Associate Professor

The George Washington University

Jun 2019 – Present Washington, DC
Department of Engineering Management and Systems Engineering

Assistant Professor

The George Washington University

Aug 2013 – Jun 2019 Washington, DC
Department of Engineering Management and Systems Engineering

Postdoctoral Fellow

Aug 2012 – Jul 2013 Baltimore, MD
Center for Advanced Modeling in the Social, Behavioral, and Health Sciences, Department of Emergency Medicine

Senior Research Scientist


Aug 2010 – Jun 2012 Arlington, VA


Graduate Students


H. Deniz Marti

Ph.D. Candidate


Pedram Hosseini

Ph.D. Student


Lydia Gleaves

Ph.D. Student


Dian Hu

Ph.D. Candidate

Predicting political events, Learning new languages/cultures/music, Playing the flute


Zhenglin Wei

Ph.D. Candidate

Systems Architecture, Policy, Regulation and Innovation


J. John Park

Ph.D. Student


Michael C. Smith

Ph.D. Candidate

Undergraduate Students


Noor Al Busaidi

Undergraduate Student



Mark Dredze

John C Malone Associate Professor, Computer Science, JHU

Natural Language Processing, Machine Learning, Health Informatics, Clinical NLP, Computational Epidemiology


Sandra Crouse Quinn

Professor and Chair, Family Science, UMD

Public health, Health disparities


Joel Moses

Institute Professor, MIT

Relationship of complexity and flexibility in large scale systems, Modelling of intelligent behavior in the mind


Christopher L. Magee

Professor of the Practice, MIT

Product development, Automotive design, Value engineering, Network analysis, Standards and protocols, Technological progress dynamics


Eili Y. Klein

Assistant Professor of Emergency Medicine, JHU

Ecology and epidemiology, Antibiotic resistance, Malaria, Plasmodium falciparum


Liana Fraenkel

Adjunct Professor of Medicine, Yale

Decision Making, Decision Making (computer assisted), Epidemiology, Rheumatology, Veterans, Patient Preference


Valerie F. Reyna

Lois and Melvin Tukman Professor of Human Development, Cornell

Judgment and Decision Making, Risk and Rationality, False Memory, Aging and Cognitive Impairment, Cognitive and Social Neuroscience, Developmental Neuroscience



SiHua Qi


Knowledge Graph-based Dialogue System (Medical QA System), Natural Language to SQL, Information Extraction, NLP Applications in Healthcare/Finance


Our research featured in Washington Post, The Guardian, and IFLScience

Best Poster in Computer Science Department

Government Intervention Needed to Address Vaccination Misinformation

Research Finds Extreme Elitism, Social Hierarchy among Gab Users

Recent & Upcoming Talks

Poster Presentation at the Society for Judgment and Decision Making Annual Meeting

Conference Presentation at the 50th Annual Meeting of the Psychonomics Society

Advancing Research on Social Media and Vaccine Confidence

Pandemic & Biosecurity Threats in the 21st Century

Hidden Agendas for Online Vaccination: Trolls, Bots & Misinformation


Online Misinformation and Disinformation

How should we combat the spread of misinformation and disinformation online?

System Architecture

How can we design very large systems such that they can be flexible and resilient?

Decision Under Risk

How do technical experts make risky decisions?

Group Decision Making

How do multi-disciplinary groups share knowledge to make better decisions?

Social Media for Public Health

Can we use social media to understand disease spread and vaccine refusal?

Recent Publications

Quickly sort through publications using this link.

