Developed a cross-platform Flutter application for smart bed systems, incorporating real-time sleep tracking, intelligent scheduling, and seamless integration with multi-sensor inputs via NVIDIA Jetson edge computing. Engineered an interactive analytics dashboard with machine learning-powered sleep pattern recognition and personalized visualizations. Optimized app performance across iOS and Android through efficient state management, sensor data fusion, and low-latency communication pipelines.

Engineered a hybrid DNA sequence error detection pipeline by integrating PySAM with DNABERT, leveraging transformer-based language models for biologically contextualized error classification. Employed advanced NGS (Next-Generation Sequencing) preprocessing techniques, including dynamic quality-based read filtering and statistical error profiling, to enhance sequencing data fidelity. Applied AI-driven sequence embedding and classification models to improve detection of subtle variant patterns and noise, significantly boosting downstream variant calling accuracy.