I'm a university student & open-source researcher passionate at the intersection of AI, Reinforcement Learning, and 3D Computer Graphics.
- 🔬 Building research-grade AI benchmarks with honest, reproducible evaluation
- 🚀 Exploring 3D Gaussian Splatting (3DGS) optimization and urban mobility AI
- 📊 Advocate for reproducible research — strict train/test splits, checked-in reference metrics, and honest negative results
- 🎯 Believer in rigorous experimentation over hype-driven claims
AI-driven taxi fleet repositioning on 263 NYC zones — demand forecasting, multi-agent simulation, offline RL & reproducible OPE
Which 3DGS renderer is fastest? Which compression is best? We measured them all — on the same GPU, same scenes, same protocol.
| Highlights | |
|---|---|
| ⚡ | 12 renderer variants — gsplat, 3DGS, Speedy-Splat, TC-GS, and HiGS |
| 📐 | 5 real-world scenes × multi-metric: FPS, PSNR, SSIM, LPIPS, VRAM |
| 🔬 | HiGS ablation — 7× sparse-training speedup research with quality retention |
| 📉 | Pareto-optimal analysis — identify true win-win trade-offs |
| 🌐 | Live Results |
A private, local AI workspace for RAG, knowledge bases, coding help, and LoRA fine-tuning.
"Reproducibility is not optional."
Every benchmark in my repositories includes:
- ✅ Checked-in reference metrics for validation
- ✅ Strict temporal / leakage-safe data splits
- ✅ Honest negative results — not every idea works
- ✅ Paired statistical tests — not just point estimates
- ✅ Transparent limitations documented alongside results
⚡ Built with rigor. Measured with honesty. Shared with purpose.