NRT-DESE Intelligent Adaptive Systems: Training computational and data-analytic skills for academia and industry

NRT-DESE 智能自适应系统：为学术界和工业界培训计算和数据分析技能

• 批准号: 1633722
• 负责人: Ramesh Balasubramaniam
• 金额: $ 292.17万
• 依托单位: University of California - Merced
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2016
• 资助国家: 美国
• 起止时间: 2016-09-15 至 2022-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1633722&HistoricalAwards=false
• 关键词: NRT; DESE; Intelligent; Adaptive; Systems

The world is bursting with data, not just in sheer amounts of it, but also in terms of complexity. Interdependencies among variables abound, and their relationships can change over time in intricate, nonlinear ways. Such complexities are common in nature and intelligent systems have evolved in biological organisms to adapt to these interdependencies and nonlinearities. More recently, engineers have begun to build intelligent systems for applications in health, security, and industry that can similarly adapt. The scientists who study intelligent adaptive systems in nature, as well as the engineers who build them in the lab, are increasingly in need of conceptual and technical abilities to deal with large, complex systems and datasets. These abilities provide a common basis for exchanging hypotheses and theories among mathematicians, physicists, biologists, cognitive scientists, computer scientists and engineers; all of whom work on common problems of adaptation, learning, regulation, and prediction. This National Science Foundation Research Traineeship (NRT) award to the University of California, Merced, will help the next generation of PhD students make interdisciplinary breakthroughs in theories and applications of intelligent adaptive systems. The project anticipates training 100 PhD students, including 50 funded trainees, from doctoral programs in applied mathematics, cognitive and information sciences, electrical engineering and computer science, mechanical engineering, physics, and quantitative and systems biology. Prior research in cybernetics, connectionism, and complex adaptive systems focused on general principles of intelligent adaptive systems that cut across disciplines and domains. The NRT program will advance the next wave of research in this area, by delving more deeply into principles of learning and adaptation as they manifest across a wider range of biological, human, and technological systems. The training program includes an intensive computational basecamp, custom course modules on intelligent adaptive systems, lab rotations, communication skills development workshops, and industry networking opportunities. Taken together, these NRT activities will enable the trainees to achieve conceptual and technical capabilities for dealing with large, complex datasets. All NRT trainees will have the opportunity to learn about entrepreneurship, network with industry mentors, engage in professional development, and engage with the local community to educate, disseminate research, and develop outreach partnerships. The NRT program will transform the capacity for interdisciplinary research and education at UC Merced. At the institutional level, the NRT program will serve as a model for collaborative, interdisciplinary graduate education. An extensive recruitment plan will connect with and enhance resources and programs at other UC campuses and a number of Hispanic-Serving Institutions to increase the diversity of scientists and engineers working on intelligent adaptive systems. Finally, the NRT program will have a direct and transformative economic impact in California's Central Valley, by fostering a culture of innovation and higher education in under-privileged communities. The NSF Research Traineeship (NRT) Program is designed to encourage the development and implementation of bold, new potentially transformative models for STEM graduate education training. The Traineeship Track is dedicated to effective training of STEM graduate students in high priority interdisciplinary research areas, through the comprehensive traineeship model that is innovative, evidence-based, and aligned with changing workforce and research needs.

