Artificial intelligence is not only revolutionizing the workplace; it challenges how society understands creativity, authorship, and human values. For countless creative professionals, AI represents both possibility and unease. It is a tool that can expand imagination, but also raises questions about labor, ethics, ownership, and the role of human expertise. In industrial design, LLM-enabled tools that rapidly generate concepts, renderings, mood boards, and presentations create tension. Yet the ability to produce convincing visual options does not necessarily mean that AI understands the human, technical, emotional, and contextual requirements of design. This project investigates whether LLM-enabled design tools produce better solutions or merely generate faster and more eye-catching ideas that still depend on human direction. Industrial design requires more than generation; it requires judgment. Designers must define problems, understand users, interpret context, evaluate constraints, and justify solutions for specific tasks, tools, environments, and experiences. Using an empirical methodology, this research compares traditional and AI-supported industrial design processes through documented workflows, design output analysis, and justification criteria related to ideation, speed, refinement, authorship, and user-task-environment fit. The study also examines the difference between how LLMs operate and how creative people think: probabilistic pattern recombination versus intuition, emotion, contradiction, lived experience, and human need. I hypothesize AI will be most valuable when used as a creativity engine, consultant, or co-creator that reduces repetitive labor, offers an expanded array of possibilities, and helps designers reach creative development faster, while the designer maintains responsibility for critique and final decision-making in the creative problem-solving process.

"Workflow scheduling aims to assign dependent tasks to processors while minimizing total execution time, or makespan. In distributed computing environments, communication delays between processors can become a major bottleneck. Task duplication is a technique that reduces communication delays by executing copies of selected tasks on multiple processors, allowing successor tasks to access data locally. While duplication has the potential to improve performance, duplicating ineffective tasks may increase computation costs and ultimately worsen makespan. As a result, identifying which tasks should be duplicated remains an important challenge.  This research investigates task duplication candidate selection within the Scheduling Algorithms Gathered (SAGA) framework. A feature-based approach was developed to rank tasks according to their expected duplication benefit. Candidate scores were computed using tasks features such as branching factor, communication ratios, descendant impact, and join importance. These feature scores were combined into a single ranking score, which was then used to identify strong and weak duplication candidates. The highest and lowest ranked tasks were evaluated using the HEFT and CPoP scheduling algorithms on both randomly generated task graphs and scientific workflows. Results show that the top-ranked candidates consistently outperform bottom-ranked candidates on random task graphs, suggesting that the selected features capture meaningful duplication signals. However, improvements were less consistent on scientific workflows, indicating that additional workflow characteristics may influence successful duplication. Future work will investigate additional heuristics and workflow structures, implement simulated annealing to use our scoring to identify effective combinations of duplication candidates, and extend the implementation to all available schedulers in SAGA."

The project Web-Ta is a web application that is designed for automated code critique. This web program is developed at Michigan Technological university under the supervision of Dr. Leo C Ureel. The web application is designed to uphold introductory programming standards, by offering students with structured feedback on their submitted code assignments, compilation errors, style issues, programming antipatterns, and test results. Web-Ta is designed to make learning code simpler, encouraging and better through continuous feedback. The purpose of my research in this application is to work on part of the front-end of Web-Ta. Create several components including course selectors that would let the user interface to select their chosen course, primarily will be used by the instructors. Creating a setting button that would let the UI bring changes to their profile portfolio. Also developing a drag and drop files upload that is hoverable that would be used to identify the student portfolio picture and potentially file uploads, and money more updates to the rest of the Web-Ta pages that were already created by other grad students. My job will be to add more details to those pages based on the information given to me by my mentor. My research will allow the Web-Ta user interface to interact with the user input and output. This study reviews how Web-Ta will be able to automated testing and analysis statistics of new programmers based on the feedback and inputs it receives from UI, which reduces the delay between submissions and critique. 

Technologies involving lipid nanoparticles (LNPs) containing mRNA have seen an exponential increase in modern drug delivery research and literature. Several key characteristics of mRNA-LNPs are especially attractive for those investigating inhalable gene therapy options for the treatment of genetic lung diseases. We set out to optimize the formulation of LNPs intended for aerosolization by focusing on the biological buffer solution selected for dialysis. Recent data suggest that there are more favorable, alternative options for PBS when dialyzing LNPs immediately following their formulation. The buffer solutions used in this study were carefully selected from literature while also remaining cognizant of potential clinical use in patients. The dialysis buffer candidates used in this study are the following: HEPES, Tris, PBS, Glycine, and Carbonic Acid/Carbonate. The design-of-experiment (DOE) approach was adopted for this study to comprehensively evaluate the efficacy of mRNA-LNPs after dialysis. Physiological and morphological changes present in mRNA-LNPs associated with a buffer candidate were evaluated with characterization techniques such as dynamic-light-scattering (DLS), encapsulation efficiency assays, and zeta potential measurement. We utilized in vitro models to measure mRNA transfection in human lung cells at air-liquid interface.

This project uses molten-salt recycling as an environmentally friendly cathode recovery in lithium-ion batteries (LIBs). These batteries are composed of critical raw materials and face increasing demand due to their versatility and high energy efficiency. As consumption expands, so does the accumulation of spent batteries and the need for sustainable recycling strategies. Current techniques like pyrometallurgy and hydrometallurgy are energy-intensive, expensive, and produce secondary pollution. In contrast, molten salt recycling uses lower temperatures, avoids toxic solvents, and enables direct upcycling of polycrystalline low-Ni NMC into single-crystal Ni-rich NMC. A cathode obtained from a failed power-tool battery was recycled using molten salts. The morphology of the material was determined using scanning electron microscopy (SEM), its composition by energy-dispersive X-ray spectroscopy (EDS), and analysis of its crystallographic structure using powder X-ray diffraction (XRD). The recycled cathode was assembled in coin half-cells to examine its electrochemical performance using electron impedance spectroscopy (EIS). The results aim to determine if the regenerated NMC performs as well as a commercially sourced one. The findings propose molten salts as a simpler and more effective green process for recycling LIB cathode materials.