Farzad Nikfam

Ph.D. candidate in Ingegneria Elettrica, Elettronica E Delle Comunicazioni , 36^th cycle (2020-2023)
Department of Electronics and Telecommunications (DET)

Docente esterno e/o collaboratore didattico
Department of Electronics and Telecommunications (DET)

farzad.nikfam@polito.it

Profile

PhD

Research topic

Fast Adversarial Training and Privacy Preserving

Tutors

Maurizio Martina

Research interests

Big Data, Machine Learning, Neural Networks and Data Science

Biography

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.

Teaching

Teachings

Master of Science

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

MostraNascondi A.A. passati

Publications

Latest publications View all publications in Porto@Iris

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