Background In 2018, Facebook introduced Ad Archive as a platform to improve transparency in advertisements related to politics and “issues of national importance.” Vaccine-related Facebook advertising is publicly available for the first time. After measles outbreaks in the US brought renewed attention to the possible role of Facebook advertising in the spread of vaccine-related misinformation, Facebook announced steps to limit vaccine-related misinformation. This study serves as a baseline of advertising before new policies went into effect. Methods Using the keyword ‘vaccine’, we searched Ad Archive on December 13, 2018 and again on February 22, 2019. We exported data for 505 advertisements. A team of annotators sorted advertisements by content: pro-vaccine, anti-vaccine, not relevant. We also conducted a thematic analysis of major advertising themes. We ran Mann-Whitney U tests to compare ad performance metrics. Results 309 advertisements were included in analysis with 163 (53%) pro-vaccine advertisements and 145 (47%) anti-vaccine advertisements. Despite a similar number of advertisements, the median number of ads per buyer was significantly higher for anti-vaccine ads. First time buyers are less likely to complete disclosure information and risk ad removal. Thematically, anti-vaccine advertising messages are relatively uniform and emphasize vaccine harms (55%). In contrast, pro-vaccine advertisements come from a diverse set of buyers (83 unique) with varied goals including promoting vaccination (49%), vaccine related philanthropy (15%), and vaccine related policy (14%). Conclusions A small set of anti-vaccine advertisement buyers have leveraged Facebook advertisements to reach targeted audiences. By deeming all vaccine-related content an issue of “national importance,” Facebook has further the politicized vaccines. The implementation of a blanket disclosure policy also limits which ads can successfully run on Facebook. Improving transparency and limiting misinformation should not be separate goals. Public health communication efforts should consider the potential impact on Facebook users’ vaccine attitudes and behaviors.

The blurry line between nefarious fake news and protected-speech satire has been a notorious struggle for social media platforms. Further to the efforts of reducing exposure to misinformation on social media, purveyors of fake news have begun to masquerade as satire sites to avoid being demoted. In this work, we address the challenge of automatically classifying fake news versus satire. Previous work have studied whether fake news and satire can be distinguished based on language differences. Contrary to fake news, satire stories are usually humorous and carry some political or social message. We hypothesize that these nuances could be identified using semantic and linguistic cues. Consequently, we train a machine learning method using semantic representation, with a state-of-the-art contextual language model, and with linguistic features based on textual coherence metrics. Empirical evaluation attests to the merits of our approach compared to the language-based baseline and sheds light on the nuances between fake news and satire. As avenues for future work, we consider studying additional linguistic features related to the humor aspect, and enriching the data with current news events, to help identify a political or social message.

We present preliminary results on the online war surrounding distrust of expertise in medical science – specifically, the issue of vaccinations. While distrust and misinformation in politics can damage democratic elections, in the medical context it may also endanger lives through missed vaccinations and DIY cancer cures. We find that this online health war has evolved into a highly efficient network insurgency with direct inter-crowd links across countries, continents and cultures. The online anti-vax crowds (referred to as Red) now appear better positioned to groom new recruits (Green) than those supporting established expertise (Blue). We also present preliminary results from a mathematically-grounded, crowd-based analysis of the war’s evolution, which offers an explanation for how Red seems to be turning the tide on Blue.

Content regulation and censorship of social media platforms is increasingly discussed by governments and the platforms themselves. To date, there has been little data-driven analysis of the effects of regulated content deemed inappropriate on online user behavior. We therefore compared Twitter — a popular social media platform that occasionally removes content in violation of its Terms of Service — to Gab — a platform that markets itself as completely unregulated. Launched in mid-2016, Gab is, in practice, dominated by individuals who associate with the “alt-right” political movement in the United States. Despite its billing as “The Free Speech Social Network,” Gab users display more extreme social hierarchy and elitism when compared to Twitter. Although the framing of the site welcomes all people, Gab users’ content is more homogeneous, preferentially sharing material from sites traditionally associated with the extremes of American political discourse, especially the far right. Furthermore, many of these sites are associated with state-sponsored propaganda from foreign governments. Finally, we discovered a significant presence of German language posts on Gab, with several topics focusing on German domestic politics, yet sharing significant amounts of content from U.S. and Russian sources. These results indicate possible emergent linkages between domestic politics in European and American far right political movements. Implications for regulation of social media platforms are discussed.

Resources for students

How to be a Successful PhD Student

Hanna Wallach and Mark Dredze wrote an excellent guide on How to be a Successful PhD Student. Although it focuses on Computer Science, machine learning and Natural Language Processing students, it’s geared for PhD students in general.

How to Write a Thesis

Richard de Neufville wrote an excellent guide on how to write a thesis. This manual was designed for students in MIT’s Technology and Policy program and has valuable insights for all students writing theses and dissertations.

Join us!

Do you want to work with the DMSA Lab? Great! If you are a GW student, please contact us using the contact form at the bottom of this page. It is probably helpful if you have strong system modeling, data analytics, software development, or experimental design skills. If you are interested in becoming a GW student you can apply here: