This project highlights the ability to bridge materials science, data analysis, and computational modeling to advance alloy design and materials informatics research.

A deep dive into feature importance of in-situ solar wind property measurements. Applying Prophet & LSTM deep learning techniques to sunspot number forecasting.

An exploratory data analysis of American popular music between 1992-2022

A tutorial on how to use the Glue python library within Juptyer Notebook

An interview based study to understand human motivation and behavior surrounding online gameplay.

TL;DR This study examined how maple wood microstructure affects stain absorption and distribution, showing that earlywood grain regions absorb stain significantly faster than latewood grain regions due to larger vessel pores and higher hydraulic conductivity. The research concluded that automated spray-staining reduces natural grain contrast due to non-uniform stain density, offering insights into improving industrial wood-finishing processes.

TL;DR This project used molecular dynamics simulations to investigate how pH influences the hydrolytic degradation behavior of polylactic acid (PLA), revealing distinct degradation mechanisms in acidic, alkaline, and neutral environments. The work demonstrated that higher pH accelerates chain breakage and increases degradation species, confirming MD as a consistent and effective tool for modeling polymer degradation.

TL;DR The Gas Hat project developed a solar-powered, Arduino-based wearable sensor designed to alert agricultural workers to dangerous oxygen-deficient conditions in manure pits. By integrating an O₂ sensor, buzzer, LED alerts, and flexible solar panels, the device offers a low-maintenance, self-powered safety solution aligned with One Health and ASSIST initiatives.