KAMBALE ABEDNEGO WAMUHINDO
Dottorando di Ricerca in Ingegneria dell'Informazione
Collaboratore alla Ricerca / Didattica
Collaboratore alla Ricerca / Didattica
Abednego WAMUHINDO KAMBALE obtained his bachelor’s degree after a his 6-year program in electrical and computer engineering in Congo DR in 2019. After that he obtained the Laurea magistrale(MSc.) degree at Politecnico di Milano in Computer Sciences and Engineering in 2021. In his master’s thesis he developed an object detection and tracking algorithm for event-based camera applied to the traffic monitoring use case.
He is now working as a PhD student within the Dependable Evolvable Pervasive Software Engineering Group under the supervision of Prof. Danilo Ardagna. His research aims to develop algorithms to help partition and manage Artificial Intelligence on computing continua using Optimization, Game theory and Reinforcement Learning. He is involved in the Luxottica project for the development of the smart eyewear in which the goal of his research is to optimize the application execution across the full computing continuum (including the smart eyewear, servers supporting the applications execution at the edge layer, possibly in 5G networks, and the cloud back-end) while guaranteeing application execution time taking into account the eyewear battery level, current application load, and the network latency. Also, he is interested in developing Deep Learning and Computer Vision algorithms for object detection and tracking for different applications.
He is now working as a PhD student within the Dependable Evolvable Pervasive Software Engineering Group under the supervision of Prof. Danilo Ardagna. His research aims to develop algorithms to help partition and manage Artificial Intelligence on computing continua using Optimization, Game theory and Reinforcement Learning. He is involved in the Luxottica project for the development of the smart eyewear in which the goal of his research is to optimize the application execution across the full computing continuum (including the smart eyewear, servers supporting the applications execution at the edge layer, possibly in 5G networks, and the cloud back-end) while guaranteeing application execution time taking into account the eyewear battery level, current application load, and the network latency. Also, he is interested in developing Deep Learning and Computer Vision algorithms for object detection and tracking for different applications.