Ansab User

Developed deep learning algorithms for image analysis in medical imaging, enabling automated diagnosis. Designed high-performance real-time image segmentation models and engineered AI-driven noise reduction techniques.

Designed deep learning models for biomolecular sequence analysis and optimized LLM agents for AI-powered semantic search, improving retrieval accuracy by 45%. Developed scalable AI architectures for distributed training.

Engineered custom CUDA kernels to enhance model inference speeds by 3x for real-time AI applications. Designed APIs to support AI-driven data lineage tracking and optimized ML training pipelines.

Designed and optimized big data pipelines for fraud detection and risk assessment, reducing data retrieval time by 50%. Developed real-time analytics dashboards for anomaly detection and worked with SQL, NoSQL, and cloud-based storage solutions.

Developed deep learning algorithms for image analysis in medical imaging, enabling automated diagnosis and biomarker detection. Designed high-performance real-time image segmentation models, optimizing their execution using CUDA and OpenMP. Engineered AI-driven noise reduction techniques for biomedical images, improving clarity for enhanced analysis.

Designed deep learning models for biomolecular sequence analysis and protein-ligand interaction predictions. Applied parallel computing techniques to accelerate large-scale scientific simulations. Developed scalable AI architectures using PyTorch and JAX, enabling distributed training on cloud-based HPC clusters.

Engineered custom CUDA kernels, enhancing model inference speeds by 3x for real-time AI applications. Architected large-scale ML training pipelines leveraging PyTorch Lightning and Ray, optimizing compute resource allocation. Designed APIs to support AI-driven data lineage tracking, improving data management across distributed systems.