Developed a machine learning model for the segmentation and analysis of carbon nanotubes in solutions. Leveraging this model to determine dispersion quality metrics. Additionally, built a full-stack internal web application to display and interpret these findings.

Leveraged computer vision, thermochemical analysis techniques (TGA, DSC, DMA), and principles of chemistry and materials science to enhance material properties using various functional grades of carbon nanotubes.

In collaboration with Dr. Micah Green and Dr. Mazin Mustafa, I utilized principles of material science and chemical engineering, such as lap shear mechanical testing, radiofrequency heating/curing, and thermodynamics, to develop carbon nanocomposite adhesives. I also authored a paper detailing a novel deep learning technique to quantify the dispersion quality of carbon nanotubes and other nanomaterials using optical microscopy data.

I deepened my understanding of manufacturing operations by applying knowledge of organic chemistry, fluid dynamics, and thermodynamics to enhance processes in the nylon industry. I received certified training in Microsoft Azure and Power BI, which I used to predict equipment lifespan with 99.2% accuracy with machine learning techniques. I also acquired experience with Aspen Process Explorer, redlining P&IDs, drafting SOPs, SIs, using IntelaTrac, and adhering to MOCs.

Quality assurance in automated test equipment for electrical manufacturing. Under this role, I learned how to interact with customers in the manufacturing industry as well as a deeper understanding of electronic boards and semiconductors.

Worked alongside Dr. Teruna Siahaan and Dr. Brian Kopec in Blood Brain Barrier protein synthesization. Contributed to the chapter "Organization of the Intestinal Mucosa and Barriers to Oral Drug Delivery" in "Nanotechnology for Oral Drug Delivery"