Gert Cauwenberghs - US grants

This award supports an integrated multidisciplinary program of education and research in computer engineering that addresses the real-time computational requirements of multimedia systems. The approach is based on parallel, fine grain hybrid analog-digital computing, using adaptation inspired by neuromorphic engineering to compensate for inaccuracies in the analog implementation. Research in this program is coordinated with classes, projects, and workshops in the curriculum of the university. At the graduate level, projects will include focal-plane arrays, speech classifiers, and A/D converters. At the advanced undergraduate level, classes include experimental projects on a smaller scale.

Phase distortions due to inhomogeneities in the optical path severely limit the perforinancc of a large class of optical systems for ground-to-ground and space communications, imaging through the atmosphere, medical laser beam focusing, among others. Demands on increased spatial resolutions and larger bandwidths call for an integrated approach to adaptive optics that modulates the wavefront in parallel at microscopic scale.

This collaborative effort combines expertise in adaptive optics, analog parallel very-large scale integrated (VLSI) niicrosys-tems, microfabrication and liquid-crystal molecular systems to create a new generation of adaptive micro-optical systems for high-resolution wavefront correction, with over 10,000 fully autonomous control elements integrated on a single, hybrid opti-cal/electronic chip. Autonomy is essential for high-bandwidth operation, and is obtained by integrating all adaptive functions directly on-chip.

At the architectural level, model-free adaptive control is implemented using parallel perturbation stochastic gradient descent optimization of an arbitrary, externally provided metric of system performance. At the physical level, high-speed wavefront control at micro-scale resolution is obtained by integrating a new type of fast nematic liquid-crystal (LC), operating at kilohertz- range bandwidths, onto the adaptive control chip. Silicon-on-sapphire (SoS) technology with ultra-thin silicon (UTSi) transis-tors provides a high-quality, low-noise, transparent active medium for high-density optical and electronic integration. We will investigate microscale structures of LC material sandwiched in between two transparent SoS wafers, implementing arrays of phase modulators with active electrodes implementing the adaptive algorithms in parallel. directly interfacing with the wave- front. The architectural and technological innovations combine to yield a projected system performance in excess of 108 control updates/sec. at least a factor 1,000 better than presently existing adaptive optics systems in speed, density and cost.

This program integrates research and education in a sequence of project-intensive courses, where teams of graduate and undergraduate students learn to design. prototype and test adaptive optics co-processors, implemented in analog VLSI and fabricated through MOSIS. The adaptive co-processors will be configured to externally control a variety of fast LC and other spatial light phase modulators, available for experimentation at the Army Research Laboratory (ARL). In addition, we will make use of full-size UTSi SoS wafers provided by Peregrine Semiconductor, custom-fabricated in a special arrangement with Hopkins, to prototype a fully integrated version of consistent optical quality. The already polished SoS wafers will be post-processed at the JHU Microfabrication Laboratory and at Boulder Nonlinear Systems. Inc.. to pattern and deposit fast nematic LC in contact with SoS for fast spatial light phase modulation. The prototyped adaptive micro-optical systems will be experimentally demonstrated on various adaptive optics and imaging tasks including laser beam focusing and stabilization for optical communications.

This project leverages advances in statistical learning theory, machine vision, and massively parallel very-large-scale-integration technology to develop a custom-trainable, versatile, self-contained, and mobile system for visually impaired users. The system will aid the user in interacting freely with other people and the environment, by rapidly detecting and localizing key visual environmental cues and rapidly recognizing and identifying familiar people and objects. At the core of the system is the "Kerneltron", a massively parallel Support Vector "Machine" (SVM) in silicon. The SVM hardware will be trained on-line by the end user to accommodate a variety of visual detection and recognition tasks in everyday situations through presentation of examples. The recognition core will be embedded in a portable prototype visual aid, interfacing with a CCD camera front-end, and an audio synthesizer back-end. Menu-driven keypad control will allow direct input and feedback from the user in training and directing the system. The user interface will be based on "OpenEyes", a wearable computer vision system for the blind. Proof of concept demonstration of the hardware system and evaluation of the training and test performance will be conducted with feedback from volunteer impaired users.

