Control Theories For Machine Learning Algorithms Analysis And Design (Ct4ml): An Overview Of A Short Course, Yangquan Chen

2026 LSU Symposium on Control, Learning, and Intelligent Systems

No abstract provided.

Robustness Of Gradient-Based Optimization Under Perturbations: From Deterministic To Stochastic Noise, Leilei Cui

2026 LSU Symposium on Control, Learning, and Intelligent Systems

No abstract provided.

Maximal State-Control Invariant Sets For Linear Systems, Ahmad Amine Mr., Nick-Marios T. Kokolakis, Ugo Rosolia, Truong X. Nghiem, Rahul Mangharam

2026 LSU Symposium on Control, Learning, and Intelligent Systems

No abstract provided.

Fairness-Aware Management Of Electric Vehicle Charging Stations, Paulo Bessa Do Rego Monteiro, Ricardo Pinto De Castro

2026 LSU Symposium on Control, Learning, and Intelligent Systems

No abstract provided.

Advances In Sampling-Based Uncertainty Quantification For Safe Learning-Based Control, Abdullah Tokmak, Thomas B. Schön, Dominik Baumann

2026 LSU Symposium on Control, Learning, and Intelligent Systems

No abstract provided.

Blood Pressure Prediction During Hemorrhage And Blood Transfusion: A Population-Informed Sequential Inference Approach, Yi-Ming Kao, Parham Rezaei, Sina Masoumi Shahrbabak, Jeremy Alanano Pepino, Ian Sebastian Kirk Shogren, Yang Wang, Andrew Tomas Reisner, Jin-Oh Hahn

2026 LSU Symposium on Control, Learning, and Intelligent Systems

No abstract provided.

Identifiability, Observability, Parameter Reduction, And State Estimation For Nonsmooth Dynamical Systems, Hesham Abdelfattah, Peter Stechlinski, Sameh Eisa

2026 LSU Symposium on Control, Learning, and Intelligent Systems

No abstract provided.

Bilevel Convex Identification Of Autonomous Multi-Mode Switching Systems, Kaito Iwasaki

2026 LSU Symposium on Control, Learning, and Intelligent Systems

No abstract provided.

The General Polyak-Lojasiewicz Inequality And Its Connection To Neural Network Training, Arthur Castello B. De Oliveira, Eduardo Sontag

2026 LSU Symposium on Control, Learning, and Intelligent Systems

No abstract provided.

Towards Adaptive Driver Training: Online Estimation Of Dual-Task Performance Model Parameters, Aleksandra Dudek, Patrick Linford, Zihan Yu, Scott James, Matthew Castanier, Chris Vermillion, Kira Barton

2026 LSU Symposium on Control, Learning, and Intelligent Systems

No abstract provided.

System Identification For An Impedance-Controlled Exoskeleton, Mason D. Mathias, Chad G. Rose, Rhet O. Hailey

2026 LSU Symposium on Control, Learning, and Intelligent Systems

No abstract provided.

Rumor Propagation And Regulation: A Graphon Game Approach With Stackelberg Control, Huaning Liu, Gokce Dayanikli

2026 LSU Symposium on Control, Learning, and Intelligent Systems

No abstract provided.

Entropy-Aware Model Predictive Control For Transient Quality Mitigation In Metal Additive Manufacturing, Md Shafikul Islam, Mahathir Mohamamd Bappy, Saifur Rahman Tushar

2026 LSU Symposium on Control, Learning, and Intelligent Systems

No abstract provided.

Learning Hidden Control Laws For Gps Spoofing Via Unknown-Input Estimation And Reservoir Computing, Seif O. Elsabagh, Wenbin Wan

2026 LSU Symposium on Control, Learning, and Intelligent Systems

No abstract provided.

Multiagent Social Influence: Modeling Persuasion In Contested Social Networks, Renukanandan Tumu, Cristian Ioan Vasile, Victor Preciado, Rahul Mangharam

2026 LSU Symposium on Control, Learning, and Intelligent Systems

No abstract provided.

Gap Metrics For Model Fidelity Quantification In Dynamical Systems, Quintin Nelson, Manoranjan Majji

2026 LSU Symposium on Control, Learning, and Intelligent Systems

No abstract provided.

