Research News
RAPID: Visualizing Epidemical Uncertainty for Personal Risk Assessment
The National Science Foundation RAPID grant for this research was obtained by Rumi Chunara and Enrico Bertini, professors in the Department of Computer Science and Engineering.
COVID-19 is one of the most deadly and fastest transmitting viruses in modern history. In response to this pandemic, news agencies, government organizations, citizen scientists, and many others have released hundreds of visualizations of pandemic forecast data. While providing people with accurate information is essential, it is unclear how the average person understands the widely distributed depictions of pandemic data. Prior research on uncertainty communication shows that even common visualizations can be confusing. One possible source of inappropriate responses to COVID-19 is the lack of knowledge about personal risk and the nature of pandemic uncertainty.
The goal of this research is to test how people understand currently available COVID-19 data visualizations and create communication guidelines based on these findings. Further, the researchers will develop an application to help people understand the factors that contribute to their risk. Users are able to interact with the application to learn about the impact of their actions on their risk. This research provides immediate solutions for teaching people about their personal risk associated with COVID-19 and how their actions influence the risks of others, which could improve the public's response and decrease fatalities. Additionally, this work supports decision making for future pandemics and any subsequent outbreaks of COVID-19 or other viruses.
Specifically, the research team, in an effort to understand how people respond to uncertainty about the nature of the pandemic, is testing the effects of currently available visualizations on personal risk judgments and behavior. By studying how changes in factors influence risk perceptions, the research can contribute to understanding how people conceptualize compound uncertainties from different sources (e.g., uncertainties associated with location, time, demographics and risk behaviors). The researchers are using this information to produce a visualization application that allows people to change the parameters of a simulation to see how the resulting changes affect their risk judgments. For example, users in one city are able to see the pandemic risk to individuals of their age in their zip code and then see how that risk would change if the infection rate increased or decreased.
The aim is to promote intuitive understanding of the epidemiological uncertainty in the forecast through participants? experimentation with the application. While in line with current recommendations for intrinsic uncertainty visualization, this work is the first of its kind to test the effect of user interaction to convey uncertainty through visualization.
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.
Resource constrained mobile data analytics assisted by the wireless edge
The National Science Foundation grant for this research was obtained by Siddharth Garg and Elza Erkip, professors of electrical and computer engineering, and Yao Wang, professor of computer science and engineering and biomedical engineering. Wang and Erkip are also members of the NYU WIRELESS research center.
Increasing amounts of data are being collected on mobile and internet-of-things (IoT) devices. Users are interested in analyzing this data to extract actionable information for such purposes as identifying objects of interest from high-resolution mobile phone pictures. The state-of-the-art technique for such data analysis employs deep learning, which makes use of sophisticated software algorithms modeled on the functioning of the human brain. Deep learning algorithms are, however, too complex to run on small, battery constrained mobile devices. The alternative, i.e., transmitting data to the mobile base station where the deep learning algorithm can be executed on a powerful server, consumes too much bandwidth.
This project that this NSF funding will support seeks to devise new methods to compress data before transmission, thus reducing bandwidth costs while still allowing for the data to be analyzed at the base station. Departing from existing data compression methods optimized for reproducing the original images, the team will develop a means of using deep learning itself to compress the data in a fashion that only keeps the critical parts of data necessary for subsequent analysis. The resulting deep learning based compression algorithms will be simple enough to run on mobile devices while drastically reducing the amount of data that needs to be transmitted to mobile base stations for analysis, without significantly compromising the analysis performance.
The proposed research will provide greater capability and functionality to mobile device users, enable extended battery lifetimes and more efficient sharing of the wireless spectrum for analytics tasks. The project also envisions a multi-pronged effort aimed at outreach to communities of interest, educating and training the next generation of machine learning and wireless professionals at the K-12, undergraduate and graduate levels, and broadening participation of under-represented minority groups.
The project seeks to learn "analytics-aware" compression schemes from data, by training low-complexity deep neural networks (DNNs) for data compression that execute on mobile devices and achieve a range of transmission rate and analytics accuracy targets. As a first step, efficient DNN pruning techniques will be developed to minimize the DNN complexity, while maintaining the rate-accuracy efficiency for one or a collection of analytics tasks.
