Making computer vision work in the real world,
where labels are noisy, annotations are scarce,
and ground truth is a moving target.
Who I am
NLP to vision, supervised to weakly supervised, seismic facies to general segmentation. I work extensively with transformer architectures (ViT, SegFormer, SETR, MiT, BERT) and CNN-based models (DeepLab, U-Net). My engineering background means I approach research with a systems mindset, and my research means I build tools shaped by real scientific needs, not abstract requirements.Projects
Weakly Supervised Semantic Segmentation
PhD research on training segmentation models under weak supervision, when ground truth annotations are noisy, incomplete, or generated automatically. Current focus on pseudo-label generation and refinement strategies that allow better use of scarce labeled data without sacrificing model reliability.
Transformer Architectures for Seismic Segmentation
A systematic comparison of transformer-based segmentation architectures applied to seismic facies data, bridging state-of-the-art vision models and the practical demands of geoscientific interpretation. The study addresses model performance and the inherent ambiguity of expert annotation in subsurface data.
Presented at the 85th EAGE Annual Conference
Fake News Detection with BERT
Two years as a junior researcher applying transformer-based NLP to automated misinformation detection. This was where I first got serious about machine learning and established the methodological foundation, from supervised NLP to the weakly supervised vision work that followed.
Minerva
Minerva fills the gap between raw PyTorch and production MLOps, the space where researchers burn time on glue code. Concrete, opinionated classes so experiments can be built and reproduced, not assembled from scratch each time.
The architecture is layered: readers, datasets, data modules, and pipelines, each with a defined contract to extend. The standout decision is FromPretrained: Minerva wraps messy SSL checkpoint transfer into a constructor-compatible class with regex filters and a rename map, composing cleanly with YAML configs.
Aimed at graduate researchers in applied deep learning: time-series, computer vision, limited-label domains. Not a production platform. Engineering for science means reproducibility and honest defaults over throughput.
Skills
- ViT (Vision Transformer)
- SegFormer
- SETR
- Segmenter
- MiT (Mix Transformer)
- DeepLab Family
- U-Net
- BERT
- Transformer architectures
- Fine-tuning strategies
- Text classification
- Weakly supervised learning
- Pseudo-label generation
- Systematic comparison
- Geoscience applications
- Seismic segmentation
- PyTorch + Lightning
- Python (primary)
- OSS framework design
- Software architecture
- Modular ML pipelines
Get in touch
Open to research collaborations,
engineering conversations,
and interesting problems.