Category Archives: Funding

Weihua Zhou is PI on $25K R and D Grant from Tulane University

Weihua Zhou

Weihua Zhou, assistant professor, Health Informatics, and member of the ICC’s Center for Data Sciences, is the principal investigator on a project that has received a $24,497 federal pass-through research and development grant from Tulane University. The project is titled, “Trans-Omics Integration of Multi-Omics Studies for Male Osteoporosis.” This is a 7-1/2 month project.

Abstract: Osteoporosis is the most prevalent metabolic bone disease and it is representative of many diseases typical of aging. While advances in omics technologies,  such as genomics, transcriptomics, proteomics, and epigenomics, have been successful in identifying risk loci for osteoporosis, each technology individually cannot capture the entire biological complexity of osteoporosis. The integration of multiple technologies has emerged as an approach to provide a more comprehensive view of biology and disease. In addition, recent advances in image analysis have enabled the characterization of not only the bone mineral density but also the bone microarchitecture and biomechanical quality with the dual-energy x-ray absorptiometry (DEXA) and quantitative computed tomography (QCT) measurements. The Tulane Center for Bioinformatics and Genomics (CBG), led by Dr. Hong-Wen Deng, has accumulated/is acquiring extensive multi-omics data and DEXA/QCT images through a number of research projects for osteoporosis and other related phenotypes. Tulane CBG is actively seeking collaborations with investigators who have the expertise and experience in integrative multi-omics analysis and advanced image analysis. With this NIH subcontract award (U19AG055373), Tulane CBG will collaborate with Dr. Weihua Zhou and his team on the development and implementation of sophisticated methods for multi-omics analysis and DEXA/QCT image analysis.
Dr. Zhou is looking for volunteer research assistants. Please visit his web pages for more details: https://pages.mtu.edu/~whzhou/, and read this blog post: https://blogs.mtu.edu/computing/2019/12/03/medical-imaging-…earch-assistants/.

Jinshan Tang Receives Research Excellence Fund Award

Jinshan Tang

The Vice President for Research Office recently announced the Fall 2019 Research Excellence Fund (REF) awards. The awardees included College of Computing Professor Jinshan Tang, a member of the ICC’s Center for Cyber-Physical Systems, who was awarded a Portage Health Foundation (PHF) Infrastructure Enhancement (IE) Grants for his proposal, “High Performance Graphics Processing Units.”

The REF Infrastructure Enhancement (REF-IE) grants are designed to provide resources to develop the infrastructure necessary to support sponsored research and graduate student education. Funded projects typically focus on acquisition of equipment, enhancement of laboratory facilities, or enhancement of administrative support structure to expand the research capability of the unit.

For additional information about the Research Excellence Funds, visit the REF website.


Chee-Wooi Ten is PI of R and D Agreement with University of California Riverside

Chee-Wooi Ten

Chee-Wooi Ten (ECE), a member of Michigan Tech’s Center for Agile and Interconnected Microgrids and the ICC’s Center for Cyber-Physical Systems, is the principal investigator on a 17-month project that has received a $99,732 research and development cooperative agreement with the University of California Riverside. The project is entitled, “Discovery of Signatures, Anomalies, and Precursors in Synchrophasor Data with Matrix Profile and Deep Recurrent Neural Networks.”


Soner Onder Receives Year One Funding for $1.2M NSF SCALE Project

Soner Onder
Dave Whalley

Soner Onder, professor of computer science, was recently awarded $246,329 for the first year of a four-year NSF grant for his project, “SHF: Medium: Collaborative Research: Statically Controlled Asynchronous Lane Execution (SCALE).” The project is in collaboration with Prof. David Whalley of Florida State University. Michigan Tech is the lead institution in the project, it is expected to total $1.2 million, with Michigan Tech receiving $600,000.

Abstract: Enabling better performing systems benefits applications that span those running on mobile devices to large data applications running on data centers. The efficiency of most applications is still primarily affected by single thread performance. Instruction-level parallelism (ILP) speeds up programs by executing instructions of the program in parallel, with ‘superscalar’ processors achieving maximum performance. At the same time, energy efficiency is a key criteria to keep in mind as such speedup happens, with these two being conflicting criteria in system design. This project develops a Statically Controlled Asynchronous Lane Execution (SCALE) approach that has the potential to meet or exceed the performance of a traditional superscalar processor while approaching the energy efficiency of a very long instruction word (VLIW) processor. As implied by its name, the SCALE approach has the ability to scale to different types and levels of parallelism. The toolset and designs developed in this project will be available as open-source and will also have an impact on both education and research. The SCALE architectural and compiler techniques will be included in undergraduate and graduate curricula.

The SCALE approach supports separate asynchronous execution lanes where dependencies between instructions in different lanes are statically identified by the compiler to provide inter-lane synchronization. Providing distinct lanes of instructions allows the compiler to generate code for different modes of execution to adapt to the type of parallelism that is available at each point within an application. These execution modes include explicit packaging of parallel instructions, parallel and pipelined execution of loop iterations, single program multiple data (SPMD) execution, and independent multi-threading.

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.

https://www.nsf.gov/awardsearch/showAward?AWD_ID=1901005&HistoricalAwards=false