RTG: Vertically Integrated Interdisciplinary Training in Mathematics for Human Health

RTG：人类健康数学垂直整合跨学科培训

• 批准号: 2230790
• 负责人: Hristo Kojouharov
• 金额: $ 109.98万
• 依托单位: University of Texas at Arlington
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-09-01 至 2028-08-31
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2230790&HistoricalAwards=false
• 关键词: RTG; Vertically; Integrated; Interdisciplinary; Training

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.

德克萨斯大学阿灵顿分校（UTA）的人类健康计划中的UTA-RTG：垂直整合的跨学科培训将培训和精神三个多级跨学科研究人员（共有九名本科生，六名Ph.D.学生和两名博士学位研究人员）。研究培训小组（RTG）将利用以下事实：UTA是一家西班牙裔服务机构，拥有大量的退伍军人，他们提供了潜水员招聘的池，而科学中的许多群体历史上的人数不足。 RTG团队将进行研究，应用数学建模，以回答有关癌症生物学，计算神经病学和媒介传播疾病的主题的问题。 RTG学者将接受数学理论和计算方法的培训，用于诊断，评估，预防和治疗慢性和传染病。计划活动将由十个UTA数学学院组成的小组与UTA护理，生物工程，生物学和心理学以及德克萨斯州西南医学中心，北德克萨斯大学健康科学中心以及弗吉尼亚州北德克萨斯州医疗保健系统的研究人员合作。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型，在这些模型中，各级教师和学员共同努力，共享自己的专业知识水平，培养他们也准备充当导师的研究人员，以及在生物技术公司和卫生组织以及学术界的团队中。这个RTG计划集成心理，跨学科研究和课程。在实验工作的指导下，该计划的研究将通过开发最新的随机建模和最佳控制框架来推动数学，以针对癌症生物标志物，神经元生理学的动力学以及共同循环载体 - 载体 - 传播病毒之间的免疫学相互作用；以及解决差异和积分等价的高效，准确的计算方法，以及将现代数据科学和机器学习方法与PRIORE相关的经典技术整合在一起，以分析数据并解决所提出的数学问题。该计划的研究经验包括：每周的计划中心和小组圆桌会议，旨在开发研究，教学和协作技能，同时还提供了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；以及在会议上写报告和介绍研究结果。 UTA-RTG计划自然建立在UTA的几个由联邦资助的心理和培训计划上，并且与增强跨学科研究，教育和社区参与健康和人类状况的机构目标非常相吻合。该奖项由劳动力计划和数学上的数学奖励措施与数学上的奖励。基金会的智力优点和更广泛的影响审查标准。