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Location
Thornton E212, 351 McCormick Rd

Xiaoxuan Yang

Assistant Professor, Electrical and Computer Engineering
Contact
Location
Thornton E212, 351 McCormick Rd
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About

Xiaoxuan Yang's research focuses on efficient and reliable processing-in-memory-based system design, biologically plausible system design, and hardware accelerators for emerging technologies and applications. Xiaoxuan Yang is an assistant professor in the Electrical and Computer Engineering at the University of Virginia. She was a postdoctoral researcher in the Robust Systems Group at Stanford University and a rising scholar research scientist in the Engineering School at the University of Virginia. She received her Ph.D. degree in Electrical and Computer Engineering from Duke University, M.S. degree in Electrical Engineering from the University of California, Los Angeles (UCLA), and B.S. degree in Electrical Engineering from Tsinghua University. 

Her research work won Third Place of ACM Student Research Competition SRC at International Conference on Computer-Aided Design (ICCAD) and the Best Research Award at ACM SIGDA Ph.D. Forum at Design Automation Conference (DAC). She has been selected as a Rising Star in EECS, an NSF iREDEFINE Fellow, and a Machine Learning and Systems Rising Star.

Education

Ph.D. Duke University

M.S. University of California, Los Angeles

B.S. Tsinghua University

Research Interests

In-Memory Computing
Hardware-Software Co-Design
Computer-Aided Design
Emerging Algorithm and Hardware

Selected Publications

Optimizing and Exploring System Performance in Compact Processing-in-Memory-based Chips
Weight Update Scheme for 1T1R Memristor Array Based Equilibrium Propagation
HERO: hessian-enhanced robust optimization for unifying and improving generalization and quantization performance
Multi-objective optimization of ReRAM crossbars for robust DNN inferencing under stochastic noise
ReTransformer: ReRAM-based processing-in-memory architecture for transformer acceleration
Neuro-Symbolic Computing: Advancements and Challenges in Hardware-Software Co-Design
ESSENCE: Exploiting Structured Stochastic Gradient Pruning for Endurance-aware ReRAM-based In-Memory Training Systems
Photonic Bayesian Neural Network using Programmed Optical Noises
Research Progress on Memristor: From Synapses to Computing Systems
Harnessing optoelectronic noises in a photonic generative network