Kassem Kallas
About Kassem Kallas
Kassem Kallas is a Research Scientist with extensive experience in R&D across various institutions, including Centrica, NIST, and Inria. He holds a PhD in Information Engineering and Mathematical Sciences and has contributed to multiple research initiatives and publications in the fields of signal processing and machine learning.
Work at Inria
Kassem Kallas has been employed as a Research Scientist at Inria since 2022. He is based in Rennes, Brittany, France. His role involves conducting advanced research in areas related to signal processing and machine learning. Inria is known for its focus on computer science and applied mathematics, providing a collaborative environment for researchers.
Previous Experience at Centrica
Before joining Inria, Kassem Kallas worked at Centrica - Imagine More as a Senior R&D from 2018 to 2020. His tenure lasted for two years in the Florence Area, Italy. During this time, he contributed to research and development initiatives aimed at enhancing energy solutions.
Research at National Institute of Standards and Technology (NIST)
Kassem Kallas served as a Research Scientist at the National Institute of Standards and Technology (NIST) from 2020 to 2022. His work in Gaithersburg involved contributions to standards and technology development, focusing on enhancing measurement science and engineering.
Educational Background
Kassem Kallas holds a Doctor of Philosophy (PhD) in Information Engineering and Mathematical Sciences from Università degli Studi di Siena, where he studied from 2013 to 2017. He also completed a Second Level Master in Wireless Systems and Related Technologies at Politecnico di Torino in 2013, and earned a Master's degree in Computer and Communication Engineering from Lebanese International University in 2012.
Research Contributions and Awards
Kassem Kallas has made significant contributions to the field of signal processing and machine learning. He received the best paper award at the 9th International Conferences on Advances in Multimedia in 2017. His research includes applying game-theoretic concepts to adversarial signal processing and the security of machine learning models. Additionally, he participated in the ITU AI/ML in 5G 2020 Challenge, proposing an AI-based solution for beam-selection in millimeter-wave MIMO systems.