My research spans artificial intelligence, quantum computing, and theoretical computer science, with an emphasis on efficient, reliable, and scalable computational methods.
My AI research focuses on making learning systems more efficient, reliable, and easier to deploy. Current work includes structured model pruning, resource-efficient learning, robust AI, and the evaluation and optimization of LLM-based multi-agent systems.
My quantum research develops algorithms and systems for near-term and emerging quantum platforms. Topics include quantum circuit cutting, hardware-aware circuit synthesis and optimization, FPGA-accelerated simulation, variational algorithms, and AI-assisted quantum workflows.
My theoretical work studies efficient algorithms and the mathematical structure underlying computation. One line of work uses error-correcting codes to design algorithms for high-dimensional closest-pair problems and the Light Bulb Problem.
Collaborative quantum computing research at Case Western Reserve University and Cleveland Clinic.