This project investigates signal processing and machine learning algorithms for identifying and localizing one or multiple targets in the acoustic scene. The algorithms will be mapped onto a parallel architecture suitable for integration with micro-power mixed-signal hardware. A biologically inspired gradient flow signal representation blindly separates and localizes targets using a miniature array of sub-wavelength aperture. A support vector machine identifies the time-frequency signatures of the localized targets. The goal of the one-year project is to determine the achievable energy efficiency and integration density of the autonomous sensor and the feasibility of its deployment in a large-scale network, and to evaluate the concept using hardware prototypes. Advanced power management using wake-up detection will be pursued to reduce standby power. The effort will also investigate efficient means of embedding acoustic sensors onto CMOS circuits towards a highly integrated directional and intelligent acoustic sensor. Miniature integration and micro-power operation are essential to provide an autonomous sensing and processing node for distributed intelligence in a sensor network. The outcomes of this project will advance the state of the art in acoustic sensing technology for surveillance, homeland security, and as an aid to the soldier's awareness in the digital battlefield. The results will also impact new developments in intelligent hearing aids and other assistive listening technologies and human-computer interfaces.

This Sensor proposal focuses on the development of sensory information
processing front-ends for a minimally-attended visual surveillance
network. We intend to use mixed-signal hardware computation methods in a
system-on-a-chip architecture. Image processing algorithms are
implemented at the focal-plane, using compact circuits that operate at
microwatt power levels to minimize battery weight and form factor.
Furthermore, the sensory front-end must demonstrate operational autonomy
(i.e. in decision making) and robustness (i.e. to changes in
environmental conditions).
Intellectual Merit: The proposed system will replace traditional
computer vision systems (cameras, digitizers and processors) with a
computational sensor. Using wide-dynamic range imaging, with local gain
control, circumvents the limitations of standard cameras. Spatiotemporal
feature extraction is used to highlight moving targets. The shape formed
by the features is identified using kernel learning in silicon; alarms
are generated based on the similarity of the targets to preprogrammed
shapes. Without these types of smart, ultra-low power, compact imaging
and computational microsystems, practical sensor networks for visual
surveillance may not be realizable.
Broader Impact: The sensors are primarily intended for surveillance of
large, remotely located areas, where limited manpower is available, e.g.
border patrolling. Their low power and small size obviates a variety of
mobile applications.
The multi-disciplinary nature of this work will result in the
development a pipeline of students, educators and researchers with the
broad skills required to succeed in modern high technology industry and
academics. Their education will be rounded with exposure to issues in
homeland security, privacy rights and international law.

[unreadable] DESCRIPTION (provided by applicant): As the supplier of nutrients to the brain, the cerebral vascular network is as crucial to normal brain function as the neurons themselves. This collaborative project aims to bridge computational and experimental approaches across neuroscience, biomedical imaging, and electrical and computer engineering to advance the fundamental understanding of coupling between neural and vascular activity at extensive spatial and temporal scales. The experimental study will focus on the rat somatosensory system with exposed cortical surface for optical access. We will develop laser speckle contrast and two-photon fluorescence imaging techniques for functional imaging of blood flow on the cortical surface at microvessel and millisecond scale resolution. The custom designed image sensor will be integrated in a micro-optic enclosure that facilitates in vivo studies in awake, behaving rodents. Experiments that utilize whisker stimulation and arterial blocking agents will supply valuable data to construct and validate spatiotemporal models of neurovascular coupling and hemodynamics in cortical arterial networks. [unreadable] [unreadable] We anticipate that this research will contribute to both fundamental advances in computational neuroscience studies of neurovascular dynamics in healthy brain, and biomedical aspects of diseased brain and its means of recovery. The research is significant in that it will explain at a quantitative level the mechanisms of blood flow and dilation in microvessels in relation to functional brain activity. This will lead to better quantitative understanding of the effect of 'blood steal' in functional magnetic resonance imaging (fMRI) of closely spaced regions of neural activity. Effects of stroke will be emulated by arterial occlusion to study reperfusion of affected brain areas. The significance of this study is that it allows to quantify the extent of spontaneous and drug-induced brain recovery after stroke, by observing changes in brain vascular activity in response to functional stimulation. The project provides unique opportunities for training of engineering and neuroscience students participating in an interdisciplinary and inter-institutional collaborative research program. [unreadable] [unreadable] [unreadable]

