Engineering Commons^™

A Hands-On Lab On Agentic Scientific Discovery For Time Series With The Model Context Protocol, Dhaval Patel, Miaomiao Feng, Chathurangi Shyalika, Shuxin Lin, Hasnat Md. Abdullah

Publications

Agentic AI offers new opportunities to accelerate scientific discovery for time series by enabling autonomous systems to inspect data, formulate hypotheses, compose experiments, and refine models based on empirical evidence. However, building agents that make sound modeling decisions requires more than connecting a language model to forecasting tools. Key challenges include exposing large model catalogues without overwhelming the agent, grounding parameter choices in data characteristics, supporting iterative experimentation, and evaluating the quality of the reasoning behind each decision.

This hands-on lab introduces participants to agentic scientific discovery for time series using the Model Context Protocol (MCP). Through five interactive sprints, …

Context Matters: Evaluating Llm-Generated Knowledge Graph Schemas, Ritvik Garimella, Riju Marwah, Atishay Jain, Khusham Bansal, Amit Sheth

Publications

Knowledge graph (KG) schema engineering is labor-intensive and resists automation at scale. We investigate whether LLMs can generate domain-specific KG schemas of sufficient quality for downstream symbolic reasoning. We propose a tiered contextual framework that varies domain context richness across four levels: zero context, domain scope, task requirements, and data distribution. Generated schemas are evaluated intrinsically on BioRED (600 PubMed abstracts, multi-type entities and relations), where automated tiered schemas match an established KG construction baseline at 79.9% EC, with edge conformance rising from 47.5% at L1 to a stable 78–80% from L2 onward. Extrinsic evaluation on a 50-record MedHop controlled …

Doing Less With More: A First-Principles Exploration Of The Suitability Of Agentic Computing Over Alternative Architectural Choices, Ritvik Garimella, Biplav Srivastava, Amit Sheth

Publications

There is growing interest in automating business activities with Agentic Artificial Intelligence (AI) due to latter's seeming ease of use. Never has it been easier, or costlier, to do less with more. However, little is known about when agents are preferable to established alternatives such as local computation, Representational State Transfer (REST), the Simple Object Access Protocol (SOAP), and the Model Context Protocol (MCP), particularly when development speed, performance, and operational cost are considered. We investigate this question using a controlled mathematical task that compares seven methods on a benchmark of 1,000 arithmetic expressions where semantics of operator precedence has …

One Size Does Not Fit All: Revisiting World Models And Neurosymbolic Ai, Amit P. Sheth, Madhur Thareja, Anushka Pawar, Niyati Rawal

Publications

World models are being built twice, from opposite ends, without a shared theory of how the two halves should meet. One lineage grounds the world model in perception: a self-supervised, latent-predictive encoder – exemplified by Joint Embedding Predictive Architectures (JEPA) – that learns the structure of sensory experi-ence. A second, older lineage grounds the world model in cognition: an explicit, inspectable structure of entities, rules, and constraints, ranging from knowledge graphs to formal logic to physical law. Neither lineage alone has produced a world model that is simultane-ously adaptive and auditable. We argue this is not solved by picking a …

Automated Guided Wave Characterization Of Marcelling: In-Plane Fiber Waviness In Thermoplastic Composites, Sourav Banerjee, Ana Tudor, Corey Leydig, Thomas Ferguson, Tally Bovender, Darun Barazanchy, Josh Widosky, Paul Ziehl

Faculty Publications

Thermoplastic composite (TPC) materials often develop in-plane fiber waviness during manufacturing, a defect known as marcelling. Marcelling negatively impacts the strength and overall performance of the composite and are hard to control. While it may appear as a surface imperfection, it can also extend partially through the thickness of the material. Currently, there is no established nondestructive evaluation (NDE) method to reliably detect and quantify marcelling in composites after manufacturing. As marcelling is not a local defect but spans across the surface in in-plane dimensions of the structure, traditional pulse-echo and phased-array ultrasonic NDE are ineffective. Automated multi-directional guided …

