Data Science
Data Science Blog
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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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A Simulation Characterizing Out-of-the-Box Stock Trading Behavior of Various LLMs
Dr. Sal Barbosa, from MTSU’s Department of Computer Science, discusses the behavior of LLM in the analysis of financial markets. The intersection of artificial intelligence and finance has led to a surge of interest as Large Language Models (LLMs) have come on the scene and promise to automate decision-making in many tasks, including stock trading.
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Faculty and Staff Spotlight – Emmanuel Nkansah, PhD
Dr. Nkansah joined MTSU in 2024. DATA classes taught: DATA 1500, DATA 2025, DATA 3500, DATA 6320, DATA 6300, DATA 6310, DATA 6330 Home Department: Department of Economics and Finance Research Interests: Econometrics (Time Series), Financial Machine Learning, Machine Labor (Joshua D. Angrist), Labor and Development Economics Why pursue data science: Data Science isn’t just
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