Ziad Jamous

Ziad Jamous (ESIB, 2004) officially joins the US list of inventors.
21 juillet 2020

On July 21st, Ziad Jamous (ESIB, 2004) officially joined the US list of inventors, with his team of scientists. You can find below his first published patent on the USPTO site (United States Patent and Trademark Office).

Ziad holds an engineering degree in Communications and networking, form the USJ School of engineering (Ecole Supérieur d’ingénieurs de Beyrouth) obtained in 2004, as well as a Master from Boston University in Computer Systems, obtained in 2007.

Ziad dedicates his contribution to this United States patent invention to the big USJ Family: “USJ has been great to us, it has given us :1) the gift of thinking, 2) the professional visa to serve the big small world 3) the support for the new generation of young leaders to establish USJ alumni network everywhere in the world. We can not only survive, but invent, and thrive. Let's stay connected with our big USJ network and invent the world services that we deserve.”




United States Patent


Sagduyu ,   et al.

July 21, 2020

System and means for generating synthetic social media data


System and means generates synthetic forms of social media data such as data from microblogging services (e.g., Twitter) and social networking services (e.g., Facebook). This system and means jointly generate interaction graph structures and text features similar to input social media data. First, an interaction graph is generated by mapping social network interactions in input (real) social media data to graph structures. This interaction graph is fitted to a social network model (or a composite model) by minimizing the distance between the input and the synthetic interaction graphs (of potentially different sizes). The distance is measured statistically or based on the performance of social media analytics. Various patterns (such as anomalies), interaction types and temporal dynamics are generated synthetically. Second, text features are extracted from input social media data with topic modeling and statistical analysis of word tuple distributions. Based on these features, synthetic social media text is generated. Third, synthetic graph structures and text features are combined to generate the synthetic social media data. The system is particularly useful in generating data to be used for developing and testing new social media analytics or for generating or analyzing social bot network behavior and campaigns in social media, and for sharing test data with others without rate and privacy concerns.


Sagduyu; Yalin Evren (Gaithersburg, MD), Li; Jason (Potomac, MD), Shi; Yi (Germantown, MD), Grushin; Alexander (Rockville, MD), El-Jamous; Ziad (Rockville, MD)