Mark H. Dredze, MA, PhD

Highlights

Languages

English

Gender

Male

Johns Hopkins Affiliations:

Johns Hopkins School of Medicine Faculty

About Mark H. Dredze

Background

Mark Dredze is the John C Malone Associate Professor of Computer Science at Johns Hopkins University. He is affiliated with the Applied Physics Laboratories, the Malone Center for Engineering in Healthcare, the Center for Language and Speech Processing, among others. He holds a secondary appointment in the Department of Health Sciences Informatics in the School of Medicine. He obtained his PhD from the University of Pennsylvania in 2009.

Prof. Dredze’s research develops statistical models of language with applications to social media analysis, public health and clinical informatics. Within Natural Language Processing he focuses on statistical methods for information extraction but has considered a wide range of NLP tasks, including syntax, semantics, sentiment and spoke language processing. His work in public health includes tobacco control, vaccination, infectious disease surveillance, mental health, drug use, and gun violence prevention. He also develops new methods for clinical NLP on medical records.

Beyond publications in core areas of computer science, Prof. Dredze has pioneered new applications in public health informatics. He has published widely in health journals including the Journal of the American Medical Association (JAMA), the American Journal of Preventative Medicine (AJPM), Vaccine, and the Journal of the American Medical Informatics Association (JAMIA). His work is regularly covered by major media outlets, including NPR, the New York Times and CNN.

For more information about Prof. Dredze's research, please visit his website: http://www.dredze.com.

Centers and Institutes

Additional Academic Titles

Joint Appointment in Medicine

Research Interests

Health informatics, Machine learning, Natural language processing, Social media

Lab Website

Mark Dredze Lab

The Mark Dredze Lab investigates topics such as natural language processing, speech, machine learning and intelligent user interfaces. Our team is currently exploring several key health information applications, including information extraction from social media and biomedical and clinical texts. Our recent research in these areas include vaccine communication during the Disneyland measles outbreak; the validity of online drug forums for estimating trends in drug use; and the use of Twitter to examine social rationales for vaccine refusal.

Research Summary

Dr. Dredze’s research in natural language processing and machine learning has focused on graphical models, semi-supervised learning, information extraction, large-scale learning and speech processing.

Selected Publications

Benton, Adrian, Raman Arora, Mark Dredze. “Learning multiview embeddings of Twitter users.” Association for Computational Linguistics (ACL). 2016.
Broniatowski, David Andre, Mark Dredze, Karen M Hilyard. “Effective vaccine communication during the Disneyland measles outbreak.” Vaccine. 2016.
Gao, Ning, Mark Dredze, Douglas Oard. “Knowledge base population for organization in emails.” NAACL Workshop on Automated Knowledge Base Construction (AKBC). 2016.
Peng, Nanyun, Mark Dredze. “Learning word segmentation representations to improve named entity recognition for Chinese social media.” Association for Computational Linguistics (ACL). 2016.
Smith, Michael, David A. Broniatowski, Mark Dredze. “Using Twitter to examine social rationales for vaccine refusal.” International Engineering Systems Symposium (CESUN). 2016.