Kaggle Master with 3 Gold, 12 Silver, and 4 Bronze medals across 20 competitions. Expertise in NLP, Computer Vision, Medical Imaging, and Optimization. Highest rank: #3 out of 1,621 teams (Jigsaw Multilingual).
3 Gold 🥇 | 12 Silver 🥈 | 4 Bronze 🥉
Top 0.2% finish. Multilingual toxicity detection using XLM-RoBERTa with TPU training and adversarial validation.
Top 0.2% finish. Melanoma detection using EfficientNet with metadata fusion and test-time augmentation.
Top 0.4% finish. Large-scale click fraud detection using LightGBM with advanced feature engineering on 200M+ rows.
Top 1.1% finish. Demand prediction combining NLP, image features, and tabular data with ensemble methods.
Top 1.3% finish. Q&A quality prediction using BERT and RoBERTa with custom head architectures.
Top 0.8% finish. Product reorder prediction using gradient boosting with extensive feature engineering.
Top 6% finish. Combinatorial optimization for gift matching using Hungarian algorithm and simulated annealing.
Top 0.6% finish. Multi-label toxicity classification using bidirectional LSTM with attention and CNN ensemble.
Top 2.2% finish. Chest X-ray segmentation using U-Net with EfficientNet encoder and deep supervision.
Top 4.9% finish. Coreference resolution using BERT with span extraction and entity embeddings.
Top 2.7% finish. Constraint satisfaction optimization using mixed integer programming and local search.
Top 4.3% finish. CT scan hemorrhage detection using ResNeXt with windowing and sequence modeling.
Top 2.6% finish. Diabetic retinopathy detection using EfficientNet with ordinal regression.
Top 2.5% finish. Text complexity prediction using RoBERTa with regression head and pseudo-labeling.
Top 4.6% finish. Bias-aware toxicity detection using BERT with custom loss functions for fairness.
Top 11.7% finish. Multi-label artwork classification using ResNeXt with focal loss.
Top 6.8% finish. Species detection from audio using mel-spectrograms with EfficientNet.
Top 9.3% finish. Sentiment phrase extraction using RoBERTa with character-level span prediction.
Top 9.1% finish. Seismic image segmentation using U-Net with ResNet encoder and deep supervision.
Sketch recognition using ResNet with stroke sequence augmentation and test-time augmentation.