Qing Qu

Siyi Chen awarded Predoctoral Fellowship to support research on Controllable and Interpretable AI models

Chen, Ph.D. student in Electrical and Computer Engineering, is working to improve the usability, safety, and reliability of AI systems in real-world applications.

Can Yaras awarded Predoctoral Fellowship to support research on deep learning and generative AI models

Yaras, Ph.D. student in Electrical and Computer Engineering, is working to better understand the inner workings of deep learning and provide better access to foundation models in AI.

Qing Qu receives U-M Chinese Heritage & Scholarship Junior Faculty Award

Prof. Qu joins the inaugural cohort of awardees recognized as emerging leaders in teaching, research, and service at U-M.

Fifteen papers by ECE researchers to be presented at the Conference on Neural Information Processing Systems

Topics of accepted ECE NeurIPS papers include diffusion models, large language models, multi-armed bandit models, and more.

ECE faculty design chips for efficient and accessible AI

Faculty specializing in architecture, hardware, and software innovation accelerate machine learning across a range of applications.

Can Yaras recognized for his research aimed at efficient algorithms for LLMs

Doctoral student Can Yaras wants to reduce the carbon footprint of AI.

Fourteen papers by ECE researchers to be presented at the International Conference on Machine Learning

Accepted papers for the ICML conference span topics including deep representation learning, language model fine-tuning, generative modeling, and more.

GenAI diffusion models learn to generate new content more consistently than expected

Award-winning research led by Prof. Qing Qu discovered an intriguing phenomenon that diffusion models consistently produce nearly identical content starting from the same noise input, regardless of model architectures or training procedures.

Improving generative AI models for real-world medical imaging

Professors Liyue Shen, Qing Qu, and Jeff Fessler are working to develop efficient diffusion models for a variety of practical scientific and medical applications.

Neural Collapse research seeks to advance mathematical understanding of deep learning

Led by Prof. Qing Qu, the project could influence the application of deep learning in areas such as machine learning, optimization, signal and image processing, and computer vision.

Miniature and durable spectrometer for wearable applications

A team led by P.C. Ku and Qing Qu have developed a miniature, paper-thin spectrometer measuring 0.16mm2 that can also withstand harsh environments.

Teaching Machine Learning in ECE

With new courses at the UG and graduate level, ECE is delivering state-of-the-art instruction in machine learning for students in ECE, and across the University

Qing Qu receives CAREER award to explore the foundations of machine learning and data science

His research develops computational methods for learning succinct representations from high-dimensional data.

Qing Qu uses data and machine learning to optimize the world