Data Science
Data Science Blog
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Addressing Computational Challenges in Geospatial Applications using Graph Structures
Computational and Data Science Ph.D. student Abigail Kelly presents elements from her internship and research. Her research focuses on Geospatial Data and Graph Theory – specifically, how to extract patterns from this difficult type of data. Geospatial data science focuses on extracting patterns and knowledge from data containing a spatial or geographic component. This discipline is
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Faculty and Staff Spotlight – Lisa Eddy
Lisa Eddy is one of the advisors for the Data Science Program. Lisa advises students whose last names begin with A-K. What is your degree? B.S. in Liberal Studies with an emphasis in Administration and Education Advice for Students Don’t limit yourself by going it alone! Think of campus like a Data Science project. Gather information from
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Predictive Maintenance for heavy equipment with a classical and Quantum Support Vector Machine using novel data
Computational and Data Science Ph.D. student, Laurel Koenig, presents elements from their internship and research, Predictive Maintenance for heavy equipment with a classical and Quantum Support Vector Machine using novel data. This work shows the differences between quantum and classical machine learning on a real-world dataset Predictive maintenance is the blanket term for methods used
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Predictive and Representation Learning for fMRI-Based Brain Disorder Analysis
Computational and Data Science Ph.D. student, Haoyuan Wang, presents Predictive and Representational Learning for fMRI-Based Brain Disorder Analysis. Neural network–based predictive and representation learning approaches are developed to improve model reliability and representational capacity for complex fMRI data. The capability of neural networks to capture high-dimensional, nonlinear structures is first examined, and a custom loss
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Parallel Algorithms for Open-Locating Dominating Sets
Computational and Data Science Ph.D. student, Robert Dohner, presents an overview of algorithms for computing open-locating-dominating (OLD) sets, a graph-based framework for fault detection and sensor placement in networks. This seminar introduces Infrastructure as Code (IaC) and discusses its relevance to data science research. Using Terraform as an example, the talk demonstrates how computing environments and machine
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Faculty and Staff Spotlight – Dr. Ramchandra Rimal
Dr. Rimal is the advisor for the Graduate Certificate in Data Science and an Assistant Professor in Mathematical Sciences. Year joined MTSU: 2020 Data Science Classes Taught DATA 3550 Applied Predictive Modeling DATA 6990 Topics Seminar in Data Science Data Science Involvement at MTSU Developing data science courses, mentoring students, and participating/organizing events on machine learning and
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Infrastructure as Code for Data Science: Reproducible Machine Learning Workflows with Terraform
Computational and Data Science Ph.D student, Dongyu Liu, presents an overview of the open source Terraform framework. This system is a fascinating approach toward machine learning workflows, deployment, and provisioning of ML systems on both local and cloud resources. This seminar introduces Infrastructure as Code (IaC) and discusses its relevance to data science research. Using Terraform as an example,
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Modeling the impact of host heterogeneity on the risk and dynamics of a West Nile virus epidemic
Paul Klockenkemper, a Computational and Data Science doctoral student at MTSU, presents an update of his research on modeling host heterogeneity in the West Nile virus West Nile Virus (WNV) is a mosquito-borne arbovirus with significant ecological and public health implications. Its transmission cycle involves avian hosts and mosquito vectors. Many factors, including host diversity
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Quantum Computing: Exploration and Application
Yousaf Khaliq, a Computational and Data Science doctoral student at MTSU, presents an overview of his research and dissertation work in preparation for his final doctoral defense in the CDS program. This works presents a modular approach to using near-term quantum computing inside hybrid quantum–classical workflows that remain anchored to classical correctness guarantees and practical
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Faculty and Staff Spotlight – Dr. Sara Shirley
Dr. Shirley is the director of the Data Science Bachelor’s degree and an Associate Professor in the Department of Economics and Finance. Year joined MTSU: 2017 Data Science Involvement at MTSU In addition to directing the BS in Data Science program, Dr. Shirley serves as the advisor of the Data Science Club. The club is
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