Hui Chen

I am a Research Fellow in the School of Computing in National University of Singapore, where I am fortunate to be advised by Bryan Hooi. My research focuses on natural language processing, machine learning and large language models.

Previously, I worked at Nanyang Technological University and received my Ph.D. degree in Computer Science from Singapore University of Technology and Design. I graduated with a B.Eng. degree in Computer Science from Zhejiang University.

I am currently working on AI for scientific research, especially developing automated research agents and benchmarks to accelerate scientific discovery reliably. Feel free to chat if you are also interested in these topics!

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Research

VerlTool: Towards Holistic Agentic Reinforcement Learning with Tool Use

MLR-Bench: Evaluating AI Agents on Open-Ended Machine Learning Research

PREMISE: Matching-based Prediction for Accurate Review Recommendation

Self-Adaptive Sampling for Efficient Video Question Answering

InstructCoder: Instruction Tuning Large Language Models for Code Editing

Domain-Expanded ASTE: Rethinking Generalization in Aspect Sentiment Triplet Extraction

SAT: Improving Semi-Supervised Text Classification with Simple Instance-Adaptive Self-Training

SANCL: Multimodal Review Helpfulness Prediction with Selective Attention and Natural Contrastive Learning

MM-Align: Learning Optimal Transport-based Alignment Dynamics for Fast and Accurate Inference on Missing Modality Sequences

DoubleMix: Simple Interpolation-Based Data Augmentation for Text Classification

Improving Multimodal Fusion with Hierarchical Mutual Information Maximization for Multimodal Sentiment Analysis

Bi-Bimodal Modality Fusion for Correlation-Controlled Multimodal Sentiment Analysis

Persuasive Dialogue Understanding: the Baselines and Negative Results

Dialogue Relation Extraction with Document-level Heterogeneous Graph Attention Networks

Academic Service

Workshop Organization

• AAAI 2026 Workshop on AI for Scientific Research

Program Committee

Design and source code from Jon Barron's website and Leonid Keselman's fork