Jianming Liang

About

Dr. Jianming Liang has a background in both academia and industry, having spent six years at Siemens before joining Arizona State University (ASU) in December 2008. He is a full professor with chair privileges across multiple graduate faculties, including biomedical informatics and data science, computer science, computer engineering, robotics and autonomous systems, software engineering, and biomedical engineering. His research is highly interdisciplinary, integrating computer vision, machine learning, knowledge graphs, causal modeling, and foundation models to advance medical artificial intelligence (AI). His work focuses on building medical AI foundation models grounded in domain fundamentals, emphasizing fairness, explainability, and trustworthiness within sociotechnical systems to improve human welfare. Dr. Liang's research has led to FDA-approved products, over 100 peer-reviewed publications, and more than 50 US patents with additional patents pending. He has been recognized with numerous awards, including election as a Fellow of the National Academy of Inventors in 2021, and has received awards such as the Faculty Innovation Award and the Distinguished Faculty Award at ASU. His team has been honored with multiple innovation awards and best paper recognitions, and his students have received over 70 awards for their research achievements.

Research topics

• Computer Science
• Machine Learning
• Artificial Intelligence
• Natural Language Processing
• Chemistry
• Economics
• Business
• Environmental protection
• Environmental science
• Natural resource economics

Selected publications

• Models Genesis

  Medical Image Analysis · 2020 · 296 citations

  Senior authorCorresponding
  DOI

• Transferable Visual Words: Exploiting the Semantics of Anatomical Patterns for Self-Supervised Learning

  IEEE Transactions on Medical Imaging · 2021 · 137 citations

  Senior authorCorresponding

  This paper introduces a new concept called "transferable visual words" (TransVW), aiming to achieve annotation efficiency for deep learning in medical image analysis. Medical imaging-focusing on particular parts of the body for defined clinical purposes-generates images of great similarity in anatomy across patients and yields sophisticated anatomical patterns across images, which are associated with rich semantics about human anatomy and which are natural visual words. We show that these visual…

  DOI

• Self-supervised learning for medical image analysis: Discriminative, restorative, or adversarial?

  Medical Image Analysis · 2024-01-28 · 26 citations

  articleOpen accessSenior authorCorresponding
  PublisherOA PDFDOI

• A fully open AI foundation model applied to chest radiography

  Nature · 2025-06-11 · 22 citations

  articleSenior author
  PublisherDOI

• Seeking an optimal approach for Computer-aided Diagnosis of Pulmonary Embolism

  Medical Image Analysis · 2023-10-13 · 20 citations

  articleOpen accessSenior authorCorresponding
  PublisherOA PDFDOI

Recent grants

• Computer-Aided Diagnosis of Pulmonary Embolism

  NIH · $2.5M · 2016–2023

Frequent coauthors

• K. R. Gurney

  Northern Arizona University

  44 shared

• Michael B. Gotway

  42 shared

• Nima Tajbakhsh

  34 shared

• Geoffrey Roest

  Northern Arizona University

  32 shared

• Zongwei Zhou

  Johns Hopkins University

  24 shared

• Scot M. Miller

  Johns Hopkins University

  20 shared

• Suryakanth R. Gurudu

  18 shared

• Mohammad Reza Hosseinzadeh Taher

  Arizona State University

  16 shared

Education

• Ph.D., Biomedical Informatics and Data Science

  Arizona State University

Awards & honors

• Fellow of the National Academy of Inventors (2021)
• Faculty Innovation Award (2019)
• Distinguished Faculty Award (2023)
• President's Award for Innovation (2015)
• President's Award for Innovation (2024)

Similar researchers at Arizona State University

• Marisa Dominoh-34
• Carl Yamashiroh-18
• Valentin Dinuh-18