NRT-HDR: FUTURE Foundations, Translation, and Responsibility for Data Science Impact

NRT-HDR：数据科学影响的未来基础、翻译和责任

• 批准号: 1922658
• 负责人: Brian McFee
• 金额: $ 300万
• 依托单位: New York University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2019
• 资助国家: 美国
• 起止时间: 2019-09-01 至 2025-08-31
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1922658&HistoricalAwards=false
• 关键词: NRT; HDR; FUTURE; Foundations; Translation

Rapid advances in computational speed, data availability, and the development of novel data analysis methods have birthed a new field: data science. This new field requires rigorously trained, cross-disciplinary, and ethically responsible data scientists. Producing researchers who are "data science natives" requires the development of an inclusive, interdisciplinary ecosystem supported by coursework and professional development activities to foster education and training. This National Science Foundation Research Traineeship (NRT) award to the Center for Data Science at New York University will build such an environment entitled FUTURE. This project anticipates training fifty (50) PhD students, including twenty (20) funded trainees, from data science and other disciplines such as mathematics, computer science, physics, neuroscience, health sciences, and psychology. An additional 145 MS or PhD students will engage in select courses and professional development opportunities offered through the project.The traineeship will fill a significant gap by rigorously training data scientists who (1) develop methodology and harness statistical tools to find answers to questions that transcend the boundaries of traditional academic disciplines; (2) effectively communicate to extract crisp questions from big, heterogeneous, uncertain data; (3) translate fundamental research insights into data science practice in the sciences, medicine, industry, and government; and (4) are aware of the ethical implications of their work. These objectives will be achieved by a combination of an innovative core curriculum, a novel data assistantship mechanism that provides training of skills transfer through rotations and internships, and communication and entrepreneurship modules. In addition, trainees will address fundamental research questions in mathematical and statistical aspects of data science, machine learning, domain applications, and ethical data use. The program will create a sustainable model of societal impact and will produce teaching materials and methodologies for incorporating translation and responsibility into data science curricula. FUTURE will build on the Center for Data Science track record of attracting and retaining a diverse cohort of graduate students and will create an interdisciplinary graduate traineeship that includes diversity of thought by design, by supporting a multitude of methodologies, application domains, and avenues for impact.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 program is dedicated to effective training of STEM graduate students in high priority interdisciplinary or convergent research areas through comprehensive traineeship models that are innovative, evidence-based, and aligned with changing workforce and research needs.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.

计算速度、数据可用性和新型数据分析方法的快速发展催生了一个新的领域：数据科学。这个新领域需要经过严格培训，跨学科和道德责任的数据科学家。培养“数据科学原生”的研究人员需要开发一个包容性的跨学科生态系统，并由课程和专业发展活动支持，以促进教育和培训。这个国家科学基金会研究培训（NRT）奖给纽约大学数据科学中心将建立这样一个名为未来的环境。该项目预计将培训50名博士生，其中包括20名受资助的学员，来自数据科学和其他学科，如数学，计算机科学，物理学，神经科学，健康科学和心理学。另外145名硕士或博士生将参加该项目提供的精选课程和专业发展机会。该实习生将通过严格培训数据科学家来填补一个重大空白，这些科学家（1）开发方法并利用统计工具来寻找超越传统学科界限的问题的答案;（2）有效沟通，从大的、异构的、不确定的数据中提取清晰的问题;（3）将基础研究见解转化为科学、医学、工业和政府的数据科学实践;及（4）意识到其工作的道德影响。这些目标将通过创新的核心课程、通过轮换和实习提供技能转让培训的新的数据援助机制以及交流和创业模块相结合的方式来实现。此外，学员将解决数据科学，机器学习，领域应用和道德数据使用的数学和统计方面的基础研究问题。该计划将创建一个可持续的社会影响模型，并将制作教材和方法，将翻译和责任纳入数据科学课程。FUTURE将建立在数据科学中心吸引和留住多样化研究生群体的记录基础上，并将通过支持多种方法，应用领域和影响途径，创建一个跨学科的研究生培训，包括设计思想的多样性。NSF研究培训（NRT）计划旨在鼓励开发和实施大胆，为STEM研究生教育培训提供新的潜在变革模式。该计划致力于通过创新的、基于证据的、与不断变化的劳动力和研究需求相一致的综合培训模式，在高优先级的跨学科或融合研究领域对STEM研究生进行有效培训。该奖项反映了NSF的法定使命，并通过使用基金会的智力价值和更广泛的影响审查标准进行评估，被认为值得支持。

项目成果

Multisample Flow Matching: Straightening Flows with Minibatch Couplings

• DOI: 10.48550/arxiv.2304.14772
• 发表时间: 2023-04
• 期刊: Cement & Concrete Composites
• 影响因子: 10.5
• 作者: Aram-Alexandre Pooladian;Heli Ben-Hamu;Carles Domingo-Enrich;Brandon Amos;Y. Lipman;Ricky T. Q. Chen

Counterfactuals for the Future

未来的反事实

• 发表时间: 2023
• 期刊: Proceedings of the AAAI Conference on Artificial Intelligence
• 作者: Bynum, Lucius;Loftus, Joshua;Stoyanovich, Julia
• 通讯作者: Stoyanovich, Julia

A probabilistic framework for task-aligned intra- and inter-area neural manifold estimation

• 发表时间: 2022-09
• 作者: Edoardo Balzani;Jean-Paul Noel;Pedro Herrero-Vidal;D. Angelaki;Cristina Savin

Fairness in Ranking: From Values to Technical Choices and Back

排名的公平性：从价值观到技术选择并返回

• DOI: 10.1145/3555041.3589405
• 发表时间: 2023
• 期刊: SIGMOD '23: Companion of the 2023 International Conference on Management of Data
• 作者: Stoyanovich, Julia;Zehlike, Meike;Yang, Ke
• 通讯作者: Yang, Ke

Weakly-supervised High-resolution Segmentation of Mammography Images for Breast Cancer Diagnosis

用于乳腺癌诊断的乳腺 X 线摄影图像的弱监督高分辨率分割

• 发表时间: 2021
• 期刊: Medical Imaging with Deep Learning
• 作者: Liu, K.;Shen, Y.;Wu, N.;Chledowski, J.;Fernandez-Granda, C.;Geras, K.
• 通讯作者: Geras, K.