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.

世界上充斥着数据，不仅数量巨大，而且复杂性也很高。 变量之间存在大量的相互依赖性，并且它们的关系会随着时间的推移以复杂的、非线性的方式发生变化。 这种复杂性在自然界中很常见，生物有机体中已经进化出智能系统来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具有类似适应性的智能系统。 研究自然界智能自适应系统的科学家以及在实验室构建智能自适应系统的工程师越来越需要处理大型复杂系统和数据集的概念和技术能力。 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共同基础；他们都致力于解决适应、学习、调节和预测等常见问题。授予加州大学默塞德分校的美国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。该项目预计培养 100 名博士生，其中包括 50 名受资助的实习生，他们的博士项目涉及应用数学、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。先前对控制论、联结主义和复杂自适应系统的研究重点关注跨学科和领域的智能自适应系统的一般原理。 NRT 项目将通过更深入地研究学习和适应原理，正如它们在更广泛的生物、人类和技术系统中所体现的那样，推动这一领域的下一波研究。培训计划包括强化计算大本营、智能自适应系统定制课程模块、实验室轮换、沟通技能发展研讨会和行业交流机会。总而言之，这些 NRT 活动将使学员获得处理大型复杂数据集的概念和技术能力。所有 NRT 学员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，并与当地社区合作进行教育、传播研究成果和发展外展合作伙伴关系。 NRT 项目将改变加州大学默塞德分校跨学科研究和教育的能力。在机构层面，NRT 项目将成为协作、跨学科研究生教育的典范。一项广泛的招聘计划将连接并加强加州大学其他校区和一些西班牙裔服务机构的资源和项目，以增加从事智能自适应系统工作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育创新文化和高等教育，对加州中央山谷产生直接和变革性的经济影响。 NSF 研究实习 (NRT) 计划旨在鼓励为 STEM 研究生教育培训开发和实施大胆的、具有潜在变革性的新模式。培训课程致力于通过创新、循证且符合不断变化的劳动力和研究需求的综合培训模式，对高度优先的跨学科研究领域的 STEM 研究生进行有效培训。

项目成果

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

多模式交互的爆发和平静：行为的时间分布揭示了言语和非言语交流之间的差异

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

Interactivity of language: Interactivity of Language

语言的交互性：语言的交互性

• DOI: 10.1111/lnc3.12282
• 发表时间: 2018-07
• 期刊: Language and Linguistics Compass
• 影响因子: 2.5
• 作者: Spevack, Samuel C.;Falandays, J. Benjamin;Batzloff, Brandon;Spivey, Michael J.
• 通讯作者: Spivey, Michael J.

Coordinated Search With Multiple Robots Arranged in Line Formations

• DOI: 10.1109/tro.2017.2776305
• 发表时间: 2018-04-01
• 期刊: IEEE Transactions on Robotics
• 影响因子: 7.8
• 作者: A. Kolling;A. Kleiner;Stefano Carpin
• 通讯作者: Stefano Carpin

Tracking Differential Activation of Primary and Supplementary Motor Cortex Across Timing Tasks: An fNIRS Validation Study

跟踪计时任务中初级和辅助运动皮层的差异激活：fNIRS 验证研究

• DOI: 10.1016/j.jneumeth.2020.108790
• 发表时间: 2020-01
• 期刊: Journal of neuroscience methods
• 影响因子: 3
• 作者: Rahimpour, A;Pollonini, L;Comstock, DC;Balasubramaniam, R.;Bortfeld, HB.
• 通讯作者: Bortfeld, HB.

Decision-Making in the Human-Machine Interface

人机界面中的决策

• DOI: 10.3389/fpsyg.2021.624111
• 发表时间: 2021
• 期刊: Frontiers in Psychology
• 影响因子: 3.8
• 作者: Falandays JB;Spevack S;Pärnamets P;Spivey M
• 通讯作者: Spivey M