Hanrui Wang

Hanrui Wang is an Assistant Professor at the University of California, Los Angeles, Department of Computer Science. His research focuses on efficient AI computing through hardware-architecture-algorithm co-design, quantum computing compiler and systems, and large language models. He has published notable works such as HAT, Atomique, SpAtten, QuantumNAS, and Q-Pilot. Wang's work has been recognized with awards including Best Paper Awards at QCE 2023 and ICML RL4RL Workshop 2019, as well as a Gold Medal at the ACM Student Research Competition in 2022. His research has been featured in media outlets such as MIT News and VentureBeat.

Ryan Hanrui Wang is a researcher specializing in Deep Learning and Computer Architecture. His work focuses on optimizing neural network models for mobile devices through automated model compression and acceleration techniques. He explores efficient 3D architectures using sparse point-voxel convolutions and develops sparse attention mechanisms for improved computational efficiency. Wang also investigates hardware-aware transformer designs for natural language processing and quantum circuit reliability estimation. His research includes quantum noise-aware training, photonic transformer accelerators, and circuit design using graph neural networks and reinforcement learning. His contributions span across AI for quantum computing, efficient sparse matrix multiplication, and system-level optimizations for deep learning.