Dr. Ding’s research interests lie in the broad and cross-cutting fields of domain-specific programming language design, architecture and compiler optimization, and hardware acceleration (e.g., FPGA, GPU systems). She has successfully built multiple high-performance, energy-efficient, and high-fidelity programming systems for both machine learning and quantum computing. These works require extensive engineering and empirical evaluation of real systems, applications, and workloads (which are extremely time intensive but important for academic and social impact). Especially, her research pioneers the effort in building optimization frameworks that take a holistic view across multiple layers, ranging from high-level algorithmic optimization to lower-level compilation and runtime optimization, and to the underlying architecture design optimization. Her works also aim to build intelligent compilation systems to automatically select the best set of optimizations towards varying inputs and underlying hardware architectures. Thanks to the large optimization space opened up by the holistic optimization stack and automatic optimization enabled by the compiler-centric infrastructure, her work brings in magnitudes of performance improvement and saves years of manual efforts in multiple computing fields, leading to transformational advances for the community.