Proposal #0847752
PI- Gert Cauwenberghs

ABSTRACT

This exploratory research project aims to observe and augment the learning experiences of children through non-intrusive acquisition, on-line analysis and interpretation of their brain dynamics. Current systems for recording high-resolution encephalogram (EEG) dynamical brain activity are not suitable for this purpose because they distract the children and constrain their mobility by excessive wiring between electrodes and computer. Existing methods are also not useful because of unreliable contact between electrodes and scalp during body motion. This two-year project specifically entails the design and implementation of a low-weight wearable, wireless EEG recording system with 128 embedded non-contact electrodes. This will include supporting software for real-time analysis and display of brain dynamics on a host computer. The research will give rise to new methods for non-intrusive acquisition and on-line interpretation of brain dynamics, and open up new research directions not possible using existing methods. The project supports inter-disciplinary graduate research combining biophysics of EEG, engineering of non-contact and wireless sensors, independent component analysis, cognitive neuroscience, and the temporal dynamics of learning.

Outcomes of this research will contribute to the broader understanding of brain function at a level combining cognitive neuroscience and social dynamics. A diverse and interdisciplinary body of students at the NSF Temporal Dynamics of Learning Center (TDLC) and the Institute of Neural Computation at UCSD will take part in applying the instrumentation to learning research in the biological, cognitive and social sciences. The wireless EEG infra-structure also significantly enhances the mobility of EEG recording in existing motion-capture facilities at TDLC, allowing the study of learning in dynamic environments with freely interacting subjects. The project will further benefit from outreach channels supported by the TDLC, including the Howard Hughes Medical Institute (HHMI) Hughes Scholar Program (HSP), and the Preuss High School at UCSD.

Intellectual Merit: This project aims at combining cognitive and computational neuroscience, neuroengineering and system identification towards a transformative understanding of the way distributed brain dynamics interact with motor activity in humans. 3-D body and limbs movement kinematics, eye movements and electroencephalographic (EEG) spatiotemporal brain data will be recorded simultaneously during motor control and adaptation in healthy and Parkinson?s disease patients. In particular, altered and real world motor tasks will be simulated in 3-D immersive virtual reality technology with force feedback robots providing proprioceptive interaction and feedback. Cognitive, behavioral and kinematics data will constrain the design of large-scale computational models of motor control and adaptation based on known anatomy and physiology of the basal ganglia. Neuromorphic engineering will guide the design of mobile embedded computational systems for real-time emulation of the brain-body models and closed-loop sensory-motor control for Parkinson?s patients. We expect that the development of new machines for neuro-rehabilitation will result in a threefold synergetic interaction between engineering and neuroscience: human-machine interactions will transform the notion of movement control and provide new contexts to study embodied cognition that will benefit neuroscience; in turn, new knowledge in neuroscience and motor control will accelerate the development of adaptive machines for rehabilitation and/or enhancement. Finally, comprehensive and predictive mathematical models of motor control implemented in neuromorphic hardware are expected to lead to new intelligent neuroprosthetic tools.

Broader Impact: Outcomes of this research will contribute to the system-level understanding of humanmachine interactions and motor learning and control in real world environments for humans, and will lead to the development of a new generation of wireless brain and body activity sensors and adaptive prosthetics devices. This will advance our knowledge of human-machine interactions, stimulate the engineering of new brain/body sensors and actuators, and have a direct influence in diverse areas where humans are coupled with machines, such as brain-machine interfaces, prosthetics and telemanipulation. We anticipate that the confluence of cognitive and computational neuroscience, control theory and wearable, unobtrusive bioinstrumentation will provide novel non-invasive approaches or the treatment and neuro-rehabilitation of Parkinson?s disease and will potentially transform our understanding of brain/body interactions. The project draws graduate and undergraduate students across divisions and in the NSF Temporal Dynamics of Learning Center (TDLC) and Institute of Neural Computation (INC) at UCSD participating in interdisciplinary engineering and neuroscience aspects of the design, optimization, and training of largescale neuromorphic systems and their human interfaces. Through outreach channels on campus supported by the TDLC and the NSF Research Experience for Undergraduates (REU), the program will actively pursue increased participation in research and education of the next generation of scientists and engineers.