Rgfc: Registry Grounded Function Calling With Stage-Decomposed Evaluation, Ritvik Garimella, Vedant Khandelwal, Anvi Kohli, Amit Sheth

Publications

Tool-calling evaluations often report whether the final call matches a reference answer, but this single outcome merges different failures: choosing a function outside the available registry, violating the selected schema, or filling arguments with values not supported by the user request. These failures matter more when models must choose from larger tool registries. We introduce RFCD, a registry-scaled function-calling benchmark built from BFCL, Glaive Function Calling v2, and Tool-Call-Data, with standardized JSON-schema registries ranging from 5 to 3,000 functions. We also introduce TAAG, a deterministic stage-decomposed evaluator for registry conformance, structural completeness, and argument grounding. Across six locally served sub-4B …

Understanding The Binding Of Nickel(Ii) Bromide To A Zirconium Metal-Organic Cage For Heterogeneous Catalysis, Luci Green

Caroliniana Undergraduate Research Journal

Heterogeneous catalysts offer inherent advantages in improving the sustainability of industrial chemical processes. Their high ease of separation and recyclability has the potential to reduce the cost, waste, and energy consumption of processes that currently rely on homogeneous catalysts. Metal–organic cages (MOCs) are an attractive material for heterogeneous catalysis due to their discrete and highly tunable structures, which can be functionalized by binding these complexes with catalytically active metals. The specific objective of this study is to provide preliminary insight into the binding of nickel(II) bromide (NiBr₂) to zirconium MOCs, which was done by studying the binding of …

Tunable Shock And Vibration Damping In Metal Laser Powder Bed Fusion Components Via Controlled Post-Print Compaction Of Particle Damper Cavities, Joud N. Stame, Yanzhou Fu, Samuel Roberts, Austin Downey, Tianyu Zhang, Lang Yuan, Daniel Kiracofe

Faculty Publications

Components manufactured by Laser Powder Bed Fusion (LPBF) can incorporate particle dampers (PDS) by retaining unfused powder within internal pockets, providing inherent vibration suppression without added mass or separate damping components. This study investigates the damping performance of such LPBF-integrated PDs through two complementary approaches: (1) quantifying the effect of post-printing volume compression on energy absorption by mechanically indenting the damper pocket, and (2) evaluating how variations in particle packing density influence the dynamic response under both sinusoidal and transient impulse excitation. Experimental results from shaker and shock testing demonstrate that increased packing density, whether by compression or tighter confinement, …

Fuel Flexibility, Gas Turbine Combustion, Laser Diagnostics, Lean Blow-Out, Austin Thomas Shealy

Theses and Dissertations

To address increasing global energy consumption, energy industries seek to implement alternative fuels and increase energy generation efficiencies to meet energy demands. However, alternative and bioderived fuels present both chemical and physical properties significantly different from traditional hydrocarbon fuels typically used for gas turbine power generation. Consequently, they present unforeseen technical challenges. To address these challenges, ammonia has been suggested for use as a hydrogen carrier fuel in gas turbines. Ammonia presents many challenges by having a low overall reactivity as well as low energy density both by mass and volume. To address ammonia’s unique combustion characteristics, two strategies have …

Quantum Spin-Like Behavior Of Elastic Guided Waves: Quantized Geometric Phase For Nde And Shm, Md Ebrahim Khalil Bhuiyan

Theses and Dissertations

Ultrasonic guided waves are widely used in nondestructive evaluation (NDE) and structural health monitoring (SHM) for detecting defects and damage in engineering materials and structures. Conventional diagnostic features, such as attenuation of wave amplitude, shifts in time-of-flight, and frequency variations are widely used for damage identification. However, these metrics are inherently sensitive to environmental and operational variations, including temperature, pressure, humidity, sensor coupling conditions, boundary constraints, and external loading. Such sensitivities introduce significant uncertainty in signal interpretation, often obscuring damage-related signatures. Recent advances in wave physics have revealed that elastic guided waves exhibit intrinsic spin angular momentum (SAM), arising from …