Next, to efficiently adapt to varying wireless channel conditions, the project will seek to design adaptive DNN architectures that can operate at variable transmission rates and computational complexities. For instance, when the wireless channel quality drops, the proposed compression scheme will be able to quickly reduce transmission rate in response while ensuring the same analytics accuracy, but at the cost of greater computational power on the mobile device.
Further, wireless channel allocation and scheduling policies that leverage the proposed adaptive DNN architectures will be developed to optimize the overall analytics accuracy at the server. The benefits of the proposed approach in terms of total battery life savings for the mobile device will be demonstrated using detailed simulation studies of various wireless protocols including those used for LTE (Long Term Evolution) and mmWave channels.
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.
A survey of cybersecurity of digital manufacturing
This survey was led by Nikhil Gupta, professor mechanical and aerospace engineering and a member of the NYU Center for Cybersecurity; and Ramesh Karri, professor of electrical and computer engineering and co-founder and co-Chair of the NYU Center for Cybersecurity.
The Industry 4.0 concept promotes a digital manufacturing (DM) paradigm that can enhance quality and productivity, which reduces inventory and the lead time for delivering custom, batch-of-one products based on achieving convergence of additive, subtractive, and hybrid manufacturing machines, automation and robotic systems, sensors, com- puting, and communication networks, artificial intelligence, and big data. A DM system consists of embedded electronics, sensors, actuators, control software, and interconnectivity to enable the machines and the components within them to exchange data with other machines, components therein, the plant operators, the inventory managers, and customers.
Digitalization of manufacturing aided by advances in sensors, artificial intelligence, robotics, and networking technology is revolutionizing the traditional manufacturing industry by rethinking manufacturing as a service.
Concurrently, there is a shift in demand from high-volume manufacturing to batches-of-one, custom manufacturing of products. While the large manufacturing enterprises can reallocate resources and transform themselves to seize these opportunities, the medium-scale enterprises (MSEs) and small-scale enterprises with limited resources need to become federated and proactively deal with digitalization. Many MSEs essentially consist of general-purpose machines that give them the flexibility to execute a variety of process plans and workflows to create one-off products with complex shapes, textures, properties, and functionalities. One way the MSEs can stay relevant in the next-generation digital manufacturing (DM) environment is to become fully interconnected with other MSEs by using the digital thread and becoming part of a larger, cyber-manufacturing business network. This allows the MSEs to make their resources visible to the market and continue to serve as suppliers to OEMs and other parts of the manufacturing supply networks.
This article, whose authors include researchers from NYU Tandon and Texas A&M, explores the cybersecurity risks in the emerging DM context, assesses the impact on manufacturing, and identifies approaches to secure DM. It resents a hybrid-manufacturing cell, a building block of DM, and uses it to discuss vulnerabilities; discusses a taxonomy of threats for DM; explores attack case studies; surveys existing taxonomies in DM systems; and demonstrates how novel manufacturing-unique defenses can mitigate the attacks.
The team's research is supported, in part, by the National Science Foundation.
Spatiotemporal Decoupling of Water Electrolysis for Dual-Use Grid Energy Storage and Hydrogen Generation
This research is led by Miguel Modestino, assistant professor of chemical and biomolecular engineering and Yury Dvorkin, assistant professor of electrical and computer engineering.
Increased efforts to curb global warming have led to a drastic surge in the deployment of renewable electricity sources, such as wind and solar power. However, as these sources form a larger fraction of the energy in the grid, their intermittency has started to cause supply instability and large fluctuations in energy prices.
To mediate this challenge, electrochemical energy storage devices such as lithium ion batteries have started to enter the utility scale market. Also gaining traction are redox flow batteries (RFB) because of their long cycle life and power and energy capacity. Despite their promising performance RFB costs remain high due to low energy density of redox energy carriers, which results in large operational costs.
An alternative energy storage method is the production of energy-dense electrofuels, such as hydrogen (H2). H2 has gained significant attention as a promising energy vector for a renewable-rich energy future, given its high gravimetric energy density that makes it desirable for both stationary and mobile applications.
