Academic Projects

As the final project in IE 418: Human/Computer Interface Design, I developed an AI-driven Health & Wellness Assistant designed to provide personalized lifestyle recommendations, including meal plans, fitness plans, and sleep insights. The AI featured a basal metabolic rate calculator, and a progress tracker that generated daily and weekly summaries for calorie intake, workouts, and sleep patterns, helping users monitor their habits over time. The system allowed users to manually input groceries or upload grocery store receipts, which the assistant used to create a personalized meal plan for the entire week based on the user's goals. Throughout development, I focused on creating an intuitive user experience, refining the UI for clarity and ease of use. Challenges included optimizing processing speed and refining data input workflows to reduce redundancy. Future improvements would focus on backend optimization for faster AI responses and enhanced macro-based meal planning. The project successfully combined AI-driven insights with a streamlined interface to support users in achieving their health goals.

My team designed and manufactured a metal-cast business card holder as part of our IE 428 final project. The process began with a simple and elegant design, incorporating a flat top surface, fillets for smooth edges, and 5° drafts for mold release. We developed a gating system with an 0.88:1:1 ratio for the sprue, runners, and in-gates, using an unpressurized system to ensure a controlled metal flow. The project involved 3D modeling, pattern plate creation, mold preparation, and multiple casting attempts to refine the final product. We encountered 3D printing errors, material selection challenges, and post-processing difficulties, which required adjustments to the match plate and tolerance considerations (±0.05 inches). The project reinforced key manufacturing principles, emphasizing the importance of design validation, process iteration, and precision in metal casting 

My team developed a resource allocation strategy for opioid crisis support vending machines as part of our IE 460 project. The project focused on optimizing inventory allocation, restocking strategies, and cost analysis for harm reduction vending machines deployed in different locations. We implemented data cleaning techniques to refine raw data, ensuring accurate demand and stockout calculations. Using volume-based allocation methods, we determined optimal item distribution within a 72-slot vending machine while maintaining space and cost constraints. A cost and space index was applied to balance trade-offs between stocking essential supplies and managing expenses. Our restocking strategy was tailored to depletion rates, ensuring efficient supply chain management. Additionally, we analyzed 10 demand scenarios to assess how changes in demand would impact resource allocation and cost projections. The final model provided a scalable framework for dynamically adjusting vending machine inventory based on real-time demand while minimizing costs over a 10-year projection period.