Experience

Building end-to-end AI-driven education platforms at institutional scale — full-stack development spanning data ingestion pipelines, LLM model integration, and scalable backend deployment. Designing intelligent recommendation workflows for student-advisor matching and academic analytics, incorporating retrieval-augmented generation and embedding-based similarity search. Delivering production-ready AI services with robust REST APIs serving large university-wide user bases.

Architected SkipPar, a hybrid CPU-GPU LLM training framework (IPDPSW 2026, First Author). Designed a co-execution paradigm where the GPU handles all forward and backward passes while the CPU concurrently runs parameter updates. Implemented a 4-thread producer-consumer pipeline coordinating GPU computation with CPU optimizer steps; integrated PyTorch DDP hooks for multi-GPU synchronization. Benchmarked on NVIDIA A100 & H100 GPUs using LLaMA-2 (10B) and GPT-2 (9B); achieved up to 17% reduction in end-to-end training time against AAAI- and ICPP-published baselines.

Conducted lab sessions and guided student projects on network design, backpropagation, and training dynamics.

Promoted heterogeneous programming models and parallel computing tools across the university developer community.