Engineering Research and Innovation Seed Funding Program

To encourage new research collaborations, the College of Engineering associate dean for research has initiated the Engineering Research and Innovation Seed Funding program (ERISF). The overarching goal of the ERISF program is to assist engineering researchers in developing new innovative research programs that have strong potential for significant future support from government agencies, corporations, industry, consortia, or foundations. Proposals must develop research initiatives with high potential for significant extramural funding.

Read the 2019 RFP (Word file)
Download the 2019 ERISF Budget Template (Excel File)

CY2019 ERISF Projects

Principal Investigator: Alexander Nelson, Computer Science and Computer Engineering

Collaborator: Yue Chen, Mechanical Engineering


This pilot work is an exploratory project to combine fabric capacitor sensor arrays into the construction of soft robotic actuators to improve functionality. The inclusion of these sensors with additional kinematic modeling enable mechanical analogs to biological somatosensation, proprioception, and hand-eye coordination – all key features in hand motor coordination. The goal of the project is to produce a working prototype to produce initial data that will enable a competitive collaborative proposal when seeking external funding. The long-term goal is to produce a soft hand exoskeleton that aids in hand motor rehabilitation – enabling repeated proper motions that aid in neurological recovery during plasticity post trauma. Supporting proper motor function prevents maladaptive compensatory behaviors that significantly alter motor function.

Principal Investigator: Xiao Liu, Industrial Engineering

Collaborator: Miaoqing Huang, Computer Science and Computer Engineering


This project addresses a rapidly emerging area known as the Data Intensive Physical Analytics (DIPA), which is a direct response to COE’s Big Data/Analytics research area and others. The objective of this project is to create a physical-statistical modeling framework and GPU-accelerated computing tools for the real-time monitoring and prediction of large-scale spatio-temporal data sets arising from physical convection-diffusion processes. The applications/impacts of the research range from the modeling, monitoring and prediction of the regional outbreaks and epidemics of Influenza A (H3N2), California wildfires, major storms of the Atlantic season such as Hurricane Florence, fine Aerosol pollution over central California, and so on. Due to the significance and wide applicability of the proposed research, the PIs have successfully identified a number of external funding opportunities, especially NSF, DOE and Walmart. In addition, leveraging PI Liu’s industry connection, the team will engage with IBM Research on an NSF GOALI project.

FY2019 ERISF Projects

Principal Investigator: David Huitink, Mechanical Engineering

Collaborator: Lauren Greenlee, Chemical Engineering


Induction heating of nanoparticles offers novel avenues toward localized heating in unique environments such as cancer tumor ablation and microfluidic chemical processes where traditional heating methods are obtrusive, yet there is still much to be understood in this frontier technology. Chemical interactions between heated NPs and their surroundings can affect their performance and stability; and in this effort, a brand new technique of using silica capped iron oxide NPs is investigated for enhanced stability during induction heating. The Huitink group will study the heat output of various iron oxide core NPs with silica shells that will be produced by the Greenlee group, for enhancing the stability and thereby enabling new technological capabilities using NP induction heating. Moreover, the chemical stability of these silica-capped magnetic NPs will also be investigated using TEM and spectroscopic analysis, for determining evolution of the NPs and their shell structures caused by the induction process.

Principal Investigator: Miaoqing Huang, Mechanical Engineering

Collaborator: Zhenghui Sha, Mechanical Engineering


The primary research objective of this interdisciplinary research is to significantly increase the scale of network modeling and analysis using GPU platform in supporting complex systems design. To achieve this objective, we will design new parallel and scalable methodologies for manipulating large-scale graphs (of millions of nodes) on GPU computer clusters in order to accomplish the network analysis in minutes. The central hypothesis is that the network models developed with large-scale network data set have a better performance than those obtained from studies at small scales. To validate the hypothesis, we propose to conduct a comparative study on modeling the U.S domestic transportation system using small-scale and large-scale network analyses, respectively. The proposed research is potentially transformative and, if successful, could fundamentally change the landscape of complex network. This project will be carried out by a joint force between Huang (PI), with expertise in high-performance computing and algorithm design, and Sha (Co-PI), with expertise in complex networks and engineering design.

FY2018 ERISF Projects

Principal Investigator: Zhong Chen, Electrical Engineering

Collaborators: Yanbin Li and Ronghui Wang, Biological and Agricultural Engineering


Huge medical costs and tremendous productivity losses every year in the world are associated with major foodborne pathogens (such as E. coli O157:H7, Salmonella Typhimurium, Listeria monocytogenes). The goal of this research is to investigate and develop optofluidic biosensor based on integrated photonics and microfluidic devices for simultaneous detection of multiple foodborne pathogenic bacteria. Optofluidic biosensor provides unique integration capability of both liquid fluidic handling and optical spectrum analysis into one chip. It can substantially improve the sensitivity of the bacteria detection and reduce the total size and weight of bio-sensing system. By utilizing the immunomagnetic nanobead for bacteria separation and fluorescent semiconductor quantum dots for simultaneous detection of multiple bacteria, optofluidic biosensor can be developed for highly sensitive portable bio-sensing system for foodborne pathogenic bacteria detection.

Principal Investigator: Sarah Nurre, Industrial Engineering

Collaborators: Kelly Sullivan, Industrial Engineering and Benjamin Runkle, Biological Engineering


Food and agriculture (ag) is a critical infrastructure making up almost 20% of the economic Activity in the US. In spite of its importance, researchers have overlooked food and ag when Classifying infrastructure interdependencies. We propose to fill this need by identifying and Classifying the interdependencies of the food and ag sector within the state of Arkansas (AR) with the aim of strengthening its resilience. Ag is the top industry in AR wherein rice and poultry comprise two of the top commodities. Using rice and poultry as case studies, we will interview key personnel in the food and ag industry such as farmers, packaging managers, researchers, and members of the AR Division of Ag. Complementing this effort, we will interview managers of interdependent infrastructures (e.g., water). We will augment the interview data with data from GIS and literature to classify and create visualizations for the types and characteristics of interdependencies within food and ag in AR.

Principal Investigator: Zhenghui Sha, Mechanical Engineering

Collaborators: Michael Gashler, Computer Science and Computer Engineering


In the last decade, improvements to AI have enhanced many services and products. However, domains still exist where modern AI is not yet being utilized. One important example is engineering design. Integrating AI with engineering design has potential to make a large impact by simplifying and accelerating engineering efforts. The first step toward making this happen is to enable machines to learn design thinking from humans who already know how to do it well. The primary objective of this interdisciplinary research is to characterize, mine and model engineering design thinking, specifically designers' divergent-convergent reasoning, system thinking and sequential decision-making, in complex systems design. To achieve this objective, we apply big data analytics in design research with a smart computer-aided design (CAD) platform – Energy3D. The central hypothesis is that unobserved design thinking can be mined from the fine-grained data of CAD logs. To validate this hypothesis, we propose to conduct human-subject experiments, and integrate data mining techniques with Markov models. The accomplishment of this objective will establish the utility of big data methods in design research and lay the foundation for our future study towards realizing AI-assisted engineering design.