The human vision system understands and interprets complex scenes for a wide range of visual tasks in real-time while consuming less than 20 Watts of power. This Expeditions-in-Computing project explores holistic design of machine vision systems that have the potential to approach and eventually exceed the capabilities of human vision systems. This will enable the next generation of machine vision systems to not only record images but also understand visual content. Such smart machine vision systems will have a multi-faceted impact on society, including visual aids for visually impaired persons, driver assistance for reducing automotive accidents, and augmented reality for enhanced shopping, travel, and safety. The transformative nature of the research will inspire and train a new generation of students in inter-disciplinary work that spans neuroscience, computing and engineering discipline.

While several machine vision systems today can each successfully perform one or a few human tasks ? such as detecting human faces in point-and-shoot cameras ? they are still limited in their ability to perform a wide range of visual tasks, to operate in complex, cluttered environments, and to provide reasoning for their decisions. In contrast, the mammalian visual cortex excels in a broad variety of goal-oriented cognitive tasks, and is at least three orders of magnitude more energy efficient than customized state-of-the-art machine vision systems. The proposed research envisions a holistic design of a machine vision system that will approach the cognitive abilities of the human cortex, by developing a comprehensive solution consisting of vision algorithms, hardware design, human-machine interfaces, and information storage. The project aims to understand the fundamental mechanisms used in the visual cortex to enable the design of new vision algorithms and hardware fabrics that can improve power, speed, flexibility, and recognition accuracies relative to existing machine vision systems. Towards this goal, the project proposes an ambitious inter-disciplinary research agenda that will (i) understand goal-directed visual attention mechanisms in the brain to design task-driven vision algorithms; (ii) develop vision theory and algorithms that scale in performance with increasing complexity of a scene; (iii) integrate complementary approaches in biological and machine vision techniques; (iv) develop a new-genre of computing architectures inspired by advances in both the understanding of the visual cortex and the emergence of electronic devices; and (v) design human-computer interfaces that will effectively assist end-users while preserving privacy and maximizing utility. These advances will allow us to replace current-day cameras with cognitive visual systems that more intelligently analyze and understand complex scenes, and dynamically interact with users.

Machine vision systems that understand and interact with their environment in ways similar to humans will enable new transformative applications. The project will develop experimental platforms to: (1) assist visually impaired people; (2) enhance driver attention; and (3) augment reality to provide enhanced experience for retail shopping or a vacation visit, and enhanced safety for critical public infrastructure. This project will result in education and research artifacts that will be disseminated widely through a web portal and via online lecture delivery. The resulting artifacts and prototypes will enhance successful ongoing outreach programs to under-represented minorities and the general public, such as museum exhibits, science fairs, and a summer camp aimed at K-12 students. It will also spur similar new outreach efforts at other partner locations. The project will help identify and develop course material and projects directed at instilling interest in computing fields for students in four-year colleges. Partnerships with two Hispanic serving institutes, industry, national labs and international projects are also planned.

The increasing prevalence of learning disorders, attention deficits, and lackluster appetite for reading across all walks of life, and particularly among school-age children, poses severe problems to humanity and, in the long run, burdens social and economic development. This Partnership for Innovation Building Innovation Capacity (PFI:BIC) collaborative project tackles the impending threats to humanity of illiteracy and faltering education heads-on by creating a new smart-service human-computer interface (HCI) neurotechnology platform as a highly effective, user-friendly, and fun-to-use tool aiding learning and stimulating cognitive development at home and in the classroom. The immersive HCI neurotechnology will allow directly measuring progress at the cognitive level and providing real-time feedback to guide the user in learning to read more effectively. The project is highly Science, Technology, Engineering and Mathematics (STEM) intensive both in its activities and in the targeted benefits of the developed technology, which extends directly to learning science and mathematics by probing cognitive performance of children while they solve puzzles. The development of unobtrusive neurotechnology further addresses a critical need for practical integrated and modular brain-computer interface (BCI) solutions in HCI promoting widespread consumer and clinical use in the marketplace. The partnership provides opportunities for students to gain practical experience in innovation in the marketplace through internships with the industrial partners.

