Stella X. Yu
Professor, University of Michigan, Ann Arbor
Adjunct Professor, University of California, Berkeley
Director, ICSI Vision Group
Contact: stellayu @ umich . edu

UM EECS 598 Action and Perception Winter 2023


Google Scholar




My research lies at the intersection of computer vision, human vision, and machine learning. Visual perception presents not just a fascinating computational problem, but more importantly an intelligent solution for large-scale data mining and pattern recognition applications.

Human vision is a universal sensing system like no other: It is a flexible light meter, an instant geometer, a versatile material comparator, and a holistic parser. What fascinates me most is that babies with normal vision eventually all learn to see out of an initial nebulous blur and from their wide range of different visual experiences; such rich integrated sensing and recognition is so well developed that seeing becomes believing and visual reality the reality.

My research has thus three themes.

1. Actionable Representation Learning Driven by Natural Data
I attribute our fast effortless vision to actionable representation learning driven by natural data, where mid-level visual pieces can be reassembled and adapted for seeing the new.
Exemplary works: The Emergency of Objectness, Unsupervised Hierarchical Semantic Segmentation, SegSort, Scalable NCA, Instance-Group Discrimination, Instance Discrimination, Open Compound Domain Adaptation, Open Long-Tailed Recognition, Unsupervised Selective Labeling.

2. Efficient Structure-Aware Machine Learning Models
I view a computational model as dual to the data it takes in; since visual data are full of structures, models reflective of such structures can achieve maximum efficiency.
Exemplary works: Co-Domain Symmetry, SurReal: Complex-Valued Learning, Recurrent Parameter Generator, Orthogonal CNN, Clipped Hyperbolic Classifiers and CO-SNE.

3. Application to Science, Medicine, and Engineering
I am interested in applying computer vision and machine learning to capture and exceed human expertise, enabling automatic data-driven discoveries in science, medicine, and engineering.
Exemplary works: Unsupervised Phenotyping of Retinal Fundus Images and Demographics Prediction from Meibography, High Fidelity MRI Reconstruction, BatVision, Regional Scale Building Information Modeling, Iterative Human and Automated Identification of Wildlife Images, Dental Restoration.

Advisees Ph.D. Students: Tsung-Wei Ke, Peter Jiayun Wang, Ke Wang, Utkarsh Singhal, Peter Zhihang Ren, Frank Xudong Wang, Daniel Chun-Hsiao Yeh, Tony Long Lian

Postdocs: Sangryul Jeon, Iksung Kang

Graduate Students: Girish Chandar Ganesan, Naren Doraiswamy, Zilin Wang, Ge Zhang

Undergraduate Students: Qianqi Yan, Leon Maksin, Matthew Wang

Note: Prospective postdocs and Ph.D. students: I always welcome motivated and thoughtful candidates with strong math / debugging / communication skills. For German and Italian postdoc candidates, please consider applying for respective ICSI visiting programs.


Ph.D. Students

Postdocs: Dong-Jin Kim, Nils-Steffen Worzyk, Yunhui Guo, Yubei Chen, Saeed Seyyedi, Sascha Hornauer, Rudrasis Chakraborty, Qian Yu, Zhirong Wu, Ziwei Liu, Caigui Jiang, Matthias Demant, Seyed Ali Amirshahi, Dimitri Lisin, Christina Pavlopoulou

Graduate Students: Xinlei Pan, Daniel Lin, Haoran Guo, Galen Chuang, Yifei Xing, Arian Ranjbar, Jesper Haahr Christensen, Jianqiao Ni, Michele Winter, Sebastian Palacio

Undergraduate Students: Tejasvi Kothapalli, Joshua Levine, Daniel Zeng, Ke Li, Runtao Liu, Shuai Liu, Emily Hsiao, Alice Duan, Lu Yu, Pengyuan Chen, Wayne Li, Lucy Yang, Borong Zhang, Vinay Ramasesh, Noah Golmant, Renee Sweeney, Riley Edmunds, William Guss, Asha Anoosheh


Wolverine, MCommunity