I design, test, and improve physical systems.

In Purdue's Rapid Prototyping Lab, I support students through the complete process of turning a CAD model into a manufacturable FDM/FFF part. Rather than only operating printers, I help students work through the decisions that determine whether a print will actually succeed: exporting CAD geometry to STL, selecting an appropriate printer and material, preparing the model in Bambu Studio or Ultimaker Cura, and choosing orientation, supports, infill, layer settings, temperatures, and other material-dependent parameters. When a design is likely to fail or print poorly, I review the geometry and slicer setup before launch and explain how changes to the CAD model or process parameters can improve manufacturability, strength, surface finish, and print reliability.

I also support the equipment itself by setting up newly deployed printers and performing calibration, preventive maintenance, troubleshooting, and general upkeep on existing systems. This makes the role a combination of additive manufacturing, equipment support, design for manufacturing, and technical coaching: I diagnose whether a problem originates from geometry, slicing strategy, material behavior, machine condition, or user setup, then translate that diagnosis into a practical fix the student can understand and apply.

As a Graduate Teaching Assistant for Purdue's introductory manufacturing course, I instructed a 19-student section through hands-on labs covering manual lathing, milling, CNC machining, metalworking, woodworking, electronics, component assembly, and design for manufacturing. My role extended beyond demonstrating equipment: I helped students connect their CAD intent to the realities of making and assembling parts, including which dimensions matter, how tolerances influence fit, and how a manufacturing process constrains the geometry a designer can reasonably specify.

I taught dimensional tolerancing and GD&T using ANSI/ASME Y14.5, reviewed engineering drawings, CAD models, bills of materials, assembly procedures, and supporting design documentation against project requirements, and helped students identify manufacturability or assembly problems before they became physical failures. I also supervised equipment use, shop safety, laboratory checkouts, metrology activities, and project validation. The experience strengthened my ability to move between design, manufacturing, inspection, documentation, and troubleshooting while explaining the reasoning behind engineering decisions rather than simply giving students an answer.

I investigated how FDM build orientation changes the mechanical and ultrasonic response of 3D-printed thermoplastics. To create a controlled comparison, I fabricated 60+ ASTM-style dogbone specimens and 24 disk specimens across six thermoplastics, printing each material in both horizontal and vertical orientations. The dogbones were used for destructive mechanical characterization through ASTM D638 tensile testing, while the disks were used for ultrasonic wave-speed measurements as a nondestructive evaluation method.

I processed the resulting stress-strain and ultrasonic datasets in MATLAB, converting the raw tensile data into Young's modulus and comparing ultrasonic wave speed across materials and orientations. The key part of the work was establishing a consistent fabrication-test-analysis workflow so orientation could be isolated as the variable of interest. Comparing horizontal and vertical populations showed that build direction significantly influenced tensile modulus and failure behavior in the tested polymers. I then consolidated the fabrication methodology, test procedures, MATLAB analysis, uncertainty considerations, results, and conclusions into formal technical documentation.

I supported Li-ion battery research by developing software around battery-performance datasets rather than treating each dataset as a one-off analysis. I built an interactive Python application using customtkinter that allowed users to upload battery data, process it, generate plots, and compare results through a repeatable interface. The tool was developed to handle and visualize 1,000+ battery-performance datapoints, reducing the amount of manual work needed whenever similar analysis or comparison tasks were repeated.

The project required me to think about both the software workflow and the engineering meaning of the outputs. I structured reusable data-handling, visualization, and comparison routines so researchers could move from imported data to interpretable plots more consistently, then used the resulting trends to build practical understanding of Li-ion cell behavior, cell-design considerations, performance tradeoffs, and battery optimization. The experience gave me a useful bridge between mechanical engineering, battery technology, data analytics, and engineering-tool development.

I worked simultaneously with two very different student groups. In Thermodynamics I, I mentored approximately 25–30 students through office hours by breaking down thermodynamic concepts, walking through solution strategies, and helping students determine where an incorrect assumption, equation, or calculation caused their solution to diverge. In ENGR 131, I instructed 50+ students in using Microsoft Excel and Python for engineering calculations, data analysis, assignments, and team projects.

I also graded 15+ assignments per student, including engineering calculations, spreadsheet logic, individual deliverables, and team reports. Because the work ranged from thermodynamic problem solving to introductory programming and engineering communication, the role strengthened my ability to explain technical reasoning at different levels and to evaluate not only whether a result was correct, but whether the method used to obtain it was defensible.