Oded Nov - US grants

This project lays the foundations for the analysis and design of peer production systems consisting of socially interacting volunteers and machines that jointly perform distributed tasks. The envisioned implementation targets the unique scientific and societal domain offered by cyber-enabled citizen science environmental control in the highly polluted Gowanus Canal in Brooklyn. The proposed "Brooklyn Atlantis" citizen science system consists of an array of mobile instrumented buoys for water monitoring with wireless capabilities, controlled by volunteers using a web-based peer-production system.

Through the integration of dynamical systems theory, marine robotics, and technology-mediated social participation, the project will develop: 1) a model for complex systems comprising networks of humans and machines with a focus on technology-mediated social participation systems; 2) design guidelines for peer production systems to enhance participation in citizen science projects; and 3) a cyber-human infrastructure for real time monitoring and hazard detection of the natural environment combining mobile robotic sensor networks and human operated control systems.

The broader impacts of this project include: 1) establishing a technology-based avenue for enhancing local community citizenry and engagement; 2) developing citizen based environmental monitoring platforms that could provide life-saving advantages; 3) leveraging interactive robotics, environmental awareness, and citizen science to foster interest and engagement in STEM fields among the general public and K-12 students; 4) fostering multidisciplinary thinking among NYU-Poly students; 5) promoting the awareness of citizen science capabilities among researchers; and 6) integrating research components in outreach programs. This project has the potential of transforming the field of dynamical systems through the integration of collective human intelligence in the design and adaption of engineered architectures. Similarly, it may transform thinking on social peer production systems to incorporate real-time mobile interactive machines and task co-execution.

This project seeks to transform the study of technology-mediated social participation through the human-centered computing equivalent of genetically targeted medicine. Just as advances in medicine enable us to use information about a person's genetic profile to target medical treatment to his needs, information about a user's personal attributes such as his motivations and personality traits can be used to target individually-tailored, theory-driven design aimed at increasing the user's participation in technology-mediated social efforts. The research focus is on technology-mediated citizen science, which offers an ideal laboratory for studying issues that are important in many other fields. The project will involve three citizen science modalities: distributed analysis, distributed data gathering, and volunteer computing. The research will test the effectiveness of design features informed by social psychology theory and human-computer interaction research, and develop a rigorous theoretical understanding of individually-tailored design.

The project seeks to advance human-centered computing theory and practice. The intellectual merits of the research therefore include: 1) advancing technology-mediated social participation theory by developing a theoretical framework that combines personal attributes and design; 2) developing and testing empirically a novel technique and specific design guidelines to enhance technology-mediated social participation, with application to citizen science projects.

The unique setting of this research within the citizen science domain promises long-term benefits to society and science. In particular, the broader impacts of the research include: 1) leveraging technology-mediated social participation and citizen science to engage members of the public in science and scientific work, and in particular, members of disadvantaged communities to whom traditional science-related activities may otherwise not be accessible; 2) enhancing the infrastructure for scientific research through effective citizen science; and 3) integrating technology-mediated citizen science in outreach programs.

This project will explore the roles human-computer interaction can play in helping non-experts to understand and engage with their personal genomic information. Recent years are seeing a dramatic growth in the scale and scope of personal genomic information that is available to non-experts, often online and in interactive forms. As a result, individuals are confronted with unprecedented amounts of sensitive and often complex information about themselves, which influences their decisions, emotional states and well-being. Consequently, questions about how people make sense of and engage with their personal genomic information, and how comfortable they feel about sharing it in order to advance scientific and biomedical research, are not only of paramount importance for life sciences researchers and policy makers, but also a pressing issue for human-computer interaction researchers.

