Experience

Researched diffusion-based data synthesis for diverse training scenarios across lighting, poses, and environments
Designed novel metric learning architecture from scratch: 3x training throughput, 47% memory reduction, 2.4% accuracy lift
Researched automated visual quality assessment — won internal competition with highest accuracy across all teams
Investigated data quality methods that improved downstream search F1 by 6.7% across 7,400 categories
Led 15+ cross-team research seminars with 20+ attendees; mentored 5 researchers

Researched cascaded conditional generation and face synthesis using ControlNet architectures
Designed end-to-end generation pipeline with automated quality assurance and artifact detection
Achieved 4.1x inference acceleration through model graph optimization

Researched CrossViT architecture for visual content understanding — improved accuracy by 3.6%, filtering 1.8M+ items monthly
Investigated efficient fine-tuning strategies for multilingual BERT: 2.7x throughput at 93% accuracy
Developed 14 classification and detection models for visual content analysis
Discovered and resolved critical scoring errors affecting production model suite
Led reevaluation of detection metrics; designed validation protocols for research-to-production consistency
Co-developed research benchmarking platform with real-time metrics visualization
Authored technical documentation; organized 40+ research meetings with 10+ participants

Researched attention-based detection architectures for traffic sign recognition: +2.1% precision, +3.6% recall
Integrated novel detection heads into RetinaNet — raised mAP from 0.906 to 0.937
Deployed model on autonomous vehicle fleet as real-time detection node

Built real-time network analysis system processing millions of events/sec with SIMD vectorization
Scaled aggregation pipeline from 100K to 1M+ events/sec through parallel data structures