2026 Ming-Wei Chang: Computer Science Researcher – H-Index, Publications & Awards

Discipline name	D-Index	World Ranking	Current World Ranking	National Ranking	Current National Ranking	Publications	Citations
Computer Science	45	6961	6759	3041	2926	105	88429

Overview

Ming-Wei Chang is affiliated with Google in the United States and has a focused research profile primarily in computer science with significant contributions across several related subfields. Their scholarly output includes a strong emphasis on artificial intelligence, computer vision and pattern recognition, biomedical engineering, pharmaceutical science, and biomaterials.

Their work spans numerous topics that intersect machine learning and biomedical applications. Key topics in Ming-Wei Chang's research include:

Topic Modeling
Natural Language Processing Techniques
Multimodal Machine Learning Applications
Electrospun Nanofibers in Biomedical Applications
Advancements in Transdermal Drug Delivery
Electrohydrodynamics and Fluid Dynamics
Domain Adaptation and Few-Shot Learning

The scientist has published extensively, with 72 papers in computer science-related fields. Their most frequent publication venues are:

arXiv (Cornell University) - 25 publications
Journal of Drug Delivery Science and Technology - 5 publications
Advanced Drug Delivery Reviews - 3 publications
Drug Discovery Today - 2 publications
Micromachines - 2 publications

Among their recent papers are the following:

"EMBI" (2024), presented at Leibniz-Zentrum für Informatik (Schloss Dagstuhl)
"REALM: Retrieval-Augmented Language Model Pre-Training" (2020), published in arXiv (Cornell University)
"A review of emerging technologies enabling improved solid oral dosage form manufacturing and processing" (2021), featured in Advanced Drug Delivery Reviews
"Application of mesoporous silica nanoparticles as drug delivery carriers for chemotherapeutic agents" (2020), in Drug Discovery Today
"High Precision 3D Printing for Micro to Nano Scale Biomedical and Electronic Devices" (2022), published in Micromachines

Ming-Wei Chang frequently collaborates with a range of researchers, including Zeeshan Ahmad, Muhammad Sohail Arshad, Kenton Lee, Saman Zafar, and Baolin Wang. The collaboration counts with these coauthors range from 9 to 31 joint publications.

Best Publications

BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding

Jacob Devlin;Ming-Wei Chang;Kenton Lee;Kristina N. Toutanova

75459
Citations
Natural Questions: A Benchmark for Question Answering Research

Tom Kwiatkowski;Jennimaria Palomaki;Olivia Redfield;Michael Collins

1954
Citations
Load forecasting using support vector Machines: a study on EUNITE competition 2001

Bo-Juen Chen;Ming-Wei Chang;Chih-Jen lin

1192
Citations
Latent Retrieval for Weakly Supervised Open Domain Question Answering

Kenton Lee;Ming-Wei Chang;Kristina N. Toutanova

894
Citations
Semantic Parsing via Staged Query Graph Generation: Question Answering with Knowledge Base

Wen-tau Yih;Ming-Wei Chang;Xiaodong He;Jianfeng Gao

780
Citations
BoolQ: Exploring the Surprising Difficulty of Natural Yes/No Questions

Christopher Clark;Kenton Lee;Ming-Wei Chang;Tom Kwiatkowski

552
Citations
A Knowledge-Grounded Neural Conversation Model

Marjan Ghazvininejad;Chris Brockett;Ming-Wei Chang;Bill Dolan

518
Citations
REALM: Retrieval-Augmented Language Model Pre-Training.

Kelvin Guu;Kenton Lee;Zora Tung;Panupong Pasupat

489
Citations
The Value of Semantic Parse Labeling for Knowledge Base Question Answering

Wen-tau Yih;Matthew Richardson;Christopher Meek;Ming-Wei Chang

427
Citations
Well-Read Students Learn Better: On the Importance of Pre-training Compact Models

Iulia Turc;Ming-Wei Chang;Kenton Lee;Kristina Toutanova

427
Citations
Zero-shot Entity Linking by Reading Entity Descriptions

Lajanugen Logeswaran;Ming-Wei Chang;Kenton Lee;Kristina N. Toutanova

300
Citations
Large Dual Encoders Are Generalizable Retrievers

Unknown

294
Citations
Question Answering Using Enhanced Lexical Semantic Models

Wen-tau Yih;Ming-Wei Chang;Christopher Meek;Andrzej Pastusiak

269
Citations
Driving Semantic Parsing from the World's Response

James Clarke;Dan Goldwasser;Ming-Wei Chang;Dan Roth

268
Citations
Importance of semantic representation: dataless classification

Ming-Wei Chang;Lev Ratinov;Dan Roth;Vivek Srikumar

265
Citations
Guiding Semi-Supervision with Constraint-Driven Learning

Ming-Wei Chang;Lev Ratinov;Dan Roth

253
Citations
To Link or Not to Link? A Study on End-to-End Tweet Entity Linking

Stephen Guo;Ming-Wei Chang;Emre Kiciman

227
Citations
Retrieval Augmented Language Model Pre-Training

Kelvin Guu;Kenton Lee;Zora Tung;Panupong Pasupat

224
Citations
Search-based Neural Structured Learning for Sequential Question Answering

Mohit Iyyer;Wen-tau Yih;Ming-Wei Chang

219
Citations
Leave-One-Out Bounds for Support Vector Regression Model Selection

Ming-Wei Chang;Chih-Jen Lin

158
Citations
Well-Read Students Learn Better: The Impact of Student Initialization on Knowledge Distillation

Iulia Turc;Ming-Wei Chang;Kenton Lee;Kristina Toutanova

153
Citations

Frequent Co-Authors

Kenton Lee Google (United States)

Kristina Toutanova Google (United States)

Dan Roth University of Pennsylvania

Wen-tau Yih Facebook (United States)

Jianfeng Gao Microsoft (United States)

Xiaodong He Chinese Academy of Sciences

Chih-Jen Lin National Taiwan University

William W. Cohen Carnegie Mellon University

Michael Collins Google (United States)

Ryen W. White Microsoft (United States)

External Links

Personal website of Ming-Wei Chang Google Scholar page

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