Because the technology that enables lay people to interact with such information is new, there is little research on it. Given the tremendous growth in the scale and scope of genomic information available to non-experts, this work has the potential to make an impact on human-computer interaction theory and practice by investigating fundamental issues concerning non-expert interaction with complex scientific information and the impact of user interface design interventions on learning from, and sharing of, personal genomic information. Advances in five areas will be sought: (1) Functional requirements for supporting meaningful engagement of non-experts with personal genomic information; (2) Fundamental knowledge of how user interface design interventions impact users' willingness to share personal genomic data; (3) Novel interaction techniques for presenting and exploring rich, complex, and highly personalized data sets by non-experts; (4) Design guidelines for effective interaction with personal genomic information, and (5) Methodology for evaluating the effectiveness of techniques for interaction with personal genomic information.

A safe and productive society increasingly depends on a safe and trustworthy cyberspace. However, extensive research has repeatedly shown that the human factor is often the weakest part in cyberspace, and that users of information systems are often exposed to great risks when they respond to credible-looking emails. Thus, spear phishing attacks - which attempt to get personal or confidential information from users through well-targeted deceptive emails - represent a particularly severe security threat.

Addressing this threat, in this project we use a combination of surveys and experiments to examine the psychological, educational and cultural factors that contribute to the users' vulnerability and response to spear phishing attacks, and their ability to detect deception. An important aspect of the project is an in vivo, multi-site setting: studies are conducted in university and commercial enterprise setting, as well as across different cultures - in all cases using realistic spear phishing email attacks. Using a three-dimensional experimental design, in this cross-disciplinary research project we (i) identify the underlying factors for the success of different spear phishing attack strategies; (ii) develop novel types of cyber-defenses that are tailored to users' idiosyncratic characteristics; (iii) validate the usefulness of personality-targeted defense in a comparative, multi-organizational, real-world settings; and (iv) develop a new, collaborative avenue for cross-disciplinary research of social scientists and computer scientists.

This project advances the study of socio-technical systems and social participation by investigating how system design, structure and agency are associated with different paths to participation in technology-mediated social participation (TMSP) systems. Online systems create organizational structural constraints and affordances by design, and therefore channel the potential for human agency -- roles, responsibilities and actions -- in ways that are not usually possible in the offline world. The project employs a mixed-methods design to examine collective behavior in different TMSP systems using longitudinal surveys and behavioral log data. In addition, this research includes a series of field experiments to test specific relationships between structural and organizational designs that facilitate collective outcomes and paths to participation. The project?s intellectual merits include: (i) developing a rigorous understanding of how social participation changes over time as a result of systems design, socio-technical structure and human agency; (ii) extending the long history of social scientific theories and empirical research on collective behavior, motivation, and community participation in both laboratory and real world environments; and, (iii) developing specific recommendations and design guidelines for future TMSP and citizen science systems.

This project will foster increased public participation in TMSP systems, and increased scholarly awareness of technology-mediated collaborations and citizen science as effective research resources. It will also encourage cross-disciplinary, cross-campus collaboration, by connecting scholars from a variety of disciplinary backgrounds, to study and advance TMSP research. In addition, it will contribute to education, by providing opportunities for students to reason about, design and implement new TMSP systems to connect large numbers of distributed volunteers with social needs.

1547864(Porfiri)

Humans are being continuously involved as cognitive and social agents in engineered systems, and, in a similar vein, engineered systems are becoming increasingly integral to systems of interacting humans. A particularly elusive issue entails understanding and predicting the evolution of the collaboration between humans and engineered systems, across behavioral and technological domains. This award supports experimental and theoretical research to elucidate the dynamics of collaboration in human-engineered systems, through the integration of dynamical systems theory, robotics, and human-computer interactions. Humans will socially interact with engineered systems in an engineering context, which will be, in turn, enabled by an engineering infrastructure. This research will contribute to lay the foundation for the next generation of autonomous environmental monitoring systems, which capitalize on human intelligence and low-cost distributed robots for rapidly and accurately monitoring the environment. Complementing the research are interdisciplinary formal and informal education activities that will benefit the training of underprivileged students and reach out to economically-disadvantaged local communities.

This research program seeks to establish a transformative experimental and theoretical framework for understanding, predicting, and, ultimately, controlling the evolution of the collaboration between humans and engineered systems. In a novel crowdsourcing infrastructure, online groups comprised of real humans and artificial experts will collaboratively perform aquatic environmental monitoring, by virtually patrolling mobile aquatic robots in a polluted canal in Brooklyn, NY, to explore the water basin, collect and classify wildlife images, and identify sources of pollution. Similar to a mechanics experiment in which one applies a sequence of mechanical forces to a solid and measures its mechanical deformation, this project will study the response of humans to the controlled actions of artificial experts and investigate their intertwined dynamics, in terms of social interactions and task performance. A series of hypothesis-driven studies will explore the roles of human cognitive abilities, dispositional factors, and behavioral plasticity on technology-mediated social interactions and performance. A new data-driven mathematical framework based on signal processing, network science, and information theory will be formulated to uncover the interplay between personal attributes and plasticity of humans, their interactions with engineered systems, and how to better design a human-engineered system for collective expertise.

This Frontier award supports the SONYC project, a smart cities initiative focused on developing a cyber-physical system (CPS) for the monitoring, analysis and mitigation of urban noise pollution. Noise pollution is one of the topmost quality of life issues for urban residents in the U.S. with proven effects on health, education, the economy, and the environment. Yet, most cities lack the resources for continuously monitoring noise and understanding the contribution of individual sources, the tools to analyze patterns of noise pollution at city-scale, and the means to empower city agencies to take effective, data-driven action for noise mitigation. The SONYC project advances novel technological and socio-technical solutions that help address these needs.

SONYC includes a distributed network of both sensors and people for large-scale noise monitoring. The sensors use low-cost, low-power technology, and cutting-edge machine listening techniques, to produce calibrated acoustic measurements and recognizing individual sound sources in real time. Citizen science methods are used to help urban residents connect to city agencies and each other, understand their noise footprint, and facilitate reporting and self-regulation. Crucially, SONYC utilizes big data solutions to analyze, retrieve and visualize information from sensors and citizens, creating a comprehensive acoustic model of the city that can be used to identify significant patterns of noise pollution. This data can in turn be used to drive the strategic application of noise code enforcement by city agencies, in a way that optimally reduces noise pollution. The entire system, integrating cyber, physical and social infrastructure, forms a closed loop of continuous sensing, analysis and actuation on the environment.

SONYC is an interdisciplinary collaboration between researchers at New York University and Ohio State University. It provides multiple educational opportunities to students at all levels, including an outreach initiative for K-12 STEM education. The project uses New York City as its focal point, involving partnerships with the city's Department of Environmental Protection, Department of Health and Mental Hygiene, the business improvement district of Lower Manhattan, and ARUP, one of the world's leaders in environmental acoustics. SONYC is an innovative and high-impact application of cyber-physical systems to the realm of smart cities, and potentially a catalyst for new CPS research at the intersection of engineering, data science and the social sciences. It provides a blueprint for the mitigation of noise pollution that can be applied to cities in the US and abroad, potentially affecting the quality of life of millions of people.

Citizen science involves the general public in research activities that are conducted in collaboration with professional scientists. Citizens' participation shortens the duration and lowers the costs of certain research activities. A key challenge inhibiting the widespread adoption of citizen science is guaranteeing the reliability of contributions submitted by volunteers. Traditional approaches have relied on redundant distribution of tasks, whereby multiple volunteers are indiscriminately assigned identical tasks. However, most citizen science projects suffer from a scarcity of long term contributors and an abundance of casual, short term volunteers. Drawing inspiration from species across every phylum of life where physical and behavioral heterogeneities are evolutionarily selected, this EArly-concept Grant for Exploratory Research (EAGER) project posits that heterogeneities in citizen scientists will improve the reliability of data gathered. The envisioned paradigm will promote the progress of science, by enabling researchers to quickly gather large quantities of reliable data with minimal changes to existing infrastructure. Outcomes of this project will be mutually beneficial to researchers and society at large: researchers will have more confidence in citizen science and put forward more exciting projects which will contrive to enhance the scientific literacy of the public.

