Inspiration
Understanding how diseases like brain tumors evolve and impact the human body often requires high-performance computing tools that are inaccessible to non-experts. I wanted to explore how complex medical simulations could be abstracted into an interface that emphasizes intuition and visual understanding rather than technical barriers.
What it does
BrainTumor HPC simulates and visualizes tumor progression and its downstream effects on the human body through an interactive dashboard. It allows users to explore how disease spreads over time and how different anatomical systems are affected, without requiring knowledge of high-performance computing or medical modeling.
How I built it
I built a web-based system that separates large-scale computation from real-time interaction, using a simulated HPC pipeline to process tumor progression scenarios and a 3D anatomical viewer to present the results. The interface abstracts complex computation into discrete, interpretable steps that can be explored visually.
Challenges I ran into
A major challenge was balancing technical credibility with usability—representing large-scale computation accurately without overwhelming users or misrepresenting medical realism. Integrating interactive visualization while maintaining a clean, controlled user experience also required careful design. Also, my teammates left due to busy schedules : (
Accomplishments that I’m proud of
I’m proud of creating a solo project that combines system design, UI/UX, and visualization to make complex computational concepts approachable. The result demonstrates how high-performance computing workflows can be meaningfully presented to non-expert users.
What I learned
This project reinforced the importance of abstraction, visualization, and interface design when communicating complex technical processes. I also gained experience thinking about how large-scale computation can be framed and explored interactively.
What’s next for BrainTumor HPC
Future work includes expanding the simulation framework, supporting additional disease models, enabling parameter customization, and refining the visualization to better represent uncertainty and variability in simulated outcomes.
(creds to Chat GPT for writing^^)
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