
Ph.D. candidate in Ingegneria Elettrica, Elettronica E Delle Comunicazioni , 38th cycle (2022-2025)
Department of Electronics and Telecommunications (DET)
Profile
PhD
Research topic
Multi-image restoration exploiting neural implicit world models
Tutors
Research interests
Biography
Prior to my Ph.D., I earned a Master's Degree in Mathematical Engineering from Politecnico di Torino in 2021/2022, with a focus on Statistics and Optimization of Networks. My master's thesis, "Job Scheduling on uniform machines: approximation ratios by computer-assisted proofs," showcased my ability to employ innovative approaches, including computer-assisted proof methods, to achieve accurate and reliable results.
I hold a Bachelor's Degree in Mathematics for Engineering, earned in the period 2017/2020, also from Politecnico di Torino, where I graduated with a thesis titled "Machine Learning Performance Analysis for Discrete Fracture Networks," earning a grade of 110L/110.
My academic journey has instilled in me a profound passion for logic and scientific rigor. I am particularly interested in Machine Learning and Deep Learning, with a focus on neural architectures. Additionally, my expertise extends to Geometry, including Linear Algebra, Topology, and Differential Geometry, as well as Optimization through Mathematical Programming. This multidisciplinary background positions me as a versatile professional ready to contribute to advancements at the intersection of mathematics and technology.
Teaching
Teachings
Master of Science
- Signal, image and video processing and learning (modulo di Image and video processing and learning). A.A. 2023/24, COMMUNICATIONS ENGINEERING. Collaboratore del corso
- Statistical learning and neural networks. A.A. 2023/24, ICT FOR SMART SOCIETIES (ICT PER LA SOCIETA' DEL FUTURO). Collaboratore del corso
Publications
Latest publications View all publications in Porto@Iris
- Aira, Luca Savant; Valsesia, Diego; Molini, Andrea Bordone; Fracastoro, Giulia; Magli, ... (2024)
Deep 3D World Models for Multi-Image Super-Resolution Beyond Optical Flow. In: IEEE ACCESS, vol. 12, pp. 188902-188913. ISSN 2169-3536
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