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


Lydia Gleaves

Ph.D. Student


Pedram Hosseini

Ph.D. Student


H. Deniz Marti

Ph.D. Candidate


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



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


Reva Schwartz

Research Director, Parenthetic LLC

Human-computer interaction, Judgment and Decision Making, Human Language Technology, Biometrics and Forensic Science


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


Government Intervention Needed to Address Vaccination Misinformation

Research Finds Extreme Elitism, Social Hierarchy among Gab Users

The George Washington University Institute for Data, Democracy and Politics (IDDP) anticipates a search for exceptional candidates for …

Our research was selected by INCOSE to be among the best from those published in Systems Engineering in 2018.

Recent & Upcoming Talks

Dr. Broniatowski gave an invited panel presentation at the OBSSR Methodology Seminar at NIH.

This talk will cover the ways in which state-sponsored and profit-seeking entities use health communication about vaccines on social …

Deniz Marti gave a presentation at the OBSSR Methodology Seminar at NIH.

Vaccine hesitancy, the reluctance or refusal to be vaccinated or to have one’s children vaccinated, has been identified by the World …


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.

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.

We propose, and test, a model of online media platform users’ decisions to act on, and share, received information. Specifically, we focus on how mental representations of message content drive its spread. Our model is based on fuzzy-trace theory (FTT), a leading theory of decision under risk. Per FTT, online content is mentally represented in two ways: verbatim (objective, but decontextualized, facts), and gist (subjective, but meaningful, interpretation). Although encoded in parallel, gist tends to drive behaviors more strongly than verbatim representations for most individuals. Our model uses factors derived from FTT to make predictions regarding which content is more likely to be shared, namely: (a) different levels of mental representation, (b) the motivational content of a message, © difficulty of information processing (e.g., the ease with which a given message may be comprehended and, therefore, its gist extracted), and (d) social values.

Objectives/Hypothesis Standard stimulating methods using square waves do not appropriately restore physiological control of individual intrinsic laryngeal muscles (ILMs). To further explore our earlier study of evoked orderly recruitment by quasitrapezoidal (QT) currents, we integrated the contribution of the cricothyroideus (CT) with attention to mutual activation in an additional patient, based on recent studies of appropriate responses via strict recurrent laryngeal nerve (RLN) stimulation. Study Design Basic science study. Methods The patient received functional electrical stimulation (FES) with QT pulses at 5 Hz, 60 to 2,000 μAmp, 100 to 500 μs pulse width, 0 to 500 μs decay. Ipsilateral electromyography (EMG) responses were calculated using the average maximum amplitude, area under the curve, and the root mean square of the rectified amplitude waveforms. The thyroarytenoideus (TA), posterior cricoarytenoideus (PCA), lateral cricothyroideus (LCA), and the CT were each interrogated via two monopolar electrodes, values were recorded in MATLAB, exported to Excel, and analyzed. Individual and mutual recruitment configurations and activation delays with stimulation were explored using multiple regression and exploration factor analyses. Results A total of 868 EMG data points based on 18 trials and up to 11 subtrials were captured from each of the four ILMs. Various combinations of pulse amplitude, pulse width, and exponential decay were found to produce significant (P ≤ .001) individual ILM responses. CT mirrored the LCA, whereas the TA and PCA exhibited separate interactions along shared trajectories in a three‐dimensional space. Conclusions FES calibrated to individual and coupled ILMs offers promise for restoring normal and pathological contraction patterns via strict RLN stimulation.

Online misinformation is primarily spread by humans deciding to do so. We therefore seek to understand the factors making this content compelling and, ultimately, driving online sharing. Fuzzy-Trace Theory, a leading account of decision making, posits that humans encode stimuli, such as online content, at multiple levels of representation; namely, gist, or bottom-line meaning, and verbatim, or surface-level details. Both of these levels of representation are expected to contribute independently to online information spread, with the effects of gist dominating. Important aspects of gist in the context of online content include the presence of a clear causal structure, and semantic coherence – both of which aid in meaning extraction. In this paper, we test the hypothesis that causal and semantic coherence are associated with online sharing of misinformative social media content using Coh-Metrix – a widely-used set of psycholinguistic measures. Results support Fuzzy-Trace Theory’s predictions regarding the role of causally- and semantically-coherent content in promoting online sharing and motivate better measures of these key constructs.

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: