American Univesity of Beirut

MSFEA PhD candidate and alumnus win MIT's 2020 top innovators under 35

​​​​​​A great way to end 2020! 

We are thrilled and proud of Maroun Semaan Faculty of Engineering and Architecture (MSFEA) PhD candidate Jessica Hanna and alumnus Haitham Hassanieh for being recognized among the top 10 innovators under 35 in the MENA region by MIT TechReview.

Innovators Under 35 celebrates promising innovators who develop new technology, or innovate utilizations of existing technologies, in order to solve the world's biggest problems. Each year since 1999, MIT Technology Review announces an annual list of exceptionally talented young innovators whose work has the greatest potential to transform the world.

Ms. Jessica Hanna​​ is the lead PhD student on the eDiamond project that is jointly supervised by professors Joseph Costantine, Youssef Tawk​, Rouwaida Kanj from the ​Department of Electrical and Computer Engineering​​, and professor Assaad Eid from the Faculty of Medicine.​

Dr. Haitham Hassanieh, an alumnus of the Computer and Communications Engineering program in 2009, is now an assistant professor at the University of Illinois, and is on the list for the second time for his invention of a new sensor for self-driving cars.

Details about all winners can be found here. In 2019 MSFEA graduates were also featured on the list, see this article for more.


Jessica Hanna

Ph.D. Candidate at American University of Beirut, Maroun Semaan Faculty of Engineering and Architecture

"Jessica is a Ph.D. candidate in the Biomedical Engineering program with her work being executed in the Electrical and Computer Engineering department and the Department of Anatomy, Cell Biology and Physiological Sciences at the American University of Beirut (AUB). Her research focuses on designing non-invasive electromagnetic wearable sensors for continuous glucose monitoring. Her invention is a first-of-its-kind noninvasive continuous wearable glycemic monitoring reconfigurable multi-sensor system. Ediamond is a non-invasive glucose monitoring system embedded inside the patient's clothes, or wearable accessories to monitor glucose variations throughout the day and night using electromagnetic fields. Sensors embedded within wearable apparel such as an armband or a glove can radiate electromagnetic waves. When these waves are transmitted into the body, the changes in the reflected and the transmitted waves at specific frequencies are associated with the glucose fluctuations in the blood. These variations are analyzed and converted into glucose levels by means of data analytics and smart algorithms. The sensors embedded within the clothes of the patients will treat the body as its load and as part of the sensor itself. In addition, these sensors are optimized to mimic the vasculature anatomy of the human body, which enhances the impact of blood composition variation on the sensors and enhance their sensitivities and selectivity to glucose."


Haitham Hassanieh

Assistant Professor at University of Illinois Urbana-Champaign
Alumnus, American University of Beirut, Maroun Semaan Faculty of Engineering and Architecture, CCE’2009

"Haitham’s research focuses on building internet-of-things (IoT) systems and technologies that deliver new capabilities and applications that were never possible before. His inventions range from new sensors that enable self-driving cars to see through fog to finding surprising ways to hack (and secure) smart home assistants like Google Home and Alexa with inaudible sound. He has also developed the world’s fastest algorithm for computing the Fourier Transform, making a major leap in the 50-year old algorithms which is used across almost all computing applications, ranging from medical imaging to GPS; this work formed the core of his PhD thesis earned him the ACM Dissertation Award (and the Sprowls award for the best doctoral dissertation in computer science at MIT), and it was selected by Technology Review as one of the world’s top 10 breakthrough technologies in 2012.

His newest invention: a sensor that enables self-driving cars to see through fog and work in adverse weather conditions. Specifically, today’s self-driving cars rely on vision sensors (like cameras and LIDARS) and thus can only see and navigate in good visibility. However, if the visibility is low (e.g., in fog or bad weather conditions), today’s cars cannot see or navigate. Indeed, this has led to multiple incidents of self-driving cars crashing in bad weather conditions. To overcome this problem, Haitham led a team of researchers that came up with a completely different solution. Their solution relies on millimeter-wave radars. Unlike visible light, millimeter-wave radars can traverse fog and rain, and reflect off other objects in the environment before coming back to the car. Prof. Hassanieh’s sensor captures these reflections and uses them to image obstacles (e.g., other cars) in order to avoid accidents. To do this, they design a first-of-its-kind AI (Artificial Intelligence) that can image and recognize cars to avoid occlusions."


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