RTG: Vertically Integrated Interdisciplinary Training in Mathematics for Human Health

UTA-RTG：德克萨斯大学阿灵顿分校（UTA）的人类健康数学垂直综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群体（共9名本科生，6名博士生，10名博士生）。学生和两名博士后研究员）。研究培训小组（RTG）将利用UTA是一个西班牙裔服务机构，拥有大量退伍军人，提供多样化的招聘人才库，其中许多群体在科学领域的历史代表性不足。RTG团队将利用数学建模进行研究，以回答有关癌症生物学，计算神经学和媒介传播疾病的问题。RTG学者将接受数学理论和计算方法的培训，用于诊断，评估，预防和治疗慢性病和传染病。计划活动将由一组10 UTA数学教师领导，与来自UTA护理，生物工程，生物学和心理学的研究人员合作，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弗吉尼亚州北德克萨斯州医疗保健系统。该计划的经验丰富的导师借鉴了多层次指导的最佳实践模式，各级教师和学员共同努力，分享自己的专业知识水平，培养研究人员谁也准备好担任导师，并在生物技术公司和健康组织以及学术界的团队。 这个RTG计划整合了指导，跨学科研究和课程。该计划的研究，实验工作的指导下，将通过发展国家的最先进的随机建模和最佳控制框架的癌症生物标志物，神经元生理学的动态数学，以及免疫相互作用共循环载体传播的病毒;以及求解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用于分析数据和解决所提出的数学问题的经典技术相结合。该计划的研究经验包括：每周计划研讨会和小组圆桌会议，开发研究，教学和协作技能，同时还提供接触UTA正在进行的跨学科研究工作;在校外RTG合作伙伴网站和UTA实验室的实践研究经验和实习;撰写报告并在会议上展示研究成果。UTA-RTG计划自然建立在UTA的几个联邦资助的指导和培训计划的基础上，并与加强跨学科研究，教育，和社区参与健康和人类状况。这个奖项是共同的，该奖项由数学科学部的劳动力计划和数学生物学计划资助。该奖项反映了NSF的法定使命，并被认为值得支持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。

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.