Device Engineering And Defect Characterization Of Wide-Bandgap Semiconductor Detectors, Ritwik Nag

Theses and Dissertations

This work investigates the device engineering and defect characterization of next-generation wide-bandgap semiconductor radiation detectors, specifically focusing on cadmium zinc telluride selenide (CdZnTeSe or CZTS) single crystal, 4H-silicon carbide (4H-SiC) epitaxial layer, and metal-oxide-semiconductor (MOS) 4H-SiC architectures. The research addresses the critical need for high-performance detectors capable of operating in harsh environments (i.e, in high radiation background and at high temperature) and at room temperature with superior energy resolution and low noise.

In the study of CZTS gamma-ray detectors, single crystals were grown using the vertical Bridgman method (VBM) and the vertical gradient freeze (VGF) method. Material characterization using EDX, …

Millimeter Wave Channel Modeling In Shipboard Environments, Patrick Morris

Theses and Dissertations

The millimeter wave (mmWave) band has been increasingly studied in recent years due to the saturation of other common frequency bands and the need for higher data rates for current and future communications systems. In this thesis, we quantify key channel parameters for mmWave channels in several environments, including an office setting, an indoor corridor, outdoor areas near a large building, and two shipboard environments. Using a center frequency of 30.5 GHz and signal bandwidth of 500 MHz, we quantify path loss and delay dispersion. The shipboard environments are a focus due to their novelty. Measurements were made using a …

An Examination Of Gendered Experiences In Undergraduate Engineering Education With Emphasis On Participation, Validation, And Persistence, Madison Lynn Burlett

Theses and Dissertations

This study investigates gendered experiences in undergraduate engineering education, with particular attention to academic validation, participation, and belonging. While prior research suggests disparities in retention and representation, these patterns are often examined through quantitative metrics such as grades, self-efficacy, or survey- based perceptions. Such approaches can provide limited insight into how students interpret and navigate engineering environments in practice. This study adds to the existing body of research by examining students’ lived academic experiences through qualitative analysis.

Semi-structured interviews were conducted with undergraduate engineering students across multiple disciplines and academic levels. Interviews elicited detailed accounts of how students approached academic …

Majority Vote Consensus Via Over-The-Air Computation, Davis Jianmin Mo

Theses and Dissertations

In this paper, we explore the reliability consensus algorithms using non-coherent Over-the-Air Computation (OAC) Majority Vote (MV) for multi-agent stochastic bias systems under ideal conditions with only sensor variance, and with both sensor noise and frequency selective fading channels. In this investigation, we will analyze the reliability data for the combination of these two concepts, such as observing computation error rate, and convergence.

Advancing Edge Ai Through Integrated Neuromorphic Algorithms And Hardware, Peyton Chandarana

Theses and Dissertations

As machine learning continues to expand across diverse sectors, including healthcare, transportation, environmental monitoring, and consumer electronics, many applications operate under strict constraints on latency, bandwidth, privacy, and power availability. Meeting these demands increasingly requires deploying machine learning at the edge to process data locally rather than relying on remote cloud infrastructure. These limitations highlight the importance of computational efficiency and motivate the exploration of alternative hardware and algorithmic paradigms capable of delivering real-time, accurate inference under tight resource constraints.

Two major paradigms have emerged for edge intelligence: conventional artificial neural networks (ANNs) deployed on highly parallel edge accelerators such …

The Effect Of Ionic Components On The Performance Of A Model Direct Seawater Electrolyzers, Fethi Mustafa Cimen

Theses and Dissertations

Direct seawater electrolysis (DSE) has gained increasing attention as a sustainable method for hydrogen production that does not require purified water resources. The ionic composition of seawater, especially the concentrations of sodium (Na+) and chloride (Cl-) ions, plays a critical role in determining the electrochemical behavior, membrane transport properties, and overall system performance. This thesis presents a numerical investigation of a model direct seawater electrolyzer with a focus on understanding how changes in seawater ionic strength influence device operation.