To make electrochemical hydrogen production more competitive, renewable energy sources need to be used with new strategies for electrochemical hydrogen production. The researchers, including Ph.D. students Daniel Frey (lead author) and Jip Kim, show a cerium-mediated decoupled electrolysis system that produces hydrogen and stores energy in the redox couples. They present electrochemical studies to observe the effects of diffusive transport, convective transport, and thermal effects. Following this, the team performed a techno-economic analysis, focusing on the optimization of the system operation and the identification of target operation parameters to achieve hydrogen production at a competitive price.
Leukemia-on-a-chip: Dissecting the chemoresistance mechanisms in B cell acute lymphoblastic leukemia bone marrow niche
Weiqiang Chen, associate professor of biomedical and mechanical and aerospace engineering led this research.
B cell acute lymphoblastic leukemia (B-ALL) hijacks the bone marrow (BM) microenvironment to form leukemic BM “niches,” facilitating chemoresistance and, ultimately, disease relapse. The key to developing more effective, targeted therapies depends on researchers' ability to isolate and examine with these evolving, heterogeneous interactions among distinct B-ALL subtypes and their varying BM niches. Current in vivo methods limit that ability.
A team including researchers from the NYU Tandon School of Engineering's departments of Mechanical and Aerospace, and Biomedical Engineering, and from NYU Langone's Perlmutter Cancer Center and Department of Pathology demonstrated an in vitro organotypic “leukemia-on-a-chip” model to emulate the in vivo B-ALL BM pathology and comparatively studied the spatial and genetic heterogeneity of the BM niche in regulating B-ALL chemotherapy resistance.
In the study "Leukemia-on-a-chip: Dissecting the chemoresistance mechanisms in B cell acute lymphoblastic leukemia bone marrow niche," published in ScienceAdvances, the team used their system to describe the heterogeneous chemoresistance mechanisms across various B-ALL cell lines and patient-derived samples, showing that the leukemic perivascular, endosteal, and hematopoietic niche-derived factors maintain B-ALL survival and quiescence. Furthermore, they demonstrated the preclinical use of their lab-on-a-chip model to test niche-cotargeting regimens, which may translate to patient-specific therapy screening and response prediction.
This work was supported by the National Science Foundation, the U.S. National Institutes of Health, the Leukemia & Lymphoma Society, the New York State Department of Health (NYSTEM Program), and the St. Baldrick’s Cancer Research Foundation (I.A).
Good for the Many or Best for the Few? A Dilemma in the Design of Algorithmic Advice
Oded Nov, professor of Technology Management and Innovation led this research.
Applications in a range of domains, including route planning and well-being, offer advice based on the social information available in prior users' aggregated activity.
A team of collaborators, including Graham Dove, a research assistant professor at NYU CUSP; Martina Balestra, a post-doc at NYU CUSP; and Devin Mann of NYU Grossman School of Medicine, considered a dilemma in the design of AI-based advice applications. Using an experiment, they studied whether, when designing these applications, it is better to offer:
• Goal-Directed advice: advice that is more likely to result in users who adhere to it achieving their goal, but which users are less likely to adopt and adhere to, or
• Adoption-Directed advice: advice that is more likely to be adopted and adhered to by a greater number of users, but which is less likely to result in a user fully achieving their goal
To explore this question the collaborators conducted an online experiment undertaken in four advice domains (financial investment, making healthier lifestyle choices, route planning, training for a 5k run), with three user types, and across two levels of uncertainty. Their findings suggest a preference for advice favoring individual goal attainment over higher user adoption rates, albeit with significant variation across advice domains; and discuss their design implications. The research is published in the Proceedings of the ACM on Human-Computer Interaction.
AI-powered and Robot-assisted Manufacturing for Modular Construction
Semiha Ergan, assistant professor in the Departments of Civil and Urban Engineering, and Computer Science and Engineering and Chen Feng, assistant professor in the departments of Civil and Urban and Mechanical and Aerospace Engineering will lead this project.
