Kai-Wei Chang

Kai-Wei Chang enhances global conversations on artificial intelligence and machine learning as an Associate Professor in the Computer Science Department at the University of California, Los Angeles. His research is at the forefront of trustworthy natural language processing (NLP), reasoning in NLP, and multimodal foundation models. With a keen interest in developing AI systems that autonomously acquire knowledge, Chang's work focuses on addressing real-world problems through advanced machine learning and natural language understanding techniques. In addition to his academic role, Chang holds a joint position as an Amazon Scholar at Alexa AI, where he collaborates on cutting-edge projects that push the boundaries of AI technology. His contributions to the field have been recognized with numerous accolades, including being named a Sloan Fellow and an AAAI senior member. These honors underscore his commitment to advancing the capabilities and reliability of AI systems. Chang's research has garnered substantial support from prestigious organizations such as the National Science Foundation (NSF), Intelligence Advanced Research Projects Activity (IARPA), Office of Naval Research (ONR), National Institutes of Health (NIH), and the Defense Advanced Research Projects Agency (DARPA). His work is also backed by industry giants like Taboola, Facebook, and Google, highlighting the practical implications and industry relevance of his research endeavors. Through his innovative research, Chang aims to bridge the gap between theoretical AI models and their practical applications, ensuring that AI technologies are not only powerful but also trustworthy and ethical. His efforts contribute significantly to the development of AI systems that can effectively understand and interact with human language, paving the way for more intuitive and reliable AI-driven solutions in various domains. Kai-Wei Chang's dedication to advancing AI research and his collaborative efforts with both academic and industry partners make him a pivotal figure in the field of computer science. His work continues to inspire and shape the future of AI, fostering a deeper understanding of how machines can learn and reason in ways that benefit society as a whole.