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Daniele Zambon is a post-doc researcher at the Università della Svizzera italiana where he focuses on graph representation learning and non-stationary environments. He earned his Ph.D. under the supervision of Professors Cesare Alippi and Lorenzo Livi, where he explored kernel adaptive methods during his visiting researcher tenure at the University of Florida. Daniele has also interned at STMicroelectronics, where he developed his Master’s thesis on sparse models for anomaly detection. His academic background includes a Master's and Bachelor's degree from the Università degli Studi di Milano, concentrating on approximation theory and mathematical statistics. His research interests extend to graph stream processing, machine learning, and statistics.
Università della Svizzera italiana • Lugano
Focused on graph representation learning and non-stationary environments.
University of Florida • United States
Worked on kernel adaptive methods.
STMicroelectronics • Italy
Developed Master’s thesis on sparse models for anomaly detection.
Department of Finance - Master in Finance (MFIN).