RTG: Vertically Integrated Interdisciplinary Training in Mathematics for Human Health

德克萨斯大学阿灵顿分校（UTA）的UTA- rtg：人类健康数学垂直整合跨学科培训项目将培训和指导三个多层次的跨学科研究人员（总共9名本科生，6名博士生和2名博士后研究人员）。研究培训小组（RTG）将利用UTA是一个拥有大量退伍军人的西班牙裔服务机构这一事实，为许多历史上在科学领域代表性不足的群体提供多样化的招聘资源。RTG团队将应用数学模型进行研究，以回答有关癌症生物学、计算神经学和媒介传播疾病等主题的问题。RTG学者将接受数学理论和计算方法的培训，用于慢性和感染性疾病的诊断、评估、预防和治疗。项目活动将由十名UTA数学教师领导，与UTA护理，生物工程，生物学和心理学以及德克萨斯大学西南医学中心，北德克萨斯大学健康科学中心和VA北德克萨斯医疗保健系统的研究人员合作。该项目经验丰富的导师借鉴了多层次指导的最佳实践模式，在这种模式下，各级教师和学员共同努力，分享各自的专业知识水平，培养出也准备好充当导师的研究人员，并在生物技术公司、卫生组织和学术界的团队中发挥作用。这个RTG项目整合了指导、跨学科研究和课程作业。该项目研究在实验工作的指导下，将通过开发最先进的随机建模和最优控制框架来推进数学，用于癌症生物标志物的动力学、神经生理学和共循环载体传播病毒之间的免疫相互作用；以及解决微分和积分方程的有效和精确的计算方法，以及将现代数据科学和机器学习方法与上述经典技术相结合，用于分析数据和解决所提出的数学问题。该计划的研究经验包括：每周一次的计划研讨会和小组圆桌会议，发展研究，教学和协作技能，同时也提供了在UTA正在进行的跨学科研究工作的机会；在校外RTG合作伙伴网站和UTA实验室进行实践研究和实习；写报告，在会议上展示研究成果。UTA- rtg项目自然建立在UTA几个联邦资助的指导和培训项目的基础上，并与加强跨学科研究、教育和社区参与健康和人类状况的机构目标保持一致。该奖项由劳动计划和数学科学部的数学生物学计划共同资助。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 UTA-RTG: Vertically Integrated Interdisciplinary Training in Mathematics for Human Health program at the University of Texas at Arlington (UTA) will train and mentor three multi-level cohorts of interdisciplinary researchers (a total of nine undergraduates, six Ph.D. students, and two postdoctoral researchers). The Research Training Group (RTG) will leverage on the fact that UTA is a Hispanic Serving Institution with a large population of veterans that offers a diverse recruitment pool with many groups historically underrepresented in the sciences. RTG teams will conduct research applying mathematical modeling to answer questions on topics in cancer biology, computational neurology, and vector-borne diseases. RTG scholars will be trained in mathematical theory and computational methods for diagnosis, assessment, prevention, and treatment of chronic and infectious diseases. Program activities will be led by a group of ten UTA Mathematics faculty, in collaboration with researchers from UTA Nursing, Bioengineering, Biology, and Psychology, as well as the University of Texas Southwestern Medical Center, the University of North Texas Health Science Center, and the VA North Texas Health Care System. The program’s experienced mentors draw on a best-practices model of multi-level mentoring in which faculty and trainees at all levels work together to share their own levels of expertise, producing researchers who are also ready to act as mentors, and in teams in biotechnology companies and health organizations as well as academia. This RTG program integrates mentoring, interdisciplinary research, and coursework. The program research, guided by experimental work, will advance mathematics by developing state-of-the-art stochastic modeling and optimal control frameworks for the dynamics of cancer biomarkers, neuronal physiology, and immunological interactions between co-circulating vector-borne viruses; and efficient and accurate computational methodologies for solving differential and integral equations, as well as integrating modern data science and machine learning methods with the aforementioned classical techniques for analyzing data and solving the proposed mathematical problems. The program’s research experiences include: a weekly program seminar and group roundtables that develop research, teaching, and collaborative skills, while also providing exposure to ongoing interdisciplinary research work at UTA; hands-on research experiences and internships at off-campus RTG partner sites and UTA labs; and writing reports and presenting research results at conferences. The UTA-RTG program naturally builds on several federally funded mentoring and training programs at UTA and aligns well with institutional goals of enhancing interdisciplinary research, education, and community engagement in Health and the Human Condition.This award is co-funded by the Workforce program and the Mathematical Biology program at the Division of Mathematical Sciences.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.