The Statistical Distribution Of U.S. Corn And Soybean Futures Prices, Chiamaka D. Osigwe

LSU Master's Theses

This thesis investigates the best-fit empirical distribution for daily U.S. corn and soybean futures price changes and examines how distributional choices affect Value-at-Risk (VaR) and Expected Shortfall (ES) estimates. The dataset comprises daily nearby futures prices for corn (11/16/1994–04/23/2025) and soybeans (10/16/1979–02/15/2023), yielding 7,626 and 10,853 observations, respectively. An ARCH-LM test confirms the presence of Autoregressive Conditional Heteroskedasticity (ARCH). Generalized Autoregressive Conditional Heteroskedasticity (GARCH) models were estimated, and to account for potential asymmetric effects, an EGARCH model was used. EGARCH (1,1) estimation reveals an inverse leverage effect, where positive price shocks generate higher volatility. Six heavy-tailed distributions were evaluated, and …

Evaluating Soil Health And Crop Yield In Louisiana Agricultural Systems: Impacts Of Best Management Practices And Prediction Models, Hector J. Mendoza Lagos

LSU Doctoral Dissertations

The adoption of conservation management practices is critical for improving soil health, enhancing nutrient use efficiency, and sustaining crop productivity in row crop systems in Louisiana. This study evaluated the role of conservation agronomic practices, soil biochemical indicators, and machine learning predictive models to improve soil nutrient dynamics, soil health indicators, microbial communities (MC), and crop productivity on a corn (Zea mays L.) research plot scale and in a commercial forty-hectare cotton (Gassypium hirsutum L.)-corn-soybean (Glycine max L.) rotation system in northeast Louisiana. The objectives of the study were to evaluate soil nutrient dynamics and MCs under …

Commissioning And Validation Of An Electron Treatment Planning System: Following Mppg.5.B Guidelines, Hailey N. Reaux

LSU Master's Theses

Purpose: The purpose of this work was to commission and validate the electron treatment planning system ElectronRT (eRT) (.decimal, Sanford, FL) for clinical use following the recommendations outlined in the American Association of Physicists in Medicine (AAPM) Medical Physics Practice Guideline (MPPG) 5.b (2022). The system’s ability to accurately calculate electron dose distributions was evaluated under normal and oblique beam incidence, varying field sizes and source-to-surface distances (SSDs), and irregular surface geometries.

Methods: The eRT beam model was configured using commissioning data from an Elekta Infinity linear accelerator at Mary Bird Perkins Cancer Center (MBPCC). Validation measurements were performed for …

Distributed File Carving, Brad J. Baudin

LSU Master's Theses

File carving is a fundamental technique in the digital forensics community, enabling analysts to recover deleted files from disk images without relying on filesystem metadata; however, as storage capacities continue to increase, modern file carving tools face significant scalability challenges, with carving time growing substantially alongside disk image size, particularly in the presence of file fragmentation. Fragmentation, a common behavior in modern filesystems, distributes file data across non-contiguous disk blocks to maximize space utilization, and in large disk images this distribution can span wide logical distances, increasing the search space and reducing carving efficiency, causing traditional approaches to struggle within …

Building The Foundation For Stem In Toddler Classrooms: Practical Strategies For Novice Teachers, Melissa L. Johnson

Spark! Innovations in Early Childhood Education: A Research-to-Practice Journal for Educators

Young children are natural explorers, and even in the toddler years, they demonstrate the inquiry behaviors foundational to later science, technology, engineering, and mathematics (STEM) learning. However, early STEM experiences are often underutilized in toddler classrooms, primarily due to misconceptions about children's capabilities and a lack of developmentally appropriate resources. This article, written for novice educators, offers practical strategies and examples to help integrate STEM into toddler classrooms. Drawing on current research, national standards, and classroom-based implementation strategies, this piece outlines how to create engaging environments that support toddlers' curiosity and emerging STEM thinking across science, technology, engineering and mathematics.

Momentum Space Algorithm For Electronic Structure Of Double-Incommensurate Trilayer Graphene, Kenneth Silver Beard

LSU Doctoral Dissertations

Numerical algorithms for computing the electronic structure of incommensurate 2D-materials using ab initio models are critical for predicting material properties and guiding experiments. For bilayers, momentum space and continuum models have been introduced to approximate observables of ab initio tight-binding models using a momentum description, despite the lack of periodicity in the tight-binding model required for Bloch theory. A similar structure has been introduced for double-incommensurate trilayers using a continuum model, where the three lattices are mutually incommensurate. However, this description leads to a four-dimensional lattice space, and numerical convergence of the density of states has been observed to be …

Greedy Algorithms And Matroids, Kiri M. Strack

LSU Master's Theses

In a connected graph with weights on the edges, a minimum-weight spanning tree can be obtained by repeatedly choosing minimum-weight edges while avoiding choosing the edge set of any cycle. This algorithm is known as Kruskal’s Algorithm, although it was first introduced by Boruvka in 1926. Prim introduced an alternative algorithm in which, at each step, the chosen set of edges forms a connected graph. Both of these algorithms make locally optimal choices that eventually yield a global optimum. This thesis considers how these algorithms can be extended to matroids. In particular, it is shown that matroids are exactly the …

Automated Analysis Of Radiation Oncology Incident Reports Using Large Language Models, Nathan A. Dobranski

