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Research & teaching at the intersection of computer architecture, HPC, quantum computing, and AI for healthcare.
My work focuses on memory-centric acceleration (near-data processing / compute caches), quantum & hybrid ML, and efficient AI systems for data-intensive applications, with emphasis on reproducibility and practical constraints.
Highlights
Research
Systems and algorithms for accelerating memory-bound workloads, with architecture-level insights and simulation-driven evaluation.
- Near-data processing, compute-cache architectures, and full-system simulation
- Efficient AI for medical imaging and multimodal clinical data
- Quantum & hybrid learning: benchmarks, robustness, and scalability
Teaching
Courses spanning low-level systems (from C to assembly), computer architecture, and emerging topics in HPC and quantum computing.
- Architecture fundamentals and performance reasoning
- Hands-on labs: measurement, profiling, and reproducible experimentation
- Capstones that connect research rigor with real deliverables