这个世界充满了数据，不仅仅是数量庞大，而且复杂性也很高。 变量之间的相互依赖性比比皆是，它们的关系可以随着时间的推移以复杂的非线性方式发生变化。 这种复杂性在自然界中很常见，生物有机体中的智能系统已经进化到适应这些相互依赖性和非线性。 最近，工程师们开始为健康、安全和工业领域的应用构建智能系统，这些系统也可以进行类似的适应。 研究自然界中智能自适应系统的科学家，以及在实验室中构建它们的工程师，越来越需要概念和技术能力来处理大型复杂系统和数据集。 这些能力为数学家、物理学家、生物学家、认知科学家、计算机科学家和工程师之间交换假设和理论提供了一个共同的基础;所有这些人都致力于适应、学习、调节和预测的共同问题。授予加州大学默塞德分校的国家科学基金会研究实习生（NRT）奖将帮助下一代博士生在智能自适应系统的理论和应用方面取得跨学科突破。该项目预计将培养100名博士生，其中包括50名受资助的实习生，他们来自应用数学、认知和信息科学、电气工程和计算机科学、机械工程、物理学以及定量和系统生物学等博士课程。先前在控制论、联结主义和复杂适应系统方面的研究集中在跨越学科和领域的智能适应系统的一般原理上。NRT计划将推进这一领域的下一波研究，更深入地研究学习和适应的原则，因为它们在更广泛的生物，人类和技术系统中表现出来。该培训计划包括密集的计算基地，智能自适应系统的定制课程模块，实验室轮换，沟通技能开发研讨会和行业网络机会。总之，这些NRT活动将使受训者能够获得处理大型复杂数据集的概念和技术能力。所有NRT学员都将有机会学习创业精神，与行业导师建立联系，参与专业发展，并与当地社区合作，教育，传播研究和发展外联伙伴关系。NRT计划将改变UC默塞德的跨学科研究和教育能力。在机构层面，NRT计划将作为合作，跨学科研究生教育的典范。一个广泛的招聘计划将连接并加强其他UC校园和一些西班牙裔服务机构的资源和计划，以增加从事智能自适应系统的科学家和工程师的多样性。最后，NRT计划将通过在贫困社区培养创新文化和高等教育，对加州的中央谷产生直接和变革性的经济影响。NSF研究培训（NRT）计划旨在鼓励为STEM研究生教育培训开发和实施大胆的，新的潜在变革模式。该培训轨道致力于在高优先级的跨学科研究领域的STEM研究生的有效培训，通过全面的培训模式，是创新的，以证据为基础，并与不断变化的劳动力和研究需求保持一致。

项目成果

The Bursts and Lulls of Multimodal Interaction: Temporal Distributions of Behavior Reveal Differences Between Verbal and Non-Verbal Communication

• DOI: 10.1111/cogs.12612
• 发表时间: 2018-05-01
• 期刊: COGNITIVE SCIENCE
• 影响因子: 2.5
• 作者: Abney, Drew H.;Dale, Rick;Kello, Christopher T.
• 通讯作者: Kello, Christopher T.

Tracking differential activation of primary and supplementary motor cortex across timing tasks: An fNIRS validation study

• DOI: 10.1016/j.jneumeth.2020.108790
• 发表时间: 2020-07-15
• 期刊: JOURNAL OF NEUROSCIENCE METHODS
• 影响因子: 3
• 作者: Rahimpour, Ali;Pollonini, Luca;Bortfeld, Heather
• 通讯作者: Bortfeld, Heather

Affordance Compatibility Effect for Word Learning in Virtual Reality

虚拟现实中单词学习的可供性兼容性效应

• DOI: 10.1111/cogs.12742
• 发表时间: 2019
• 期刊: Cognitive Science
• 影响因子: 2.5
• 作者: Gordon, Chelsea L.;Shea, Timothy M.;Noelle, David C.;Balasubramaniam, Ramesh
• 通讯作者: Balasubramaniam, Ramesh

Attention in Skilled Behavior: an Argument for Pluralism

对熟练行为的关注：多元化的论据

• DOI: 10.1007/s13164-021-00529-6
• 发表时间: 2021
• 期刊: Review of Philosophy and Psychology
• 影响因子: 2
• 作者: Dayer, Alex;Jennings, Carolyn Dicey
• 通讯作者: Jennings, Carolyn Dicey

Modeling nonlinear dynamics of fluency development in an embodied-design mathematics learning environment with Recurrence Quantification Analysis

利用递归量化分析对体现设计的数学学习环境中流利度发展的非线性动力学进行建模

• DOI: 10.1016/j.ijcci.2021.100297
• 发表时间: 2021
• 期刊: International Journal of Child-Computer Interaction
• 作者: Tancredi, Sofia;Abdu, Rotem;Abrahamson, Dor;Balasubramaniam, Ramesh
• 通讯作者: Balasubramaniam, Ramesh