Modular construction, with an established record of accelerating projects and reducing costs, is a revolutionary way to transform the construction industry. However, new construction capabilities are needed to perform modular construction at scale, where, as is the case in factories, the industry suffers from the dependency on skilled labor. Among the challenges this project aims to address:
- Every project is unique and requires efficiency and accuracy in recognition and handling workpieces
- Design and production-line changes are common, and require design standardization and optimization of modules, and
- Production lines are complex in space and time, and necessitate the guidance of workers while processing design and installation information accurately
To focus on these challenges while exploring modular construction within the context of Future Manufacturing (FM), this project exploits opportunities at the intersection of AI/robotics/building information modeling and manufacturing, with the potential to increase the scalability of modular construction.
The research will pioneer initial formulations to enable (a) high throughput in manufacturing through the definition and evaluation of processes that embrace real-time workpiece semantic grounding and in-situ AR-robotic assistance, (b) feasibility studies of optimizing and standardizing the design of modules, and utilization of a cyber-infrastructure for their standardization, (c) prototyping cyber-infrastructures as both novel ways of forming academia and industry partnerships, and data infrastructures to accelerate data-driven adaption in FM for modular construction, and (d) synergistic activities with a two-year institution to train and educate FM workforce for the potential of FM and technologies evaluated.
The team argues that, while the evaluations of technologies will focus on the modular construction, the proposed technologies could make manufacturing industries more competitive, particularly heavy manufacturing industries that share similar challenges such as agricultural, mining, and ship building. The project will therefore enhance U.S. competitiveness in production, bolster economic growth, educate students, and influence workforce behavior towards efficiency and accuracy with the skills required for leadership in FM.
Tandon Team Leads NYC Future Manufacturing Collective
Nikhil Gupta, professor of mechanical and aerospace engineering is principal investigator on this project with Kurt Becker professor of applied physics and Vice Dean of Research Innovation, and Entrepreneurship; and Justin Hendrix, executive director of the NYC Media Lab at NYU Tandon.
Extensive use of sensors, computers and software tools in product design and manufacturing requires traditional manufacturing education to evolve for the new generation of cyber-manufacturing systems. However, with a widening gap between today's education and training curricula and the actual skills required by industry, innovative solutions are needed to prepare the workforce for the evolution of manufacturing.
The New York City Future Manufacturing Collective (NYC-FMC) will develop a network of multidisciplinary researchers, educators, and stakeholders in New York City to explore future cyber manufacturing research through the lens of the worker's relationship to an increasingly complex and technologically driven environment and set of processes. The NYC-FMC will:
- advance related technologies as well as the underlying systems, processes, and organizational conditions to which these interfaces are connected, to change and drive the roles of people in manufacturing.
- organize a variety of activities, including an internship program for students to obtain exposure to industrial environments by engaging major manufacturing based corporations, a newsletter to define the state-of-the-art in manufacturing technologies and the new manufacturing ecosystem, and two manufacturing-focused symposia each year.
- build a coalition of multidisciplinary faculty from NYC universities, industry executives and technologists, investors, entrepreneurs, public sector and other relevant manufacturing ecosystem participants.
The network will rely on faculty leadership at NYU and Columbia University, as well as a broad network of researchers from other NYC institutions, in the areas of manufacturing, computer vision, robotics, machine learning, virtual and augmented reality, human behavior and cognition, economics and other relevant areas. Executives and technologists from industry, including large manufacturing concerns with a connection to the greater NYC region and beyond and startup companies in the Brooklyn Navy Yard's New Lab, will provide stimulus from the private sector and help create conditions to advance education and research goals. This coalition will build a novel education and workforce training program framework to create a learning and feedback loop between researchers, industry partners, and the workforce focused on the future cyber-manufacturing systems.
The project, supported by a $500,000 Future Manufacturing grant from the National Science Foundation.
Dissecting the immunosuppressive tumor microenvironments in Glioblastoma-on-a-Chip for optimized PD-1 immunotherapy
Weiqiang Chen, associate professor of biomedical and mechanical and aerospace engineering led this research.
