Tianfu Wu
Education
-
Ph.D.
2011
Statistics
University of California, Los Angeles -
Master's
2005
Computer Science
Hefei University of Technology -
Bachelor's
1999
Electrical and Computer Engineering
University of Science and Technology of China
Funded Research
- SEMA: Segmentation and Estimation of Missile Attitude
- ASTUTE: Automated Spectral and Temporal Understanding of Tiered Events
- Development: The Next-Generation Public Charging Infrastructure and Cyber-Information Network for Enhanced Inclusion and Independent Living of Power Mobility Device Users
- NRI: Fnd: A Novel Intervention Method to Promote Workers’ Safety Awareness and Mental Health During Human-Robot Collaboration
- RI:Small: Neural Architecture Search with Deep Compositional Grammatical Structures
- Learning Deep Grammar Networks for Visual Question Answering
- Learning Deep AND OR Grammar Networks for Object Tracking, Detection and Parsing: A Unified Framework
- Interdisciplinary Distinguished Seminar Series
Recent News
Researchers Fine-Tune Control Over AI Image Generation
Posted on June 2, 2021 | Filed Under: Research
ECE researchers fine-tune control over AI image generation with a new approach that allows AI to move or alter elements in an image while keeping them identifiably the same.
New Framework Makes AI Systems More Transparent Without Sacrificing Performance
Posted on October 22, 2019 | Filed Under: Research
Researchers led by Tianfu Wu are proposing a framework that would allow users to understand the rationale behind AI decisions.
This post was originally published in NC State News.
New Framework Improves Performance of Deep Neural Networks
Posted on May 23, 2019 | Filed Under: Research and Smart
A new framework for building deep neural networks outperforms existing state-of-the-art artificial intelligence frameworks, including the widely-used ResNet and DenseNet systems, in visual recognition tasks.
This post was originally published in NC State News.
Recent Publications
- A Bottom-Up and Top-Down Integration Framework for Online Object Tracking (2021)
- Event driven sensor fusion (2021)
- Learning Regional Attraction for Line Segment Detection (2021)
- Local Clustering with Mean Teacher for Semi-supervised learning (2021)
- Holistically-Attracted Wireframe Parsing (2020)
- AOGNets: Compositional Grammatical Architectures for Deep Learning (2019)
- Image Synthesis From Reconfigurable Layout and Style (2019)
- Jointly social grouping and identification in visual dynamics with causality-induced hierarchical Bayesian model (2019)
- Learning Attraction Field Representation for Robust Line Segment Detection (2019)
- Neural Abstract Style Transfer for Chinese Traditional Painting (2019)