Large-Scale Text Corpus Deduplication and Dataset Enhancement

• Developed and deployed text corpus deduplication using a suffix array algorithm on MapReduce, boosting assessor F1-score from 0.77 to 0.82.
• Trained a classifier to improve benchmark coverage, enhancing dataset relevance for pretraining tasks.
• Created a dataset augmentation pipeline with Back Translation, increasing pretraining robustness and generalization.
• Enhanced document parsing quality, resulting in 3% faster model convergence and improved resource efficiency.

• Engineered a SOTA multi-document summarization model for news aggregation, achieving an 87% human acceptance rate with less than 1% critical errors.
• Designed automated news timelines for evolving events, resulting in a 2.8% increase in content depth and a 1.4% boost in user time spent.
• Optimized ranking algorithms with a CTR prediction model, increasing daily active users by 4.3% and user engagement by 2.1%.
• Created a novel similarity scoring formula, improving F1-score from 0.91 to 0.95 for news clustering.

• Implemented processing of all basic data types from Clang AST to equivalent constructions in Eolang.
• Developed a mechanism for translating multidimensional arrays to Eolang.
• Developed a mechanism for translating Enum types to Eolang.

• Used StarGAN architecture to classify images across multiple domains.
• Achieved an F1 Score of 0.83, Precision of 0.9917, and Recall of 0.8051 on the CelebA dataset.
• Trained model over 500,000 iterations using PyTorch and Adam optimizer.
• Applied adversarial loss, classification loss, and gradient penalty for balanced training.
• Optimized performance with specific hyperparameters ensuring stable performance.

As a computer vision expert, I was consulted to address the challenge of facial pore recognition for a beauty application. The problem involved accurately identifying and measuring individual pores on various skin types and tones. To tackle this, I designed an algorithm that could adaptively capture high-resolution images and extract relevant features. Leveraging machine learning, I trained a model on a comprehensive dataset that I helped gather, comprising different face images under various lighting conditions. The resulting algorithm could recognize and measure individual pores with high precision, providing personalized skincare recommendations to users, and thereby enhancing the app's user experience.