The central aim is to develop and leverage new HCI technology as a learning coach and personal cognitive development assistant that facilitates learning to read and acquiring other critical skills in cognitive development. The immersive yet unobtrusive HCI technology testbed will comprise a dry-electrode electroencephalography (EEG) BCI, a tablet with touchscreen and integrated camera, and a suite of signal processing algorithms running in the cloud, for monitoring brain and gaze activity in children learning to read, and providing real-time neurofeedback on progress in cognitive performance to promote enhanced learning. The partnership will transition scientific advances of a previous NSF-sponsored UCSD project (NSF EFRI-M3C, ENG-1137279) in studying the distributed dynamics of human motor control, to development of neurofeedback training paradigms for learning enhancement, and to practical deployment on the unobtrusive immersive testbed implemented using Cognionics dry-electrode EEG wireless BCI neurotechnology and Syntrogi real-time cloud-based signal processing software pipelines. The potential for human empowerment by the technology will be demonstrated by evaluating effectiveness in enhancing learning capabilities and cognitive performance in simulated classroom settings and other targeted learning environments.

The lead institution for the project is University of California San Diego (UCSD), with investigators from the Institute for Neural Computation and Department of Bioengineering. The industrial partners in the effort are Syntrogi Inc. (dba Qusp, small business, San Diego CA) and Cognionics, Inc. (small business, San Diego, CA). The project also engages broader context partners Drs. Andrea Chiba and Leanne Chukoskie from the UCSD Temporal Dynamics of Learning Center, Dr. Barbara Moss from San Diego State University Department of Psychology, and Dr. Zewelanji N. Serpell from Virginia Commonwealth University Department of Psychology, in the human case studies and the assessment of the developed HCI technology in diverse learning environments.

Cells from living animals are very small, and yet measuring them and manipulating them are critical for producing new medical therapies. There are encouraging new methods to understand and control cells one at a time, but there really isn't now the technology to measure and control individually the thousands or millions of cells needed for a therapy. New approaches to individually measure and control cells using multiple nanoscopic devices are sorely needed. This award will study a method to control and measure many cells simultaneously. The base technology is a high-density platform of nanoscopic wires that interact with the cells in a culture system. The scalable nanomanufacturing of nanowire devices will make it possible to build "nanolab-on-a-chip" machines. Such tiny "laboratories", combined with a patient's own growing cells could create low-cost, predictive drug-screening platforms to accelerate drug discovery and personalized treatments. The project provides training opportunities for undergraduate, high school, and under-represented minority students in interdisciplinary research in materials science, engineering, and medicine. It augments and improves the course curriculum, and fosters a robust translational exchange with industry partners.

The project aims to overcome the barriers in developing a nanowire array-based system that enables multi-use, non-destructive, high-sensitivity measurements in 3D networks that are not possible with patch-clamp, automated patch, or microelectrode array techniques. Human-derived neurons and cardiomyocytes, which are highly relevant human models for drug screening, are studied. The project explores nanoimprint lithography as a scalable nanomanufacturing method to develop a wafer-scale nanowire neurophysiology platform scalable to 8000 simultaneous data points for 250 wells with 32 nanowire electrodes each. This scalable fabrication method enables the integration of nanowires in high densities and large numbers in integrated systems that comprise on-chip acquisition and digitization electronics and microfluidic drug intervention channels and wells. Furthermore, new architectures of multiple height nanowires are devised for screening the effects of drugs from 3D neuronal and cardiomyocyte networks and fully integrate readout electronics with the nanowire sensors. Finally, all components on a single, low cost platform scalable to 1820 wells and 115,840 simultaneous measurement points are monolithically integrated and the platform validated with a panel of drugs at the Sanford Burnham Prebys Medical Discovery Institute and UC San Diego. These technical innovations should enable non-destructive intracellular potential measurements across the depth of a tissue.

Neuromorphic cognitive computing aims at learning to solve complex cognitive tasks by emulating the principles and physical organization of highly efficient and resilient adaptive information processing in the biological brain. Despite over 30 years of development and a recent surge of broad interest across all Science, Technology, Engineering and Mathematics (STEM) disciplines, access to neuromorphic cognitive computing remains mostly limited to a small community of highly trained researchers in the field due to high entry barriers and costs associated with the specialized nature and complex operation of currently available systems. This project will construct and support a general-purpose neuromorphic cognitive computing platform that will be the largest and most versatile realized to date as well as the first to be broadly available and open to the research community at large, for research into new forms of brain-inspired computing that are more effective and more efficient in approaching the cognitive capabilities of the human mind. Targeting wide adoption by a diverse cross-section of users in the broader STEM research community, the platform will feature a natural user interface that shields novice users from the challenges arising in operating and configuring highly specialized neuromorphic hardware, by providing a set of user-friendly software tools maintained by and shared with the user community. Building on extensive existing network and storage infrastructure for user access and data sharing at the San Diego Supercomputer Center, the platform will be hosted and maintained through the Neuroscience Gateway (NSG) Portal, which currently serves over 600 active users in the scientific community.

