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My research lies at the intersection of computer vision, human
vision, machine learning, and robotics. Visual perception is not only
a fascinating computational challenge, but more importantly, an
intelligent solution to large-scale pattern recognition and adaptive
decision-making in complex environments.
Human vision is a universal sensing system like no other: It serves
as a flexible light meter, an instant geometer, a versatile
material comparator, and a holistic parser. What fascinates me
most is that infants with normal vision all learn to see --
starting from a nebulous blur and shaped by diverse, natural
experiences. This developmental process leads to deeply integrated
sensing and recognition, such that seeing becomes believing
and visual reality the reality.
My research explores this phenomenon through four interconnected 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 elements
can be flexibly recomposed to support generalization. My work
seeks to uncover these internal compositional structures that make
recognition and adaptation in humans so efficient, and to build
models that do the same.
Recent works:
Open Ad-hoc Recognition,
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. Integrative Perception-Action Learning for Embodied Autonomy
I study vision as integral to how an agent achieves autonomy and
understands the world through interaction. Traditional vision
research often defines perception tasks in isolation, e.g.,
recognition, detection, segmentation, as if perception were an end in
itself. In contrast, I view perception as fundamentally shaped by its
role in agent autonomy and world understanding. This broader
perspective is rooted in human development, where vision emerges not
as a separate module, but through its continuous coupling with bodily
action and environmental feedback. My research explores this coupling
through computational models and embodied learning systems that treat
perception and action as inseparable components of autonomy.
Recent work:
Integrative Skill Development
in Humanoids
3. Efficient Structure-Aware Machine Learning Models
I view a computational model as dual to the data it takes
in. Because natural visual data are inherently structured - spatially,
temporally, and semantically - models that reflect these structures
can achieve greater efficiency, robustness, and interpretability. My
work incorporates structural inductive biases such as symmetry,
part-whole hierarchies, and geometric constraints to accelerate
learning and improve generalization.
Recent works:
Visually Consistent Hierarchical Recognition,
Unsupervised Pose-Aware Feature Learning,
Emergent Data-Driven Prototypicality,
Co-Domain Symmetry,
SurReal: Complex-Valued Learning,
Recurrent Parameter Generator,
Orthogonal CNN,
Clipped Hyperbolic Classifiers and
CO-SNE.
4. 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:
Coordinate-Based Neural
Representations for Computational Adaptive Optics,
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.
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| Advisees |
Ph.D. Students:
Utkarsh Singhal,
Alfredo De Goyeneche,
Ryan Feng,
Zilin Wang,
Anna Kay,
Jerry Zhengjie Xu,
Yixing Wang,
Seung Hyun Lee,
Andrew Christopher Scheffer,
Stanley Cheng-Lin Hsieh,
Ye Li,
Sihan Xu
Postdocs:
Iksung Kang,
Amir Rahimi,
Kwan-Yee
Lin,
Yan Xu,
Seulki
Park,
Gustavo Perez,
Osher Azulay
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 to get a sense if we can develop shared vision.
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| Alumni |
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:
Sangwoo Mo,
Fei Pan,
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:
Brian Wang,
Saicharan Bandikallu,
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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