Glioblastoma (GBM) is the most common and aggressive primary brain tumor among adults, with an average survival of less than 14 months despite aggressive surgery, chemotherapy, and radiotherapy. One of the most promising therapies has been the inhibition of programmed cell death protein-1 (PD-1), which can turn the immune system against the tumors in order to destroy it. However, that approach has been difficult due to the genetic difference between blastomas and the environments they form in.
Improving the clinical use of anti-PD-1 immunotherapy in GBM patients requires a comprehensive understanding of tumor genetics and microenvironment as well as the ability to dissect the dynamic interactions among GBM and immune suppressor cells. Now, a team of researchers led by NTU Tandon’s Weiqiang Chen have integrated critical markers of these microenvironments in a microfluidics-based ex vivo microphysiological system termed ‘GBM-on-a-Chip.’
Modeling the human immune environment in the current animal-based cancer models is challenging. Compared to chemotherapy, it is difficult to preclinically validate and study immunotherapy. Discrepancies between preclinical and clinical results have raised concerns about how the findings from the current models can be translated to patients. The engineered tumor model on chips can be an alternative to the current animal models and patient studies, and even achieve a so-called "clinical trial on chips" for a pre-screening of patients suitable for immunotherapy, and screening personalized therapy for each patient. These chips are patient-specific, allowing longitudinal analysis of cells to understand how the environment around a tumor changes the way that PD-1 cells act.
The team – including researchers from NYU Tandon, NYU Langone Health and NYU School of Medicine, with funding from the National Science Foundation and National Institutes of Health – were able to use the results of this longitudinal study to show that molecularly distinct GBM subtypes have distinct epigenetic and immune signatures that may lead to different immunosuppressive mechanisms. They could see which cells were elevated past their healthy amounts, and which were not responding as they should. This allowed them to administer a specialized treatment for the specific environment, supressing some cells like tumor-associated macrophages while boosting the effectiveness of specific T-cells. Thus, they showed that these patient-specific chips could lead to personalized immunotherapy screening, potentially improving therapeutic outcomes in GBM patients.
The research was conducted with investigators from NYU Tandon, NYU Langone Health, and the NYU School of Medicine.
Enabling Remote Whole-Body Control with 5G Edge Computing
Huaijiang Zhu, a Ph.D. student in electrical and computer engineering was lead author.
There are many real-world — and, someday, off-world — applications for light-weight, energy-efficient, fully autonomous robots. Yet the more autonomous a robot is, the greater its computational requirements. Onboarding the components to handle this computational function adds weight, cost and reduces potential for applications in hostile environments.
It might thus be desirable to offload intensive computation--not only sensing and planning, but also low-level whole-body control--to remote servers in order to reduce on-board computational needs. Fifth Generation (5G) wireless cellular technology, with its low latency and high bandwidth capabilities, has the potential to unlock cloud-based high performance control of complex robots. However, state-of-the-art control algorithms for legged robots can only tolerate very low control delays, which even ultra-low latency 5G edge computing can sometimes fail to achieve.
In this work, the investigators, led by Ludovic Righetti, associate professor of electrical and computer engineering and mechanical and aerospace engineering, and a member of NYU WIRELESS, investigate the problem of cloud-based whole-body control of legged robots over a 5G link. Their novel approach consists of a standard optimization-based controller on the network edge and a local linear, approximately optimal controller that significantly reduces on-board computational needs while increasing robustness to delay and possible loss of communication. Simulation experiments on humanoid balancing and walking tasks that includes a realistic 5G communication model demonstrate significant improvement of the reliability of robot locomotion under jitter and delays likely to experienced in 5G wireless links.
The team included Kai Pfeiffer, a postdoctoral researcher, and Manali Sharma, a master's student in the Department of Electrical and Computer Engineering; Sundeep Rangan, professor of electrical and computer engineering and associate director of NYU WIRELESS; and Marco Mezzavilla, a research scientist at NYU WIRELESS.
The project, funded in part by the National Science Foundation National Robotics Initiative and OPPO Mobile Telecommunications, will be presented at the IEEE/RSJ International Conference on Intelligent Robots and Systems in October, 2020.