Funded Research Grants

SANDY: Sparsification-Based Approach for Analyzing Network Dynamics

PI: Sajal K. Das
Award amount: $163,067
Award date: 9/1/17 to 8/31/20

The goal of this three-year project, Sparsification-based Approach for Analyzing Network Dynamics (SANDY), is to develop a suite of scalable parallel algorithms for updating dynamic networks for different problems that can be executed on a wide range of HPC platforms. Dynamic network analysis will enable researchers to study the evolution of complex systems in diverse disciplines, such as bioinformatics, social sciences, and epidemiology. The SANDY project is expected to initiate a new direction of research in developing parallel dynamic network algorithms that will benefit multiple analysis objectives (e.g., motif finding and network alignment) and application domains (e.g., epidemiology, health care).  [Read more] [Sept 24, 2018]

Manufacturing USA: Intelligent Human-Robot Collaboration for Smart Factory

NRI: INT: COLLAB: Manufacturing USA: Intelligent Human-Robot Collaboration for Smart Factory

PI: Zhaozheng Yin
Co-PI: Ming C. Leu
Award amount $667,656
Award date: 9/15/18 to 8/31/22

This National Robotics Initiative (NRI) collaborative research project addresses the NSF Big Idea of Work at the Human-Technology Frontier by targeting human-robot collaboration in manufacturing. Recent advances in sensing, computational intelligence, and big data analytics have been rapidly transforming and revolutionizing the manufacturing industry towards robot-rich and digitally connected factories. However, effective, efficient and safe coordination between humans and robots on the factory floor has remained a significant challenge. To meet the need for safe and effective human-robot collaboration in manufacturing, the investigators will research an integrated set of algorithms and robotic test beds to sense, understand, predict and control the interaction of human workers and robots in collaborative manufacturing cells. It is expected that these methods will significantly improve the safety and productivity of hybrid human-robot production systems, thereby promoting their deployment in future "smart factories".  [Read more] [Sept 24, 2018]

NeTS: JUNO2: Collaborative Research: STEAM: Secure and Trustworthy Framework for Integrated Energy and Mobility in Smart Connected Communities

PI: Sajal K. Das
Award amount: $91,090
Award date: 9/1/18 to 8/31/19

The rapid evolution of data-driven analytics, Internet of things (IoT) and cyber-physical systems (CPS) are fueling a growing set of Smart and Connected Communities (SCC) applications, including for smart transportation and smart energy. However, the deployment of such technological solutions without proper security mechanisms makes them susceptible to data integrity and privacy attacks, as observed in a large number of recent incidents. If not addressed properly, such attacks will not only cripple SCC operations but also influence the extent to which customers are willing to share data. This in turn will make trustworthiness in SCC applications very challenging. To address this, a synergistic team of researchers from the US and Japan, under the JUNO2 program, will collaborate on this project, called STEAM (Secure and Trustworthy framework for integrated Energy and Mobility) to develop a framework to ensure data privacy, data integrity, and trustworthiness in smart and connected communities.  [Read more] [Sept 24, 2018]

A sample Cyber-Physical system is a water treatment system in which each component is in its own security domain. Each portion of the process is monitored (each with a wise owl who is an oracle of knowledge) through a valuation V (its spyglass) from that domain to other domains that contain information from the physical owls, cyber owls, and knowledge in the form of invariants (the books) to ensure system operation. ” Figure courtesy of Sarah Martin.

CPS: TTP Option: Medium: Collaborative Research: Trusted CPS from Untrusted Components

PI: Bruce McMillin
Co-PI's: Rui Bo & Jonathan Kimball

Amount: $962,695
Time Period: 10/1/2018 – 09/30/2021

The nation's critical infrastructures are increasingly dependent on systems that use computers to control vital physical components, including water supplies, the electric grid, airline systems, and medical devices. These are all examples of Cyber-Physical Systems (CPS) that are vulnerable to attack through their computer systems, through their physical properties such as power flow, water flow, chemistry, etc., or through both. The potential consequences of such compromised systems include financial disaster, civil disorder, even the loss of life.  [Read more] [Sept 24, 2018]

CRII: III: Understanding Urban Vibrancy: A Geographical Learning Approach Employing Big Crowd-Sourced Geo-Tagged Data

PI: Yanjie Fu
Amount: $174,515
Time Period: 8/1/2018 – 07/31/2020

Vibrant communities are defined as places with the following features: permeability, vitality, variety, accessibility, identity, and legibility. Developing vibrant communities can help boost commercial activities, enhance public security, foster social interaction and, thus, yield livable, sustainable, and viable environments. With the advent of the mobile and sensing technologies, big crowd-sourced geo-tagged data (BCGD) are increasingly available from diverse sources (e.g., buildings, vehicles, human, sensors, devices) in urban space, and represent an invaluable source of intelligence for understanding urban vibrancy and enhancing smart growth. [read more]   [Oct 24, 2018]

Lung segmentation and disease classification in CT images. 12 Sigma Technologies Inc.

PI: Zhaozheng Yin
Amount: $44,215
Time Period: 04/1/2018 – 03/31/2019

Various lung diseases are causing millions of deaths worldwide. Detecting lung diseases at an early stage may dramatically lower the death rate. Screening individuals and applying computer aided diagnosis on the medical input, e.g. Computed Tomography (CT) images, enable the early stage detection of lung diseases. Supported by 12 Sigma Technologies Inc., Dr. Zhaozheng Yin’s group is developing medical image analysis methods for lung segmentation which is usually the first step for lung disease analysis systems. [read more] [Nov 07, 2018]