Master of Computer Science student at Texas A&M University focusing on the intersection of 3D Computer Vision, Deep Learning, and Biomechanical Analysis.
Current research at TAMU involves developing modular 3D reconstruction pipelines for dynamic sports analysis, specifically investigating the decoupling of camera ego-motion from global human trajectories.
- Graduate Researcher at TAMU: Developing pipelines to transform 2D footage into 3D actionable data for biomechanical analysis.
- 3D Vision Specialization: Working on multi-view multi-person reconstruction, addressing temporal inconsistency and "foot sliding" artifacts in architectures like TRAM and WHAM.
- Deep Learning Reproducibility: Implementing state-of-the-art research papers from scratch (e.g., Recurrent Attention Models, custom ResNet/VGG variants).
| Category | Tools & Technologies |
|---|---|
| Languages | Python, C/C++, SQL (Postgres, MySQL), R |
| Deep Learning | PyTorch, TensorFlow, CUDA Toolkit, PyTorch Profiler |
| 3D Vision | SLAHMR, WHAM, Human3R, SAM3D, VGGT |
| Scientific Computing | NumPy, Pandas, Scikit-Learn, Matplotlib, Seaborn |
| Spatial & Imagery | GDAL, Rasterio, Cartopy, OpenCV |
| Data & Cloud | Spark, PySpark, Dask, AWS, Docker, Weights & Biases |
End-to-end ML pipeline for cyclone detection and intensity estimation.
- Developed an automated pipeline processing ~14,000 satellite images with ISRO Space Applications Centre.
- Reduced inference latency from hours to <1 second through model pruning and quantization.
- Impact: Co-authored peer-reviewed work accepted at IEEE IGARSS 2025.
Reproducing the RAM architecture in PyTorch using reinforcement learning.
- Achieved 96.64% test accuracy using only ~49% of image pixels (6 glimpses).
- Implemented via policy-gradient reinforcement learning (REINFORCE).
Research interned at Georgia Institute of Technology.
- Improved model robustness by 15% using GANs to generate synthetic insider-threat datasets.
