Graduate Studies & Research
Dr. Jennifer Rizkallah
Postdoctoral Research Fellow
School of medicine
Dr. Jennifer Rizkallah joined the team of the dean Dr. Sola Aoun Bahous at LAU in to September 2021. She also teaches Data structures course at LAU faculty of engineering.
She holds a PhD in signal, image and vision from University of Rennes 1 and PhD biomedical engineering from the Lebanese University in the context of the European project LUMINOUS (H2020 FET-Open) where her main research activities were focused on using the Electroencephalography (EEG) to track the dynamics of brain activity at millisecond time-scale and the EEG source connectivity method to solve the inverse problem, reconstruct and track the brain networks dynamics at the cortical level while maintaining the EEG excellent temporal resolution. This work extended the methodological and clinical developments on functional connectivity at cortical level where this method was used in a clinical application related to the disorders of consciousness.
evaluating resistance towards carbapenem antibiotics in Pseudomonas aeruginosa. She was awarded scholarships to pursue her PhD degree in “Biology & Health” with emphasis in “Cell Biology” and “Microbiology” at Université de Montpellier. During her PhD, she conducted her research at the French National Council for Scientific Research (CRBM, Montpellier) where she played a role in characterizing the intracellular lifestyle of the endosymbionts Wolbachia which are bacteria that could be used as tools in the fight against some viral and parasitic infections. Her research interests are mainly summarized by the following key-words: Host-microorganism interactions – Symbiosis – Infectious diseases – Cellular organelles – Cellular stress – Microscopy – Genetic engineering – Microbial genomics – Antimicrobial resistance.
Most existing approaches have focused on analyzing structural and functional brain graphs separately. However, graph signal processing (GSP) is an emerging area of research, where signals recorded at the nodes of the graph are studied atop the underlying graph structure, allowing to analyze the signals from a new viewpoint. This work can also be applied on other biomedical signals such as high-density surface electromyography (HD-sEMG) or multimodal cardiac recordings in order to provide more reliable and robust clinical monitoring and diagnosis.