Kai-Wei Chang

About

Kai-Wei Chang is an associate professor in the Department of Computer Science at the UCLA Samueli School of Engineering. His research focuses on building intelligence systems that solve real-world problems by automatically acquiring knowledge. This involves developing machine learning components capable of efficiently making coherent decisions for problems with complex structures, as well as natural language understanding components that enable systems to extract knowledge from unstructured text. He has been broadly published in fields including machine learning, natural language processing, artificial intelligence, and data mining. Chang's work aims to advance the understanding and application of statistical approaches to natural language processing and tractable machine learning methods for handling complex and large-scale data.

Research topics

• Computer Science
• Machine Learning
• Artificial Intelligence
• Data Mining
• Theoretical computer science

Selected publications

• Unified Pre-training for Program Understanding and Generation

  Proceedings of the 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies · 2021 · 577 citations

  Senior authorCorresponding

  Wasi Ahmad, Saikat Chakraborty, Baishakhi Ray, Kai-Wei Chang. Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies. 2021.

  DOI

• GPT-GNN

  2020 · 437 citations

  Graph neural networks (GNNs) have been demonstrated to be powerful in modeling graph-structured data. However, training GNNs requires abundant task-specific labeled data, which is often arduously expensive to obtain. One effective way to reduce the labeling effort is to pre-train an expressive GNN model on unlabelled data with self-supervision and then transfer the learned model to downstream tasks with only a few labels. In this paper, we present the GPT-GNN framework to initialize GNNs by gene…

  DOI

• The Root Shapes the Fruit: On the Persistence of Gender-Exclusive Harms in Aligned Language Models

  2025-06-23 · 4 citations

  articleOpen access

  Natural-language assistants are designed to provide users with helpful responses while avoiding harmful outputs, largely achieved through alignment to human preferences.Yet there is limited understanding of whether alignment techniques may inadvertently perpetuate or even amplify harmful biases inherited from their prealigned base models.This issue is compounded by the choice of bias evaluation benchmarks in popular preference-finetuned models, which predominantly focus on dominant social catego…

  PublisherOA PDFDOI

• Exploring Visual Vulnerabilities via Multi-Loss Adversarial Search for Jailbreaking Vision-Language Models

  2025-06-10 · 2 citations

  article

  Despite inheriting security measures from underlying language models, Vision-Language Models (VLMs) may still be vulnerable to safety alignment issues. Through empirical analysis, we uncover two critical findings: scenario- matched images can significantly amplify harmful outputs, and contrary to common assumptions in gradient-based attacks, minimal loss values do not guarantee optimal attack effectiveness. Building on these insights, we introduce MLAI (Multi-Loss Adversarial Images), a novel ja…

  PublisherDOI

• Multimodal Integration of Ambulatory ECG and Clinical Features for Sudden Cardiac Death and Pump Failure Death Prediction

  medRxiv · 2026-04-22

  articleOpen access

  Ambulatory electrocardiograms (ECG) provides continuous monitoring of the heart's electrical activity. However, many existing machine learning and artificial intelligence models for analyzing ambulatory ECG traces are often unimodal and do not incorporate patient clinical context. In this study, we propose a multimodal framework integrating ambulatory ECG-derived representations with clinical text embeddings to predict two cardiac outcomes: sudden cardiac death and pump failure death. Ambulatory…

  PublisherDOI

Recent grants

• AI-DCL: Governing bias in AI system with humans in the decision loop

  NSF · $300k · 2019–2022

• CAREER: MetaQuerier: Dynamic Ad Hoc Information Integration Across the Internet

  NSF · $300k · 2002–2007

• CRII: RI: Learning Structured Prediction Models with Auxiliary Supervision

  NSF · $174k · 2017–2017

Frequent coauthors

• Nanyun Peng

  107 shared

• Kuan-Hao Huang

  57 shared

• Wasi Uddin Ahmad

  56 shared

• Cho‐Jui Hsieh

  46 shared

• Aram Galstyan

  46 shared

• Jieyu Zhao

  38 shared

• Liunian Harold Li

  36 shared

• Muhao Chen

  University of California, Davis

  28 shared

Awards & honors

• Google Research Scholar Award, 2021
• EMNLP Best Long Paper Award, 2017
• C.L and Jane W. S. Liu Award, University of Illinois, 2013
• Yahoo! Key Scientific Challenges Program Award, 2010
• KDD Best Paper Award, 2010

Similar researchers at University of California, Los Angeles

• Demetri Terzopoulosh-93
• Wei Wangh-91
• Eleazar Eskinh-83