Research
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AI systems, unlike humans, are brittle, not robust, often struggle when faced with novel situations, and highly sensitive to small perturbations, which can lead to catastrophically poor performance.
My research aims to develop trusted and safe machine intelligence, by building tools to address learning framework, algorithmic, data, and computing challenges.
My perspective is to connect computing issues in representation
learning (imperfect data, structure knowledge), self-supervised learning (limitation of labels), interactive learning (weak supervision and uncertain environments), adaptive learning (shift and drifted environment), stream learning (limitation of memory) and more as disruption-robust learning.
This connection not only provides a unified understanding, but also paves a principled and innovative way to design trusted and safe systems as a disruption-robust framework.
I execute two important steps steps towards this vision.
The first step (data representation construct) aims to integrate structure knowledge, self-optimization, explainability to achieve deep robust representation to fight imperfect and complexity data.
The second step (learning strategy construct) aims to integrate robust representations with adaptive and interactive learning to fight uncertain and constrained environments.
PhD Advisee Dissertations and Publications
- AI4Data: Generative AI for Data Transformation and Augmentation
- Data-centric AI: from Reinforcement Decisions to Generative Intelligience
- Geospatial Generative AI for Automated Urban Planning and Urban Informatics
- Deep Time Series Learning
- Reinforcement Learning for Automated Data Science
- Structure Knowledge Guided Spatial-Temporal Graph and Knowledge Graph Representation Learning: Summary
Student Advisees Before COVID
- Self-optmizing Feature Selection
- Dual Learning, Neural Diffusion, Social Computing, and Recommender Systems
- AI for Smart Education and Learning Science
- Machine Learning for Human Mobility Modeling: Summary
- Deep Graph Learning
Some Intersting Collaborations with Indutries Before Ph.D. Graduation
- Machine Learning for Urban Vibrancy Analysis with Crowd-sourced Geo-tagged Data: Summary
- Machine Learning for In-App Behavior Analysis
- Machine Learning for Mobile Recommender Systems
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