Philip Yu

Distinguished Professor & Wexler Chair in Information Technology

University of Chicago

United States

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About

Professor Philip Yu is a Distinguished Professor and Wexler Chair in Information Technology at the University of Illinois at Chicago. His research areas encompass artificial intelligence, graph neural networks, and federated learning, as evidenced by his recent publications exploring topics such as AIOps, privacy and fairness in machine learning, and medical image report generation. He is recognized for his contributions to advancing knowledge and techniques in information technology and its applications.

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Collaborators (5)

Wanlei Zhou

University of Sydney

AUSTRALIA

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Ziyue Li

Assistant Professor

University of Cologne

GERMANY

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Jian Yang

Macquarie University

AUSTRALIA

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Lichao Sun

Lehigh University

UNITED STATES

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Cheng Long

Assistant Professor

Nanyang Technological University (NTU)

SINGAPORE

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Philip Yu

Research Interests

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About

Articles (30)

A Survey of AIOps in the Era of Large Language Models

Enhancing signed graph neural networks through curriculum-based training

Linkage on security, privacy and fairness in federated learning: New balances and new perspectives

GL-BKGNN: Graphlet-based Bi-Kernel Interpretable Graph Neural Networks

PromptSED: An evolving topic-enhanced prompting framework for incremental social event detection

Dual-level semantic collaboration and inference network for medical image report generation

EGNN: Exploring Structure-Level Neighborhoods in Graphs With Varying Homophily Ratios

Topology-Based Node-Level Membership Inference Attacks on Graph Neural Networks

Introduction to the Special Issue on Knowledge Transferring for Recommender Systems

Unique Security and Privacy Threats of Large Language Models: A Comprehensive Survey

Privacy and Fairness in Federated Learning: On the Perspective of Tradeoff

HUSP-SP: Faster Utility Mining on Sequence Data

Machine Unlearning: A Survey

FELight: Fairness-Aware Traffic Signal Control via Sample-Efficient Reinforcement Learning

Unsupervised Social Bot Detection via Structural Information Theory

Triplet-aware graph neural networks for factorized multi-modal knowledge graph entity alignment

Augmenting Low-Resource Cross-Lingual Summarization with Progression-Grounded Training and Prompting

Relational Prompt-based Pre-trained Language Models for Social Event Detection

A Survey of Text Watermarking in the Era of Large Language Models

Privacy-preserving federated discovery of DNA motifs with differential privacy

EViT: Privacy-Preserving Image Retrieval via Encrypted Vision Transformer in Cloud Computing

Towards complex dynamic physics system simulation with graph neural ordinary equations

A Contrastive-Enhanced Ensemble Framework for Efficient Multi-Agent Reinforcement Learning

Introduction to the Special Issue on Advanced Graph Mining on the Web: Theory, Algorithms, and Applications: Part 1

Triadic Closure Sensitive Influence Maximization

Learning From Atypical Behavior: Temporary Interest Aware Recommendation Based on Reinforcement Learning

Modeling Reviews for Few-Shot Recommendation via Enhanced Prototypical Network

Balancing Learning Model Privacy, Fairness, and Accuracy With Early Stopping Criteria

Adversarial Attacks and Defenses in Deep Learning: From a Perspective of Cybersecurity

MC 2 : Unsupervised Multiple Social Network Alignment

Collaborators (5)

Wanlei Zhou

Ziyue Li

Jian Yang

Lichao Sun

Cheng Long

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