Stella X. Yu
Professor, University of Michigan, Ann Arbor
Adjunct Professor, University of California, Berkeley
stellayu @ umich . edu
NEW on LinkedIn 734-647-1761
Research Areas: Computer Vision
Embodied AI
Developmental AI
Machine Learning



Advanced Topics in Computer Vision 2024 Fall


Action and Perception 2024 Winter


Advanced Topics in Computer Vision 2023 Fall


Action and Perception 2023 Winter


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 from 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.
Recent works: Concurrent Segmentation and Recognition, Unsupervised Hierarchical Semantic Segmentation, The Emergence of Objectness, 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.
Recent works: Emergent Data-Driven Prototypicality, 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.
Recent 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.


Ph.D. Students: Utkarsh Singhal, Alfredo De Goyeneche, Ryan Feng, Zilin Wang, Anna Kay, Jerry Zhengjie Xu, Stanley Cheng-Lin Hsieh, Andrew Christopher Scheffer, Yixing Wang, Seung Hyun Lee

Postdocs: Iksung Kang, Sangwoo Mo, Amir Rahimi, Fei Pan, Kwan-Yee Lin, Yan Xu, Seulki Park, Gustavo Perez

M.S. Students: Brian Wang, Saicharan Bandikallu

Note: I look for motivated and thoughtful postdocs and Ph.D. students in computer vision, robotics, and machine learning. Strong math / debugging / communication skills are desired. Please read my papers. I am not considering highschoolers or remote interns.


Ph.D. Students

  • Peter Zhihang Ren: Serial Dependence Study in Medical Image Perception via Generative Models
  • Peter Jiayun Wang: Structure-Aware Representation Learning and Its Medical Applications
  • Ke Wang: Magnetic Resonance Image Reconstruction with Greater Fidelity and Efficiency
  • Tsung-Wei Ke: Learning Visual Groupings and Representations with Minimal Human Labels
  • Ke Wang: Magnetic Resonance Imaging with Greater Fidelity and Efficiency
  • Zhongqi Miao: Deep Learning Applications in Wildlife Recognition
  • Baladitya Yellapragada: Insights and Applications from Data-driven Representation Learning
  • Jyh-Jing Hwang: Learning Image Segmentation with Relation-centric Loss and Representation
  • Pat Virtue: Complex-valued Deep Learning with Applications to Magnetic Resonance Image Synthesis
  • Elena Bernardis: Finding Dots in Microscopic Images
  • Weiyu Zhang

Postdocs: Sangryul Jeon, Dong-Jin Kim, Nils-Steffen Worzyk, Yunhui Guo, 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: Youren Zhang, Oliver Wang, Gaurav Kaul, Daniel Chun-Hsiao Yeh, Tony Long Lian, Girish Chandar Ganesan, Ge Zhang, Qingyi Chen, Frank Xudong Wang, Naren Doraiswamy, Xinlei Pan, Daniel Lin, Haoran Guo, Galen Chuang, Yifei Xing, Arian Ranjbar, Jesper Haahr Christensen, Jianqiao Ni, Michele Winter, Sebastian Palacio,

Undergraduate Students: Leon Maksin, Qianqi Yan, Matthew Wang, 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

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