泛研网

公众版 查看说明
全球科研项目数据库2026版

REU Site:Advancing high-performance computing opportunities in undergraduate research at UW-Eau Claire to meet challenges of multidisciplinary computational science

项目来源

美国国家科学基金(NSF)

项目主持人

Rahul Gomes

项目受资助机构

UNIVERSITY OF WISCONSIN EAU CLAIRE

财政年度

2025,2024,2021

立项时间

未公开

项目编号

2447779

研究期限

未知 / 未知

项目级别

国家级

受资助金额

1315382.00美元

学科

未公开

学科代码

未公开

基金类别

Standard Grant

关键词

RSCH EXPER FOR UNDERGRAD SITES ; REU SITE-Res Exp for Ugrd Site

参与者

Ying Ma

参与机构

UNIVERSITY OF WISCONSIN SYSTEM

项目标书摘要：In recent years,computationally intensive scientific and engineering studies involving large-scale data processing have driven groundbreaking innovations across STEM disciplines.These studies require an interdisciplinary approach,combining domain-specific expertise with the development of scalable algorithms,including applications of Artificial Intelligence(AI)in data analysis and model optimization.The nine-week Research Experience for Undergraduates(REU)program at the University of Wisconsin-Eau Claire will continue to offer a comprehensive research experience in High-Performance Computing(HPC),aiming to inspire a diverse group of students to pursue careers in STEM fields related to multidisciplinary computational science and AI.The REU site will support ten undergraduate students each summer,engaging them in collaborative research focused on designing computationally intensive algorithms and simulation models using HPC.The program will expose students to advanced computing resources and hardware,providing hands-on experience that many undergraduate students do not typically encounter during their studies.This experience will also introduce students to the role of AI and machine learning in solving complex scientific problems by optimizing simulations and data-driven models.A central goal of the program is to attract students to STEM careers,especially in fields where computing and AI intersect with domain-specific challenges,and to train them in using advanced technologies.Furthermore,the REU site will promote national STEM workforce diversity goals by recruiting students from underrepresented groups,particularly from two-year colleges where research opportunities are limited.With its focus on HPC and its application in diverse scientific disciplines,the project will serve to fulfill several of the key goals of NSF:to advance discovery and learning,to promote inclusion in the science and engineering workforce,and provide an integrated strategy to advance the frontiers of knowledge.Through a focused research experience guided by dedicated mentors,students will have the opportunity to develop new algorithms utilizing HPC infrastructure to solve real-world scientific problems.They will learn to complete end-to-end research projects,including developing simulation and predictive models,analyzing results,preparing manuscripts for submission to journals and conference proceedings,and delivering technical presentations.Students will be able to choose from eight projects across four computational science domains:(1)Deep Learning and Computer Vision,(2)Bioinformatics,(3)Computational Materials Science,and(4)Computational Chemistry.As part of the REU program,students will also have opportunities to enhance their leadership and interpersonal skills through seminars on undergraduate research,diversity in STEM,scientific writing,ethics in computing,career planning,and preparing for graduate school.In addition to these developmental activities,students will participate in field trips to key research centers.These include a visit to Mayo Clinic Health System and the Center for High Throughput Computing at University of Wisconsin-Madison,and Chippewa Valley Technical College Regional Data Center among others where students can explore cutting-edge applications of computational science and HPC in medicine and large-scale data analysis.Moreover,students will present their research at the Wisys Spark Symposium,which highlights research from all 13 campuses in the Universities of Wisconsin System,providing them with a valuable platform to showcase their work to a broader scientific community.This experience,combined with invited guest speaker presentations and workshops,will further enhance students'communication,networking,and presentation skills.The success of the REU site will be evaluated through internal assessments as well as by utilizing the evaluation resources and services offered by the CRA Center for Evaluating the Research Pipeline(CERP).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.

人员信息

Rahul Gomes(Principal Investigator)：gomesr@uwec.edu；Ying Ma(Co-Principal Investigator)：yingma@uwec.edu；

机构信息

【University of Wisconsin-Eau Claire(Performance Institution)】StreetAddress：105 GARFIELD AVENUE,EAU CLAIRE,Wisconsin,United States/ZipCode：547014811；【UNIVERSITY OF WISCONSIN SYSTEM】StreetAddress：105 GARFIELD AVENUE,EAU CLAIRE,Wisconsin,United States/PhoneNumber：7158363405/ZipCode：54701；

项目主管部门

Directorate for Computer and Information Science and Engineering(CSE)-Office of Advanced Cyberinfrastructure(OAC)(OAC)

项目官员

Sharmistha Bagchi-Sen(Email：shabagch@nsf.gov；Phone：7032928104)