This research program seeks to demonstrate a novel methodology to cogently distribute tasks among volunteers based on prior performance, affinity to the project, and technical potential. Specifically, the project hypothesizes that data obtained from subsamples of participants that are highly heterogeneous in terms of individual attributes will lead to more reliable data, thereby enabling a significant reduction in the degree of task redundancy and an improvement in data quality. This hypothesis will be tested within Brooklyn Atlantis, an online citizen science project for monitoring the environmental health of the Gowanus Canal - a highly polluted Superfund site. In Brooklyn Atlantis, citizen scientists identify objects of interest in images taken from the surface of the canal through an aquatic robot. A series of studies will be performed to: i) elucidate the relationship between data reliability and individual attributes; ii) quantify the potential of data fusion to enhance quality and accuracy of contributions; and iii) understand the role of group heterogeneity on data reliability. Rigorous statistics and constrained optimization will drive the implementation of an optimal task allocation engine for use in distributed citizen science applications.

The project will develop, implement, and assess an initiative to promote robotic design and entrepreneurship experiences among students and teachers. Each year, 16 teachers and 32 students from 8 high schools located in all 5 boroughs of New York will attend a 4-week summer institute consisting of a 2-week guided training and a 2-week collaborative robotic-product development. The participants will come primarily from schools in underserved neighborhoods with socially, economically, racially, and ethnically diverse student bodies; approximately half of the participants will be female. During the academic year, each school's two teachers will conduct a robotics course for at least 25 students. Classroom adoption will be facilitated through a professional learning community (PLC). In the annual grand finale, school teams will compete in a robot product design and business idea contest, modeled after the Inno/Vention contest coordinated by the Incubator Initiatives of NYU Tandon School of Engineering. Over the 3-year project duration, 48 teachers and 96 students from 24 high schools will participate in the summer institute with 160 contact hours. Through the academic year elective, teachers will engage 1,200 students during the project funding period. The project has formed an interdisciplinary team including experts in robotics, entrepreneurship, K-12 curriculum design, and assessment to support project design and implementation.

The project design adapts features from research on project-based learning (PBL), robotics and entrepreneurship in K-12 STEM education, social cognitive career theory, and teacher professional development embedded in a PLC. Formulating robotics activities in a PBL framework will help participants learn content, develop planning and problem-solving skills, and foster their higher-order cognitive skills. Integration of PBL with entrepreneurship activities will address participant fear of failure, lack of confidence, and creativity and communication skills. The design of the teacher professional development will support transfer of training through content-immersion, allow modeling and rehearsing of desired skills, and involve teachers for a sufficient duration to support the cognitive demands of new learning. The project will research the broad overarching question: Do robotics design and entrepreneurship activities, experienced through PBL, positively influence teacher practices and student outcomes? The project will investigate if participation (1) builds teacher capacity to effectively utilize PBL, contextualized in robotics and entrepreneurship, to promote STEM learning and (2) positively impacts students self-efficacy, outcome expectations, goals, and interest in STEM studies and careers. This project is funded by the Innovative Technology Experiences for Students and Teachers (ITEST) program that supports projects that build understandings of best practices, program elements, contexts and processes contributing to engaging students in learning and developing interest in STEM, information and communications technology (ICT), computer science, and related STEM content and careers.

1604355 - Porfiri

Robotic devices have been playing an increasingly central role in physical rehabilitation due to their capability to support an increasing range of therapeutic treatments, minimize therapist time commitment, and record the performance of patients. To further extend rehabilitation treatments beyond clinical settings and enhance rehabilitation progress, considerable effort has been devoted toward robot-mediated telerehabilitation, which allows a physical therapist to remotely monitor and supervise several patients simultaneously. However, the cost of these devices and the repetitive nature of the prescribed exercises have significantly hampered the practicality of robot-mediated telerehabilitation. This project will open new directions for transforming robot-mediated telerehabilitation, through the integration of therapeutic treatments with low-cost haptic devices and interactive online citizen science activities. In the envisioned paradigm, participants will contribute to citizen science by performing activities that are part of their therapeutic regiment, and consequently increase the time spent on otherwise-boring rehabilitation activities. This integration is expected to not only contribute to scientific research but also increase patient self-esteem.