LSU Master's Theses

Patient safety incident reporting in radiation oncology requires expert analysis that is time-intensive and subject to variability. This thesis presents the development and technical validation of a locally deployed large language model (LLM) system for automated incident report analysis across multiple cancer centers. The system was designed for automated summarization and taxonomy assignment of Radiation Oncology Incident Learning System (RO-ILS) reports, operating entirely on local infrastructure to preserve patient privacy. A two-round, multi-rater evaluation methodology was employed, incorporating 600 total expert evaluations from two academic cancer centers. Round 1 established baseline performance using Mistral 7B and Mixtral 8x7B models with …

Recent Wetland Elevation Dynamics In Coastal Louisiana, Usa, Elizabeth Harris

LSU Master's Theses

Coastal marshes globally are increasingly vulnerable to accelerating relative sea-level rise (RSLR), which threatens their capacity to maintain elevation through feedbacks among sediment supply, vegetation productivity, hydrology, and soil processes. Although many marshes can persist under moderate rates of sea-level rise through vertical accretion and belowground biomass production, this resilience is strongly constrained by sediment availability and subsurface processes such as autocompaction and organic matter decomposition. High accretion is often assumed to confer marsh resilience; however, subsurface processes, particularly autocompaction driven by surface loading, can substantially offset elevation gains. Coastal Louisiana experiences among the highest rates of RSLR worldwide due …

The Effects Of Dietary Protein Manipulation On Operant Demand, Brianna R. Lilly

LSU Master's Theses

Previous work with dietary protein restriction in rodents has demonstrated that rodents fed a low protein diet exhibit physiological effects such as lower rates of weight gain and increased levels of circulating fibroblast growth factor 21 (FGF21). Dietary protein restriction also produces behavioral effects. Compared to rodents that are not protein-restricted, rodents that are protein-restricted exhibit increased food intake and higher preference for a protein vs. carbohydrate solution. Additionally, protein-restricted rodents will respond at higher levels for a protein reinforcer than non-restricted rodents. The current study assessed the effect of dietary protein restriction on operant protein demand during and after …

Exploring Perspectives Of Autistic Individuals And Caregivers On Autism Inclusive Services In Public Libraries, Ayva J. Rose

LSU Master's Theses

Public libraries are increasingly recognized as important community hubs designed to promote literacy, learning, language, family engagement, and inclusivity. Despite this, autistic individuals may experience language, literacy, sensory, and social differences that could impact their ability to participate in literacy-based programming in libraries. Librarians currently report a desire to provide services that not only include but meaningfully support autistic children in their families; however, little research has examined how key stakeholders within the autism community perceive autism-inclusive programming within libraries. This research seeks to explore these perspectives and identify (1) perceived barriers to attending children’s library programs, (2) accommodations and …

Movable Bed Physical Model Investigation Of Bed Level Changes Caused By River Sediment Diversions, Hayden Cole Franklin

LSU Master's Theses

Since 1932, due to human and natural processes, over 2,000 square miles of Louisiana’s coast have been lost. River sediment diversions have been proposed as sustainable options to combat land loss. These projects, like the proposed Mid-Barataria Sediment Diversion, are designed to deliver sediment-rich Mississippi River water into nearby bays and estuaries, helping build and maintain land. However, river sediment diversions may alter river hydraulics and sediment transport, potentially inducing upstream degradation and downstream aggradation. Using the Lower Mississippi River Physical Model (LMRPM), this study quantitatively analyzed bed level changes as well as hydraulic conditions associated with the proposed Mid-Barataria …

Machine Learning Based Models For Simulation And Analysis Of Bulk Earth Melt System, Abin Shakya

LSU Doctoral Dissertations

Understanding the segregation of bulk Earth melt systems into metallic (core) and silicate (mantle) phases under high-pressure and high-temperature conditions is central to modeling Earth’s interior, yet relevant experimental and computational studies remain limited. This work develops a machine learning–based simulation pipeline that iteratively couples first-principles (quantum mechanical) calculations with neural network training to generate high-fidelity force fields. Using major-element Fe–Mg–Si–O melt systems, with and without H and N, as testbeds, we demonstrate that this framework enables large-scale molecular dynamics simulations at near first-principles accuracy. We further introduce a sequence of phase identification methods, progressing from statistical binning of elemental …

Extremal Connectivity In Graphs And Matroids, Yiwei Ge

LSU Doctoral Dissertations

Connectivity is a central theme in both graph theory and matroid theory. This dissertation investigates extremal connectivity in graphs and matroids, with emphasis on unavoidable structures and minimal connectivity phenomena.

Chapter 2 introduces cycle-contraction minors of graphs and investigates their structural properties. We establish a connection between cc-minors and induced subgraphs via graph duality. The main result gives an unavoidable-families characterization for cc-minors of sufficiently large loopless $2$-connected graphs.

Chapter 3 studies super-minimally $3$-connected graphs, namely $3$-connected graphs that have no proper $3$-connected subgraphs. We establish extremal bounds on structural parameters of these graphs, including the minimum number of degree-$3$ …