  • 排序方式：
  • 1
  • /

  • 1.Optimizing Mobile Vision Transformers for Land Cover Classification

    • 关键词：
    • vision transformers; MViT; ShuffleNet; CNN; land cover classification;DEEP LEARNING BENCHMARK; EUROSAT; DATASET
    • Rozario, Papia F.;Gadgil, Ravi;Lee, Junsu;Gomes, Rahul;Keller, Paige;Liu, Yiheng;Sipos, Gabriel;Mcdonnell, Grace;Impola, Westin;Rudolph, Joseph
    • 《APPLIED SCIENCES-BASEL》
    • 2024年
    • 14卷
    • 13期
    • 期刊

    Image classification in remote sensing and geographic information system (GIS) data containing various land cover classes is essential for efficient and sustainable land use estimation and other tasks like object detection, localization, and segmentation. Deep learning (DL) techniques have shown tremendous potential in the GIS domain. While convolutional neural networks (CNNs) have dominated image analysis, transformers have proven to be a unifying solution for several AI-based processing pipelines. Vision transformers (ViTs) can have comparable and, in some cases, better accuracy than a CNN. However, they suffer from a significant drawback associated with the excessive use of training parameters. Using trainable parameters generously can have multiple advantages ranging from addressing model scalability to explainability. This can have a significant impact on model deployment in edge devices with limited resources, such as drones. In this research, we explore, without using pre-trained weights, how the inherent structure of vision transformers behaves with custom modifications. To verify our proposed approach, these architectures are trained on multiple land cover datasets. Experiments reveal that a combination of lightweight convolutional layers, including ShuffleNet, along with depthwise separable convolutions and average pooling can reduce the trainable parameters by 17.85% and yet achieve higher accuracy than the base mobile vision transformer (MViT). It is also observed that utilizing a combination of convolution layers along with multi-headed self-attention layers in MViT variants provides better performance for capturing local and global features, unlike the standalone ViT architecture, which utilizes almost 95% more parameters than the proposed MViT variant.

    ...

  • 2.Polyethylene Glycol Impacts Conformation and Dynamics of Escherichia coli Prolyl-tRNA Synthetase Via Crowding and Confinement Effects

    • 关键词：
    • Amino acids;Atomic force microscopy;Catalysis;Catalyst activity;Conformations;Dynamics;Escherichia coli;Fluorescence spectroscopy;Functional polymers;Ions ;Molecular dynamics;Molecular weight;Molecules;Polyethylenes;Polymerization;Proteins;Biological research;Biologically inert;Confinement effects;Conformational dynamics;Crowding effects;Medical research;Nontoxic polymer;Prolyl-tRNA synthetase;Protein dynamics;Variable sizes
    • Liebau, Jessica;Laatsch, Bethany F.;Rusnak, Joshua;Gunderson, Keegan;Finke, Brianna;Bargender, Kassandra;Narkiewicz-Jodko, Alex;Weeks, Katelyn;Williams, Murphi T.;Shulgina, Irina;Musier-Forsyth, Karin;Bhattacharyya, Sudeep;Hati, Sanchita
    • 《Biochemistry》
    • 2024年
    • 期刊

    Polyethylene glycol (PEG) is a flexible, nontoxic polymer commonly used in biological and medical research, and it is generally regarded as biologically inert. PEG molecules of variable sizes are also used as crowding agents to mimic intracellular environments. A recent study with PEG crowders revealed decreased catalytic activity of Escherichia coli prolyl-tRNA synthetase (Ec ProRS), where the smaller molecular weight PEGs had the maximum impact. The molecular mechanism of the crowding effects of PEGs is not clearly understood. PEG may impact protein conformation and dynamics, thus its function. In the present study, the effects of PEG molecules of various molecular weights and concentrations on the conformation and dynamics of Ec ProRS were investigated using a combined experimental and computational approach including intrinsic tryptophan fluorescence spectroscopy, atomic force microscopy, and atomistic molecular dynamic simulations. Results of the present study suggest that lower molecular weight PEGs in the dilute regime have modest effects on the conformational dynamics of Ec ProRS but impact the catalytic function primarily via the excluded volume effect; they form large clusters blocking the active site pocket. In contrast, the larger molecular weight PEGs in dilute to semidilute regimes have a significant impact on the protein’s conformational dynamics; they wrap on the protein surface through noncovalent interactions. Thus, lower-molecular-weight PEG molecules impact protein dynamics and function via crowding effects, whereas larger PEGs induce confinement effects. These results have implications for the development of inhibitors for protein targets in a crowded cellular environment. © 2024 The Authors. Published by American Chemical Society.

    ...
  • 排序方式：
  • 1
  • /