This research program seeks to advance upper limb robot-mediated telerehabilitation for patients recovering from stroke by empowering them through active science participation. The envisioned system comprises a low-cost haptic joystick interfaced to a PC, which affords online social interactions in a citizen science research project. Patients will contribute to an environmental monitoring citizen science project developed by the research team, by analyzing images acquired by an aquatic mobile robot in a polluted canal in Brooklyn, NY, while interacting with other patients online. By harnessing the motivation and interests of patients in science and the environment, the proposed approach aims to maximize retention and efforts towards rehabilitation. The analysis will be performed using a haptic joystick, which provides a force-feedback to the patient while recording salient rehabilitation performance indices for upper limb rehabilitation. The system will be tested on both healthy subjects and patients undergoing rehabilitation for post-stroke hemiparesis through a series of experimental studies that elucidates the combined effects of force feedback and social interactions on patient performance and satisfaction.

Recent years are seeing a sharp increase in the availability of personal and environmental 'omic' data about genomes or microbiomes to non-experts. Popular omic testing services produce data about people's personal genome, about their microbiome, and about plants and organisms in their living environment. As a result, people with no formal education in the life sciences can get access to their omic data by sending a self-collected sample to a direct-to-consumer testing provider, and results are delivered online. These people then need to interpret large amounts of complex data involving sensitive issues such as disease risk, carrier status, and potentially meaningful correlations with health and physical traits. While personal omics promise advances in health, for example through precision medicine, the slew of potential findings and the rapidly evolving interpretations these approaches produce are difficult to communicate to most non-experts. This project aims to empower people to explore, share, curate and better understand such data, which in turn can make substantial impact on their wellbeing. The project will identify user needs and develop novel human-computer interfaces to help people make sense of personal, social, and environmental omic data. These tools will be evaluated in a longitudinal study in real households, both validating the work and providing direct impact on participants' understanding of the data and their wellbeing. The tools will also be available through Open Humans, an open platform that brings together researchers, citizen scientists, and members of the public who share their personal omic data. In addition, the project will increase omic literacy among non-experts and contribute to increasing the participation of women and other underrepresented minorities in STEM research.

The project will conduct research on human-computer interaction for UbiqOmic environments: living spaces and social interactions where omic data is available about people, plants, animals, and surfaces. In particular, the team will identify user needs and develop web-based visual tools that integrate omic data sets from heterogeneous resources and multiple samples. These tools will allow users to aggregate, explore, relate, and connect pervasive omic information, and facilitate collaborative sense making of omic information within various social contexts including families and cohabiting communities. In addition, the project will harness the power of augmented reality (AR) to visualize the invisible, designing, developing, and evaluating an AR interface which overlays timely and actionable omic data in the environment and on the user's own body (oral, gut, skin). The team will evaluate these tools with both general audiences and early adopters through a series of usability and longitudinal studies. The project will advance the fields of human-computer interaction, computer-supported cooperative work, and personal informatics.

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 AISL Pilots and Feasibility project will study the data science learning that takes place as members of the public explore and analyze open civic data related to their everyday lives. Government services, such as education, transportation, and non-emergency municipal requests, are becoming increasingly digital. Generally, program workshops and events may be able to support participants in using such data to answer their own questions, such as: "How do City agencies respond to noise in my neighborhood?" and "How do waste and recycling services in my neighborhood compare with others?? This project seeks to understanding how such programs are designed and facilitated to support diverse communities in accessing and meaningfully analyzing data will promote innovation and knowledge building in informal data science education. The team will begin by summarizing best practices in data science education from a variety of fields. Next they will explore the design and impacts of two programs in New York City, a leader in publicly available Open Data initiatives. This phase will explore activities and facilitation approaches, participants? objectives and data literacy skills practice, and begin to identify potential barriers to entry and levels of participation. Finally, the team will build capacity for other similar organizations to explore and understand their impacts on community members? engagement with civic data. This pilot study will establish preliminary evidence of the effectiveness of these programs, and in turn, inform future research into the identifying and amplifying best practices to support public engagement with data.

