Jie Lu

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Professor

University of Sydney

Australia

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About

Professor Jie Lu is a faculty member at the University of Technology Sydney, Australia. His research interests include genomics-enhanced healthcare systems, federated learning, and reinforcement learning, with a focus on personalized and ethical AI applications. Recent publications highlight his work on cancer risk prediction, graph neural networks, and adaptive learning methods in dynamic environments.

Recent Grants

Grant: Close

Project DP140101366

Open Date: 2014-01-01

Close Date: 2016-12-31

Grant: Close

(Trust-enhanced recommender systems for personalised government-to-business e-service)

Open Date: 2011-01-01

Close Date: 2013-12-31

Grant: Close

(A Comprehensive Platform for Dynamic Decision Support in Warning Systems through Better Management of Uncertain Information)

Open Date: 2008-01-01

Close Date: 2010-12-31

Grant: Close

(Uncertain Information Processing for Situation Awareness and Dynamic Decision-Making in Emergency Management)

Open Date: 2005-01-01

Close Date: 2007-12-31

Grant: Close

(Generalizing Multi-level Decision Support Handling Multi-objectives, Multi-followers and Uncertainty for Critical Resource Planning)

Open Date: 2005-01-01

Close Date: 2009-12-31

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Collaborators (8)

Feng Liu

Assistant Professor in Machine Learning

University of Melbourne

AUSTRALIA

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Zheng Yan

University of Sydney

AUSTRALIA

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Guangquan Zhang

Associate Professor

University of Sydney

AUSTRALIA

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Yiliao Song

RMIT University

AUSTRALIA

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Junyu Xuan

University of Sydney

AUSTRALIA

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Qian Liu

Nanyang Technological University (NTU)

SINGAPORE

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Yi Zhang

University of Sydney

AUSTRALIA

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Zhen Fang

Lecturer

University of Sydney

AUSTRALIA

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Jie Lu

Research Interests

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About

Recent Grants

Articles (20)

Genomics-Enhanced Cancer Risk Prediction for Personalized LLM-Driven Healthcare Recommender Systems

Responsible AI: Portraits with Intelligent Bibliometrics

Learning Latent and Changing Dynamics in Real Non-Stationary Environments

Inferring Latent Temporal Sparse Coordination Graph for Multiagent Reinforcement Learning.

A Federated Graph Neural Network with Differential Privacy for Cross-domain Recommender Systems

Fuzzy Rule-Based Test-Time Adaptation for Class Imbalance in Dynamic Scenarios

Out-of-distribution detection with non-semantic exploration

Fuzzy Domain Adaptation From Heterogeneous Source Teacher Models

Integrated Image-Text Augmentation for Few-Shot Learning in Vision-Language Models

Multiview Classification Through Learning From Interval-Valued Data

Disentangling Stochastic PDE Dynamics for Unsupervised Video Prediction

Bridging the Theoretical Bound and Deep Algorithms for Open Set Domain Adaptation

Learning Data Streams With Changing Distributions and Temporal Dependency

MGRC: An End-to-End Multigranularity Reading Comprehension Model for Question Answering

A Deep Dual Adversarial Network for Cross-Domain Recommendation

Evolving Gradient Boost: A Pruning Scheme Based on Loss Improvement Ratio for Learning Under Concept Drift

Semi-Supervised Heterogeneous Domain Adaptation: Theory and Algorithms

An Extremely Simple Algorithm for Source Domain Reconstruction

Graph Convolutional Neural Networks With Diverse Negative Samples via Decomposed Determinant Point Processes

Multi-Stream Concept Drift Self-Adaptation Using Graph Neural Network

Collaborators (8)

Feng Liu

Zheng Yan

Guangquan Zhang

Yiliao Song

Junyu Xuan

Qian Liu

Yi Zhang

Zhen Fang

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