Hiba Baroud

Dr. Hiba Baroud is an assistant professor in the Department of Civil and Environmental Engineering and the Littlejohn Dean's Faculty Fellow.  Her work explores data analytics and statistical methods to measure and analyze the risk, reliability, and resilience in critical infrastructure systems.  In particular, she has studied data-driven Bayesian methods to predict the occurrence of disruptive events in infrastructure systems and stochastically model the recovery process of the physically disrupted system as well as other interdependent and indirectly impacted systems. She also developed decision analysis tools to assess different preparedness and recovery investment strategies for the protection of civil infrastructures.

Dr. Baroud holds a Ph.D. in Industrial and Systems Engineering from the University of Oklahoma. She has a Master of Mathematics from the Department of Statistics and Actuarial Science at the University of Waterloo where she focused in her research on the application of statistics, particularly time series models, to analyze financial data. Prior to that, she obtained her B.S. in Actuarial Science from Notre Dame University, Lebanon.

In the summer of 2013, she had an internship with IBM at the Watson Research Center in Yorktown Heights, NY, and she spent the summers of 2014 and 2015 at the Summer Doctoral Institute organized by the Center for International Business Education and Research at the George Washington University. In Fall 2014, she was a visiting student scholar in the Department of Geography and Environmental Engineering at Johns Hopkins University, working with the Guikema Research Group.

Her work has twice been awarded the Best Paper Award in the Homeland Security Track of the Industrial and Systems Engineering Research Conference. In 2013, she was the recipient of the Student Merit Award of the Engineering and Infrastructure Specialty Group of the Society for Risk Analysis, she discusses one aspect of her research in this video that was produced for the award.

Dr. Baroud is part of the Infrastructure Resilience Division (IRD) in the American Society of Civil Engineers (ASCE). She is a member of the Society for Risk Analysis (SRA), the Institute for Operations Research and the Management Sciences (INFORMS), International Society for Bayesian Analysis (ISBA), Institute for Industrial Engineers (IIE), and the Institute of Electrical and Electronics Engineers (IEEE). 

Research Areas

Critical infrastructures constitute key elements in the operation of today’s society and economy. The Department of Homeland Security identifies 16 sectors such as transportation, energy, and water systems, among others. Some of the major concerns pertaining to such systems are security, resilience, and sustainability. A number of factors including natural hazards, extreme weather, climate change, intelligent threats, aging infrastructure, and restrictive funding make critical infrastructures management challenging. As a result, research is currently focused on identifying and developing tools that address such issues as well as define and measure the risk, reliability, and resilience in critical infrastructure systems. The objective of this research area is to provide an efficient risk-informed decision making process for the protection of civil infrastructures. 

Critical infrastructure systems are highly correlated and interdependent systems. A disruptive event impacting one system not only results in cascading effects in the physically disrupted system but can also have indirect impacts on interdependent systems. For instance, a disruption in the water infrastructure will result in failures in communications, power, and transportation systems leading to large scale impacts on the nation’s economy. With the recent technological advances, systems information is recorded on a timely basis. As a result, questions arise with respect to managing high volume and high velocity data collected from a variety of sources, or in other words Big Data. The challenge in this area is to develop or identify the appropriate statistical techniques to draw inferences, make predictions, and inform decision making to improve preparedness and recovery investment strategies.