A three-dimensional multiphysics Computational Fluid Dynamics (CFD) model was developed in STAR-CCM+ to simultaneously solve heat and mass transport, charge transport, …

Simulating K-Distributed Sea Clutter For Improvement Of X- Band Radar Littoral Object Detection Performance, Hudson Richard Dye

Theses and Dissertations

This thesis provides a statistically based method of simulating K- distributed sea clutter for littoral X-band radar object detection. Littoral radar systems must detect small objects in cluttered environments dominated by return from the sea surface. Accurate modeling of sea clutter is fundamental for improving radar detection performance. The clutter synthesizer presented here is an effort to enable computer modeling of realistic sea clutter even with restricted knowledge of sea clutter behavior.

The K-distribution is widely used to model sea clutter in X-band radar systems. Its parameters 𝜈 and 𝑏 are closely tied to third and fourth statistical moments and …

Advanced Modeling Techniques And Crosstalk Mitigation Methodology For High-Speed Interconnects In Modern Communication Systems, Yingcong Zhang

Theses and Dissertations

The increasing demand for higher data rates, greater computational performance, and continued PCB miniaturization has rendered interconnects a critical bottleneck in modern communication and computing systems. High-density routing, while enabling compact designs, significantly exacerbates signal integrity (SI) challenges such as impedance mismatch, far-end crosstalk (FEXT), and propagation delay. Despite advances in design practices, efficient closed-form analytical models capable of accurately characterizing complex, high-frequency interconnect geometries remain limited, necessitating reliance on computationally intensive numerical simulations. This dissertation addresses these limitations by developing analytical modeling methodologies and proposing novel interconnect structures to ensure robust SI in high-speed PCB systems.

The first part …

Generalized Planning Using Language Models And Its Applications, Vishal Pallagani

Theses and Dissertations

Planning is a fundamental capability for intelligent systems, yet classical planners remain difcult to scale and generalize across domains due to their reliance on hand- engineered models, brittle search procedures, and limited adaptability. These limita- tions constrain real-world applications that require fexible reasoning, robust decision- making, and the ability to operate across diverse environments.

Large language models have emerged as powerful learners supported by their ar- chitectures and large-scale training data, demonstrating strong capabilities in tasks such as question answering, code generation, summarization, and reasoning. Moti- vated by their broad generalization abilities, I investigate whether language models can be systematically …

Development And Optimization Of Aluminum-Water Reactors For Efficient And Sustainable Hydrogen Production, Guru Prasad Dhungel

Theses and Dissertations

Hydrogen is a promising clean energy carrier; however, safe, controllable, and on- demand hydrogen generation remains a significant engineering challenge. Aluminum– water reactions offer high theoretical hydrogen yield, but practical implementation is limited by oxide passivation and difficulty in maintaining stable thermal and pressure conditions.

This study investigates hydrogen generation from aluminum foam–steam reactions under controlled temperature and pressure using engineered reactor configurations. Three reactor designs, horizontal, vertical, and cylindrical—were experimentally optimized, developed and evaluated to enhance hydrogen production rate and pressure stability without chemical additives. The experiments focused on heat transfer behavior, steam management, pressure buildup, and reaction stability. …

Strain-Induced Oxygen Vacancy Effect On Thermoelectric Properties In Oxide Thin Films, Ebenezer Seesi

Theses and Dissertations

The development of highly efficient and cost-effective thermoelectric (TE) materials is critical for converting waste heat into electricity in advanced power generators. For high-temperature TE applications, transition metal oxides (TMOs) have attracted significant interest because they are comparatively safe, earth-abundant, and stable under oxidizing conditions where many conventional heavy-metal and chalcogenide TE materials degrade. However, the TE performance of TMOs is limited by the intrinsic trade-off between the Seebeck coefficient (S) and electrical conductivity (σ), both of which depend inversely on charge carrier concentration. This constraint limits the achievable power factor (PF = S²∙σ) and remains a central obstacle for …

Plasma Dynamics And Chemistry In Atmospheric Pressure, Ayoub Al Wahaibi

Theses and Dissertations

Atmospheric-pressure plasmas are attractive for practical chemical and energy applications because they can be generated without vacuum systems and can produce reactive environments under relatively mild bulk conditions. However, these discharges are often non-uniform and highly sensitive to operating conditions, especially when water or multiphase flows are introduced. This dissertation investigates how atmospheric- pressure plasma behavior and plasma-driven chemistry change in three challenging but application-relevant regimes: water-mist dielectric barrier discharges (DBDs), multiphase plasma-assisted fuel reforming, and striated AC glow discharges. The work combines optical and electrical diagnostics, imaging, chemical analysis, and time-dependent analysis to connect discharge characteristics with measurable chemical …

A Pcb-Based Three-Level Active-Neutral-Point-Clamp Converter With Gan Devices, Mohammad Hassan Adeli

Theses and Dissertations

Modern microgrids, EV chargers, and renewable energy systems require power converters that are efficient, compact, and capable of high-frequency, high-power operation. However, in tightly packaged high-power designs, parasitic inductances and capacitances become significant, leading to voltage overshoot and elevated electromagnetic interference (EMI) under rapid dv/dt transitions. To address these challenges, this work presents a PCB-based 25kW, 480V, 200kHz grid-connected three-level active neutral-point-clamped (3L-ANPC) converter employing GaN devices. The PCB design emphasizes the optimization of parasitic inductances and capacitances to enhance high-voltage safety and reliability. A multilayer PCB busbar structure is implemented to minimize parasitic loops, enabling fast switching with minimal …

Deep Learning Models For Anomaly Detection In Industrial Inspection, Jiawei Guo

Theses and Dissertations

Anomaly detection plays a pivotal role in ensuring the longevity and safety of engineered systems, particularly in domains where defects can have significant operational or economic consequences. From internal damage in structural parts to subtle visual defects in manufactured components, identifying abnormal patterns requires models that are both computationally efficient and suitable for real-world deployment. This dissertation explores deep learning-based solutions designed to address these challenges, with a focus on Structural Health Monitoring (SHM) and industrial defect inspection under real-world scenarios.

This dissertation first addresses the problem of efficient and reliable component- level perception with limited supervision and strict hardware …

Laser-Based Wideband Guided Ultrasonic Wave Inspection Of Isotropic And Anisotropic Materials, Andrew Philip Campbell

Theses and Dissertations

Non-destructive evaluation (NDE) and structural health monitoring (SHM) have experienced significant growth in both capability and utility as material evaluation and damage detection methods for structural components throughout various industries over the past two decades. Evaluating a structure throughout its life cycle or on demand as needed can identify critical weaknesses that impact safe operation and may prevent failures. Various NDE/SHM methods have been shown to be effective; among them, guided ultrasonic waves (GUW), particularly Lamb wave-based methods, have been intensively studied due to their capability to interrogate internal defects and their relatively larger coverage. With the advancements in new …

Roadmap On Artificial Intelligence-Augmented Additive Manufacturing, Ali Zolfagharian, Liuchao Jin, Qi Ge, Wei-Hsin Liao, Andrés Díaz Lantada, Francisco Franco Martínez, Tianyu Zhang, Tao Liu, Charlie C.L. Wang, Mohammad Hossein Mosallanejad, Reza Ghanavati, Abdollah Saboori, Alejandro De Blas De Miguel, William Solórzano-Requejo, Yi Cai, Xiangyang Dong, Huangyi Qu, Najmeh Samadiani, Guangyan Huang, Austin Downey, Yanzhou Fu, Lang Yuan

Faculty Publications

Artificial intelligence-augmented additive manufacturing (AI2AM) represents a transformative frontier in digital fabrication, where artificial intelligence (AI) is embedded not as a peripheral tool, but as a central framework driving intelligent, adaptive, and autonomous additive manufacturing (AM) systems. The objective of this Roadmap is to present a comprehensive vision of the state-of-the-art developments in AI2AM while charting the future trajectory of this rapidly emerging field. As AM applications continue to expand across diverse sectors, conventional design and control strategies face growing limitations in scalability, quality assurance, and material complexity. AI uses tools like computer vision, generative design, and large language models …

