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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.
Recent 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.
Recent 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.
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.
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| Advisees |
Ph.D. Students:
Utkarsh Singhal,
Peter Zhihang Ren,
Daniel Chun-Hsiao Yeh,
Ryan Feng,
Zilin Wang,
Anna Kay,
Jerry Zhengjie Xu,
Postdocs:
Sangryul Jeon,
Iksung Kang
Graduate Students: Youren Zhang
Undergraduate Students:
Leon Maksin
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.
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| Alumni |
Ph.D. Students
- 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
- 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
- Weiyu Zhang
- Elena
Bernardis: Finding Dots in Microscopic Images
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:
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:
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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