Yingzhen Yang

Yingzhen Yang is an Assistant Professor at Arizona State University's School of Computing and Augmented Intelligence. His research focuses on statistical machine learning, deep learning, optimization, and theoretical computer science. He has held industrial internships at Microsoft Research and Hewlett-Packard Labs, and has contributed to projects in computer vision and graphics.

• Ph.D. from the University of Illinois

Research interests include theory of deep learning, subspace learning, sparse representation, and optimization. His work bridges machine learning theory and applications, with notable contributions to L0-Sparse Subspace Clustering and neural architecture search.

Recent publications highlight advancements in graph neural networks, transformer optimization, and medical image classification. His work on L0-SSC earned a Best Paper Finalist award at ECCV 2016.

• Awards: ECCV 2016 Best Paper Finalist, 2010 Carnegie Dean's Tuition Fellowship

Advises students in the Statistical Deep Learning Lab, focusing on topics like model compression and AutoML. Collaborates on synthetic data generation and robust neural architecture search.

Labs/Teams: Leads the Statistical Deep Learning (SDL) Lab, fostering research in theory-driven machine learning applications.