AiXGa1−XN (0.7 < X < 1) Schottky Diodes Using Distributed Polarization Doped Layer With A Current Density Of 14 Ka/Cm2, Tariq Jamil, Abdullah Al Mamun Mazumder, Mafruda Rahman, Muhammad Ali, Grigory Simin, M. Asif Khan

Faculty Publications

High Al-content Al_xGa_1-xN (0.7 < x < 1) quasi-vertical Schottky barrier diodes (SBDs) with distributed polarization doping were grown on the bulk AlN substrate. They exhibit excellent rectification behavior with a large forward current density (~14 kA/cm² ) and a high breakdown field of ~8.3 MV/cm. The SBDs also exhibited low ideality factors of (n~1.2) with a high Schottky barrier height (Φ _b~ 1.7 eV). Thus, this study demonstrates the feasibility of the distributed polarization doping approach for high current–high voltage devices.

Mechanical Performance Of Equilateral Triangular Lattices: The Role Of Nodal Fillets, Fakhreddin Emami, Andrew J. Gross

Faculty Publications

Triangular lattices are widely employed for their high strength to weight ratios, yet their mechanical performance is sensitive to geometric features, particularly the nodal geometry. This study investigates the influence of nodal geometry on the mechanical behavior of equilateral triangular lattices across a broad range of relative densities using high fidelity finite element simulations. We characterize the elastic properties, and strength limits as functions of fillet radius. Our results confirm the expected trend that, in stretching-dominated lattices with low relative density, the introduction of fillets reduces both stiffness and buckling resistance. In contrast, at higher relative densities, filleted nodes can …

Roadmap: Integrating Artificial Intelligence In Structural Health Monitoring Systems, Simon Laflamme, Erik Blasch, Flippo Ubertini, Zheng Liu, John Wertz, Christine Knott, Matthew Cherry, Eric Lindgren, Fu-Kuo Chang, Amrita Kumar, Jack Poole, Keith Worden, Austin Downey, Jie Wei, Patrick F. Musgrave, Adrian S. Wong, Guiseppe Quaranta, Marco Martino Rosso, Giuseppe Carlo Marano, Yu Chen, Et. Al.

Faculty Publications

Advances in computing and machine learning (ML) methods have led to a rapid rise in artificial intelligence (AI) research and applications in many fields. AI research benefitted from advances in computation hardware, collection and distribution of large data sets, and proliferation of software techniques. AI techniques include ML for provable results, deep learning for data exploration, reinforcement learning for control, and active learning for adaptive systems. Likewise, AI algorithms can handle large amounts of data, construct unknown representations, and provide a direct link between data and classification for decision making. These unmatched capabilities have been seen as a path to …

Towards Sustainable Energy Storage: Evaluating The Performance Of Three Polymer Electrolytes For Zinc-Ion Batteries, Roya Rajabi, Shichen Sun, Buke Wu, Jamil A. Khan, Kevin Huang

Faculty Publications

Polymer electrolytes have been explored as an alternative to conventional aqueous electrolytes in zinc-ion batteries, particularly for flexible and wearable applications. Despite the increasing interest in polymer electrolyte-based zinc-ion batteries (ZIBs), their development is still in its early stages due to various challenges. In this study, we investigated three promising polymer electrolytes: CSAM (carboxyl methyl chitosan with acrylamide monomer), PAM (polyacrylamide monomer hydrogel electrolyte), and p-PBI (phosphate-doped polybenzimidazole solid electrolyte) with Zn(ClO4)2 and Zn(OTf)2, as electrolytes for zinc-ion batteries. The p-PBI solid electrolyte showed high mechanical stability and improved resistance to short-circuiting during cycling. The presence of carboxyl groups in …