# Ming-Wei Chang

Ming-Wei Chang is a computer scientist at Google DeepMind, known for co-authoring the BERT paper and contributing to large language models and natural language processing.

Ming-Wei Chang is a computer scientist and researcher at [Google DeepMind](https://www.wikiprompt.org/wiki/google-deepmind), recognized for his contributions to [natural language processing](https://www.wikiprompt.org/wiki/natural-language-processing) (NLP) and [large language models](https://www.wikiprompt.org/wiki/large-language-model). He is best known as a co-author of the BERT paper, which introduced a [transformer](https://www.wikiprompt.org/wiki/transformer)-based model that significantly advanced the field of [machine learning](https://www.wikiprompt.org/wiki/machine-learning) and became a foundational technology in modern [artificial intelligence](https://www.wikiprompt.org/wiki/artificial-intelligence).

Chang's work spans various aspects of NLP, including model architectures, [deep learning](https://www.wikiprompt.org/wiki/deep-learning) techniques, and applications of [neural networks](https://www.wikiprompt.org/wiki/neural-network). His research has influenced both academic research and industrial applications, particularly in search and language understanding.

## Early Career and Education

Chang received his PhD in computer science from the University of Illinois at Urbana-Champaign, where he worked on machine learning and NLP. His doctoral research focused on structured prediction and learning with latent variables, topics that later informed his work on large-scale language models.

After completing his PhD, Chang joined Microsoft Research, where he contributed to projects on entity linking and knowledge base population. He later moved to Google, where he became part of the team that developed BERT.

## BERT and Its Impact

In 2018, Chang co-authored the paper "BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding" with Jacob Devlin and other colleagues. BERT (Bidirectional Encoder Representations from Transformers) introduced a method for pre-training a [transformer](https://www.wikiprompt.org/wiki/transformer) model on a large corpus of text, then fine-tuning it for specific tasks. This approach achieved state-of-the-art results on a wide range of NLP benchmarks, including question answering and language inference.

BERT's success marked a turning point in NLP, leading to the widespread adoption of pre-trained language models. It influenced subsequent models such as GPT and T5, and its architecture became a standard building block in many [large language models](https://www.wikiprompt.org/wiki/large-language-model). Chang's contributions to BERT included work on the model's training objectives and evaluation.

## Research Contributions

Beyond BERT, Chang has published numerous papers on topics such as multi-task learning, [few-shot learning](https://www.wikiprompt.org/wiki/few-shot-learning), and model interpretability. He has also worked on question answering systems and information extraction, aiming to make AI systems more robust and efficient.

At Google, Chang has been involved in projects that bridge research and product, helping to integrate advanced NLP techniques into search and other services. His work often emphasizes practical applications, such as improving the accuracy of Google Search and enabling more natural interactions with AI assistants.

## Current Role and Influence

As of 2025, Chang is a senior research scientist at [Google DeepMind](https://www.wikiprompt.org/wiki/google-deepmind), where he continues to explore new architectures and training methods for [large language models](https://www.wikiprompt.org/wiki/large-language-model). He is also an active contributor to the academic community, serving on program committees for major conferences like ACL and NeurIPS.

Chang's work has been widely cited, and he is regarded as a key figure in the development of modern NLP. His research has not only advanced the field but also shaped the direction of [generative AI](https://www.wikiprompt.org/wiki/generative-ai), particularly in the area of language understanding.

## Recognition and Awards

Chang has received several awards for his research, including best paper awards at top conferences. His contributions to BERT have been recognized as foundational to the current wave of AI progress, and he is frequently invited to speak at industry and academic events.

Despite his achievements, Chang remains focused on addressing open challenges in AI, such as bias and efficiency, and on making AI systems more accessible and reliable.

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Source: https://www.wikiprompt.org/wiki/ming-wei-chang
License: CC BY-SA 4.0 (https://creativecommons.org/licenses/by-sa/4.0/)
Last updated: 2026-09-05T13:26:24.933792+00:00