This research team will begin by synthesizing data science learning best practices based on varied literatures and surveys with academic and practitioner experts.
Synthesis results will be applied as a lens to gather preliminary evidence regarding the impacts of two programs on participants? data science practices and understanding of the nature of data in the context of civics. The programs include one offered by the Mayor's Office of Data Analytics (MODA), which is the NYC agency with overall responsibility for the City?s Open Data programs, and BetaNYC, a leading nonprofit organization working to improve lives through civic design, technology, and engagement with government open data. The research design triangulates ethnographic observations and artifacts, pre and post adapted surveys, and interviews with participants and facilitators. Researchers will identify programmatic metrics and adapts existing measures to assess various outcomes related to public engagement with data, including: question formulation, data set selection and manipulation, the use of data to make inferences, and understanding variability, sampling and context. These metrics will be shared through an initial assessment framework for data science learning in the context of community engagement with civic open data. Researchers will also begin to identify barriers to broader participation through literature synthesis, interviews with participants and facilitators, and conversations with other organizations in our networks, such as NYC Community Boards. Findings will determine the suitability of the programs under study and inform future research to identify and amplify best practices in supporting public engagement with data.

This project is funded by the NSF Advancing Informal STEM Learning program, which seeks to advance new approaches to, and evidence-based understanding of, the design and development of STEM learning in informal environments. This includes providing multiple pathways for broadening access to and engagement in STEM learning experiences, advancing innovative research on and assessment of STEM learning in informal environments, and developing understandings of deeper learning by participants.

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.

As the use of digital health technologies grows, gaps between the potential of new technologies, existing healthcare practices, and workers’ preparedness for new technologies limit the potential of digital health to achieve acceptance and effective utilization at scale. This transition to scale research project views inclusion as a key driver of scale in future technology-facilitated healthcare work. Inclusive technology for healthcare work will enable workers in diverse roles and skills to leverage increasing access to data-driven technologies. The project focuses on the growth of Data-Intensive Technologies (DIT), which include telehealth and AI-based tools. The project’s approach to transition to scale centers on alleviating existing misalignment between current healthcare work and data-intensive technologies in three ways. First is through the co-development of tools and generalizable design principles with users that lower the barrier to technology integration for healthcare workers. Second is by empowering individuals within healthcare systems who have diverse roles to adopt and use the tools and improve their skills. Third is to enable patient-centered healthcare that promotes autonomy and strengthens clinician-patient concordance. The project represents a multi-institutional commitment to transitioning innovative healthcare to scale, through DIT facilitated inclusion of diverse workers in healthcare systems across the U.S., which together encompass over 1000 care sites in U.S. 24 states, multiple work roles, and different levels of training and hierarchy.

This project brings together several scientific fields, including human-computer interaction, health informatics, artificial intelligence (AI), sensing, medicine, organizational behavior, and research on diversity and inclusion. The investigator team is structured to achieve multiple convergent goals such as quantifying the impacts of scaling DIT on inclusive healthcare work and modelling prescription and adoption of DIT towards inclusive deployment at scale. Additionally, the investigators seek to identify generalizable DIT design principles for inclusive healthcare work at scale, and to develop theory and tools to facilitate at-scale inclusion through DIT-based patient-provider concordance. Finally, the project expects to develop tools and practices for lowering barriers to comprehension of and engagement with DIT by diverse healthcare workers; to create AI-based team-focused tools; and to analyze the opportunities and challenges in using AI for diverse healthcare teams’ work. This project has been funded by the NSF Future of Work at the Human-Technology Frontier cross-directorate program to promote deeper basic understanding of the interdependent human-technology partnership in work contexts by advancing design of intelligent work technologies that operate in harmony with human workers.

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.