Computational materials modeling
Simulated crystal structures to study mechanical properties under variable conditions. Built pipelines to generate datasets, run simulations, and compare outputs with lab measurements.
Science projects
Research-focused work that sharpened my approach to data, experimentation, and careful validation. I like to bridge theory with hands-on implementations that can be tested and iterated.
Projects
Selected explorations spanning university research, collaborations, and self-driven investigations.
Climate signal analysis
Decomposed environmental sensor data to detect seasonal trends and anomalies. Evaluated feature extraction methods to improve signal-to-noise for forecasting models.
Bio-inspired robotics notes
Explored control strategies inspired by muscle reflexes for small robotic platforms. Documented experiments, failure cases, and lessons for lightweight controllers.
Data quality for citizen science
Built validation checklists and heuristics for crowd-sourced observations. Balanced sensitivity and precision to keep contributions usable for downstream analyses.
What science taught me
Principles I carry into engineering work.
- Design experiments that isolate variables and make decisions on evidence, not assumptions.
- Stay disciplined about data quality, instrumentation, and post-mortems.
- Communicate results clearly for both technical and non-technical collaborators.
- Document what failed so the next iteration is faster.