的黑人机构、为大量美国原住民服务的机构，以及与社区大学在全州范围内的联系。BME-SIM的教育目标是通过多学科方法使用模拟，成像和建模作为一个共同的线程来完成的。开发模型有几个核心概念，例如边界或初始条件，以及验证。发展对这些统一主题的理解使学生能够更好地在更广泛的背景下欣赏建模。与其他方法相比，计算建模提供了几个优势：1)生成和测试新的理论；2)估计难以测量的变量；3)纳入受试者特定参数，通常来自非侵入性成像技术；4)告知限制动物和人类使用的实验设计。每个REU项目都推进了其各自模型或配套成像技术的开发、实现或准确性方面的知识。建模跨越尺度和融合不同学科的能力使这种方法处于理解生物机制、系统动力学和最终改善健康的前沿。该奖项反映了美国国家科学基金会的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。

The Biomedical Engineering in Simulations, Imaging, and Modeling (BME-SIM) Research Experiences for Undergraduates (REU) Site at East Carolina University provides authentic research experiences to 30 targeted underrepresented and underserved students -- including women, underrepresented minority students, and students whose home institution offers limited research opportunities. Exposing these students to innovative research will raise awareness of graduate school; thereby, increasing application rates and ultimately the STEM graduate school pipeline. Broadening participation by increasing the diversity of STEM professionals promotes the progress of science. Complementary to the research experience, the BME-SIM program addresses the social dimensions of engineering; thus, promoting a more inclusive environment and increased sense of belonging. This is accomplished through both the program's innovative leadership development program and promotion of positive mentoring relationships. The REU program goals will be accomplished through a multidisciplinary approach using simulations, imaging, and modeling as a common thread. The power of modeling to span scales and blend different disciplines places this approach at the forefront for understanding biological mechanisms, system dynamics, and ultimately improving health. To support growth in this field, students must be adept in computational modeling; an area which is rarely addressed in undergraduate curricula. Thus, the BME-SIM program fills this workforce gap while advancing knowledge of modeling physiological systems and training the next generation of researchers.The goal of the BME-SIM REU is to provide authentic research experiences to 30 students, especially underrepresented and underserved populations, increasing awareness of and application to graduate school. At the end of the program students should achieve the following five outcomes: 1) improved understanding of the research process; 2) increased awareness of graduate school; 3) reinforcement of STEM career path; 4) greater identification as engineer/scientist; and 5) increased leadership capacities. The project will engage participants for longer than the 10-week summer research experience by both collaborating with home institution mentors and reconnecting at the Biomedical Engineering Society annual meeting. To ensure a diverse applicant pool, student recruitment will begin at partner institutions which include historically black institutions, an institution serving a large Native American population, and statewide connections with community colleges. The BME-SIM educational goals are accomplished through a multidisciplinary approach using simulations, imaging, and modeling as a common thread. There are several central concepts to developing models, such as boundary or initial conditions, and validation. Developing an understanding of these unifying themes enables students to better appreciate modeling in a broader context. Computational modeling offers several advantages over other approaches: 1) generate and test new theories; 2) estimate difficult to measure variables; 3) incorporate subject-specific parameters, generally derived from non-invasive imaging techniques; and 4) inform experimental designs limiting the use of animals and humans. Each REU project advances knowledge on the development, implementation, or accuracy of its respective model or companion imaging technique. The power of modeling to span scales and blend different disciplines places this approach at the forefront for understanding biological mechanisms, system dynamics, and ultimately improving health.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.