IGERT: Vision and Learning in Humans and Machines

考虑创建(a)一个计算机系统，帮助医生利用病人的所有医疗数据和图像以及数百万的病例历史做出诊断；(b)能够感知居住者活动的智能建筑和汽车；(c)观察并了解你习惯的个人数字助理——不仅能从网上收集信息，还能记住你把钥匙放在哪里了；或者(d)在孩子做科学实验时看着她的电脑家教。这些场景中的每一个都需要能够看到和学习的机器，尽管计算机视觉和计算学习已经取得了巨大的进步，但目前用于许多应用（如面部识别）的计算机视觉和学习系统仍然不如幼儿的视觉和学习能力。与此同时，通过心理物理和神经生理实验，在理解人类的视觉识别和学习方面取得了长足的进步。这一提议的智力价值在于它专注于在四个领域之间创造新的互动：计算机和人类视觉，人类和机器学习。我们相信这些领域是紧密交织在一起的，他们同时研究的协同作用将导致所有四个领域的突破。因此，我们在这个IGERT中的目标是培养新一代的科学家和工程师，他们既精通计算机视觉和计算学习的数学和物理基础，也精通自然视觉和学习的生物学和心理学基础。一方面，学生将被训练提出生物视觉某些方面的计算模型，然后设计实验（功能磁共振成像，单细胞记录，心理物理学）来验证这个模型。另一方面，他们将准备扩展学习理论的前沿，并将所得技术嵌入到现实世界的机器视觉应用中。这个项目更广泛的影响将是培养一代学者，他们将带来新的工具来解决人类和计算机视觉以及人类和机器学习的基本问题。我们将开发新的课程，引入新的跨学科课程，以补充现有课程。此外，接受该计划的学生将在课程开始前进行为期两周的新兵训练营，在那里他们将接受机器学习和使用MatLab的视觉，感知心理物理学和脑成像方面的强化培训。我们将平衡校园培训和暑期实习。IGERT是一项nsf范围内的计划，旨在满足教育美国博士科学家和工程师的挑战，他们具有跨学科背景，所选学科的深厚知识，以及未来职业需求所需的技术，专业和个人技能。该计划旨在通过建立创新的研究生教育和培训模式，在一个超越传统学科界限的合作研究的肥沃环境中，促进研究生教育的文化变革。在该计划的第六年，奖项将颁发给那些由NSF支持的科学和工程领域的项目的机构

Consider creating (a) a computer system to help physicians make a diagnosis using all of a patient's medical data and images along with millions of case histories; (b) intelligent buildings and cars that are aware of their occupants activities; (c) personal digital assistants that watch and learn your habits -- not only gathering information from the web but recalling where you had left your keys; or (d) a computer tutor that watches a child as she performs a science experiment. Each of these scenarios requires machines that can see and learn, and while there have been tremendous advances in computer vision and computational learning, current computer vision and learning systems for many applications (such as face recognition) are still inferior to the visual and learning capabilities of a toddler. Meanwhile, great strides in understanding visual recognition and learning in humans have been made with psychophysical and neurophysiological experiments. The intellectual merit of this proposal is its focus on creating novel interactions between the four areas of: computer and human vision, and human and machine learning. We believe these areas are intimately intertwined, and that the synergy of their simultaneous study will lead to breakthroughs in all four domains.Our goal in this IGERT is therefore to train a new generation of scientists and engineers who are as versed in the mathematical and physical foundations of computer vision and computational learning as they are in the biological and psychological basis of natural vision and learning. On the one hand, students will be trained to propose a computational model for some aspect of biological vision and then design experiments (fMRI, single cell recordings, psychophysics) to validate this model. On the other hand, they will be ready to expand the frontiers of learning theory and embed the resulting techniques in real-world machine vision applications. The broader impact of this program will be the development of a generation of scholars who will bring new tools to bear upon fundamental problems in human and computer vision, and human and machine learning.We will develop a new curriculum that introduces new cross-disciplinary courses to complement the current offerings. In addition, students accepted to the program will go through a two-week boot camp, before classes start, where they will receive intensive training in machine learning and vision using MatLab, perceptual psychophysics, and brain imaging. We will balance on-campus training with summer internships in industry.IGERT is an NSF-wide program intended to meet the challenges of educating U.S. Ph.D. scientists and engineers with the interdisciplinary background, deep knowledge in a chosen discipline, and the technical, professional, and personal skills needed for the career demands of the future. The program is intended to catalyze a cultural change in graduate education by establishing innovative new models for graduate education and training in a fertile environment for collaborative research that transcends traditional disciplinary boundaries. In this sixth year of the program, awards are being made to institutions for programs that collectively span the areas of science and engineering supported by NSF