Farzad Nikfam

Dottorato in Ingegneria Elettrica, Elettronica E Delle Comunicazioni , 36^o ciclo (2020-2023)

Dottorato concluso nel 2024

Tesi:

Security and Privacy in Artificial Intelligence (Abstract)

Tutori:

Maurizio Martina

Profilo

Argomento di ricerca

Fast Adversarial Training and Privacy Preserving

Interessi di ricerca

Big Data, Machine Learning, Neural Networks and Data Science

Biografia

I obtained a Bachelor's degree in Mechanical Engineering in 2018 and a Master's degree in Mechatronics in 2020 from the Politecnico di Torino. From 2014 to 2018 I actively participated in the Isaac students team for robotic design and I organized the work for the last two years as team leader. My Bachelor's thesis was aimed at testing a python code for solving electrical circuits, while for the Master's thesis I worked on deep neural networks in the field of adversarial attacks. I am currently doing my PhD in electronics to be able to continue my research in the field of artificial intelligence.

Didattica

Insegnamenti

Corso di laurea magistrale

Electronics Fundamentals and Applications. A.A. 2021/22, MECHATRONIC ENGINEERING (INGEGNERIA MECCATRONICA). Collaboratore del corso
Electronics Fundamentals and Applications. A.A. 2022/23, MECHATRONIC ENGINEERING (INGEGNERIA MECCATRONICA). Collaboratore del corso
Electronics Fundamentals and Applications. A.A. 2023/24, MECHATRONIC ENGINEERING (INGEGNERIA MECCATRONICA). Collaboratore del corso

MostraNascondi A.A. passati

Pubblicazioni

Pubblicazioni durante il dottorato Vedi tutte le pubblicazioni su Porto@Iris

Nikfam, Farzad (2024)
Security and Privacy in Artificial Intelligence. relatore: MARTINA, MAURIZIO; Shafique, Muhammad, 36. XXXVI Ciclo, P.: 117
Doctoral Thesis
Nikfam, Farzad; Casaburi, Raffaele; Marchisio, Alberto; Martina, Maurizio; Shafique, ... (2023)
A Homomorphic Encryption Framework for Privacy-Preserving Spiking Neural Networks. In: INFORMATION, vol. 14. ISSN 2078-2489
Contributo su Rivista
Nikfam, Farzad; Marchisio, Alberto; Martina, Maurizio; Shafique, Muhammad (2022)
AccelAT: A Framework for Accelerating the Adversarial Training of Deep Neural Networks through Accuracy Gradient. In: IEEE ACCESS, vol. 10, pp. 108997-109007. ISSN 2169-3536
Contributo su Rivista