The large-scale neuromorphic platform will serve as a new and unparalleled resource to the Computer and Information Science and Engineering (CISE) research community, addressing a great need for an experimental testbed for research in alternative forms of computing beyond the traditional von Neumann paradigm and the impending physical limits to Moore's Law expansion in the scaling of computing technology. The reconfigurable platform will feature a hierarchically interconnected network of in-memory computing processing nodes that emulates, in real-time, highly flexible neural dynamics (integrate-and-fire, graded, stochastic binary, etc) of up to 128 million neurons with high flexible connectivity and plasticity (spike-timing dependent plasticity, gradient-based deep learning, etc) of up to 32 billion synapses. The system will be capable of biophysical detail in computational neuroscience modeling, as well as high performance and efficiency in on-line adaptive pattern recognition, serving and bringing together both computational neuroscience and computational intelligence communities that have traditionally pursued disparate computational approaches. The user interface of the platform will support software tools and resources for deep learning and run-time optimization in artificial intelligence applications, and for interference of structure and functional connectivity from recorded neural activity in computational neuroscience research, among others. To facilitate greatest scientific and societal impact, the infrastructure will be made available free of charge, on a time-managed shared basis, to any researcher in return for agreeing to share source code and data necessary to replicate results reported in the literature.

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.

This research project investigates a framework that can significantly improve the energy-efficiency of training artificial intelligence (AI) systems using circuits and system architectures that are based on quantum-tunneling dynamic-analog-memory (DAM) devices. In 2019, the energy required to train a top-of-the-line AI system was more than the energy required to operate five US cars over their entire lifetime. The energy requirements for training large-scale AI systems have only gotten worse since to the point of being unsustainable. The proposed research aims to develop novel learning hardware that will make the training of ML and AI systems more energy sustainable. The project is also developing software tools for training AI systems that can be disseminated and adopted by the research community. The novel online learning and memory consolidation algorithms that are being developed in this project will be integrated with an openly shared, general-purpose neuromorphic cognitive computing platform available through the Neuroscience Gateway (NSG) Portal at the San Diego Supercomputer Center. In collaboration with Efabless Inc. the project is supporting open-source development of mixed-signal integrated circuits (IC) design tools that is being evaluated through in class-room instruction and projects.<br/><br/>The technical activities of this research project are based on an ultra-energy-efficient synaptic element called Fowler-Nordheim Dynamic Analog Memory (FN-DAM) that can be easily fabricated on a standard integrated circuits process. The memory retention property of the synaptic element has been previously shown to be adaptive and can be traded-off with the energy required for synaptic updates. These FN-DAM properties are being explored within the context of the following research objectives: 1) Investigation into novel FN-DAM based neural network training and learning algorithms and architecture: Mechanisms are being explored that can connect the dynamics of FN-DAM array with the training formulations of standard convolutional neural network. Efficient one-shot continual online learning techniques are being investigated that exploit the dynamics of FN-DAM to improve the speed and robustness of learning. The framework is being used to explore connections between the FN-DAM based architectures with neuromorphic memory architectures that combines episodic-memories with incremental learning paradigms; 2) Investigation into novel FN-DAM based compute-in-memory and on-chip learning architectures: Analog compute-in-memory learning architectures are being investigated that integrate FN-DAM arrays with CMOS computing circuits and on-chip adaptation and learning strategies; 3) Validation of the FN-DAM based hardware-software co-design framework: The project is validating the co-design framework for achieving high energy-efficiency in neural network training using the NSF CISE Community Research Infrastructure (CRI) for large-scale neuromorphic cognitive computing developed and maintained at University of California at San Diego (UCSD). The project is also validating the energy-efficiency improvements that can be achieved using prototypes that will be fabricated in a standard integrated circuits process.<br/><br/>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.