Category: Zhou

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/.

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Weihua Zhou to Present Friday Seminar Talk

Weihua Zhou

The College of Computing (CC) will present a Friday Seminar Talk on November 15, at 3:00 p.m. in Rekhi 214. Featured this week is Weihua Zhou, assistant professor of Health Informatics and member of the ICC’s Center for Data Sciences. He will present his research titled: “Information retrieval and knowledge discovery from cardiovascular images to improve the treatment of heart failure.” Refreshments will be served.

Abstract: More than 5 million Americans live with heart failure, and the annual new incidence is about 670,000. Once diagnosed, around 50% of patients with heart failure will die within 5 years. Cardiac resynchronization therapy (CRT) is a standard treatment for heart failure. However, based on the current guidelines, 30-40% of patients who have CRT do not benefit from CRT. One of Zhou’s research projects is to improve CRT favorable response by information retrieval and knowledge discovery from clinical records and cardiovascular images. By applying statistical analysis, machine learning, and computer vision to his unique CRT patient database, Zhou has made a number of innovations to select appropriate patients and navigate the real-time surgery. His CRT software toolkit is being validated by 17 hospitals in a large prospective clinical trial.

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Zhou Feng is PI on $500K NSF Project

Zhuo Feng (ECE/ICC) is Principal Investigator on a project that has received a $500,000 research and development grant from the National Science Foundation. This potential three-year project is titled, “SHF: Small: Spectral Reduction of Large Graphs and Circuit Networks.”

This research project will investigate a truly-scalable yet unified spectral graph reduction approach that allows reducing large-scale, real-world directed and undirected graphs with guaranteed preservation of the original graph spectra. Unlike prior methods that are only suitable for handling specific types of graphs (e.g. undirected or strongly-connected graphs), this project uses a more universal approach and thus will allow for spectral reduction of a much wider range of real-world graphs that may involve billions of elements:

  • spectrally-reduced social (data) networks allow for more efficiently modeling, mining and analysis of large social (data) networks;
  • spectrally-reduced neural networks allow for more scalable model training and processing in emerging machine learning tasks;
  • spectrally-reduced web-graphs allow for much faster computations of personalized PageRank vectors;
  • spectrally-reduced integrated circuit networks will lead to more efficient partitioning, modeling, simulation, optimization and verification of large chip designs, etc.

From Tech Today, June 21, 2019

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