Anti-Sparkle Optimization with ML
Objective: Reduce sparkle while preserving anti-glare and brightness.
How: Optuna-tuned NN in TensorFlow to search film parameters and quantify trade-offs.
Selected projects across ML, optical modeling, data products, and more
Objective: Reduce sparkle while preserving anti-glare and brightness.
How: Optuna-tuned NN in TensorFlow to search film parameters and quantify trade-offs.
Objective: Modernize legacy BSDF modules for maintainable R&D workflows.
How: Fortran→C# interop with structured tests and docs for virtual optics pipelines.
Objective: Prioritize user stories and improve effort estimation.
How: Embeddings + NN for ranking/points with a React UI and AWS integration.
Objective: Assess a NASA-derived concept for neurological diagnostics.
What: Clinical, regulatory, and market analysis with staged validation and risk mapping.
Objective: Explore VR-based cognitive assessment leveraging NASA COBRA signals.
How: Prototype task design, signal exploration, and pilot scoping with advisors.
Objective: Present work clearly for recruiters and collaborators.
What: Foundation grid, semantic HTML, fast loads, and focused CTAs.
Objective: Classify species from noisy field recordings.
How: Spectrogram transforms, CNN baselines, and augmentation for robustness.
Objective: Create learning games with adjustable difficulty.
How: Greedy, UCS, A*, and Minimax (Alpha-Beta) strategies decoupled from UI.
Objective: Make climate and crop yield trends understandable.
What: Static routes + responsive charts with clear narrative annotations.
Objective: Demonstrate secure DB access patterns.
How: Python + SQLite examples contrasting concat vs. parameterized queries.
Objective: Support targeted mental-health programming.
What: Dashboards and outcome summaries for staff after data cleaning.
Objective: Reveal participation patterns to improve outreach.
How: Segment analysis and recommendations aligned to program capacity.