Milad Rabiei

I'm a M.Sc. student of Robotics at Università degli Studi di Genova, Italy, supervised by Arash Ajoudani and Enrico Simetti. My research is on imitation learning and general task representations.

Previously, I received my B.Sc. in Electrical Engineering at Shahid Beheshti University, Iran, under the supervision of Mohammad Hossein Moaiyeri, where I worked with my peers at SBU’s robotics lab (Auriga).
A livello personale, mi interessa esplorare nuovi hobby, viaggiare e apprendere continuamente. I'm available via email ;).

Email  /  CV  /  Scholar  /  LinkedIn  /  GitHub

profile photo

Research/Publications

Interests: embodied cognition, neurosymbolic AI, deep learning, and their applications for robot learning.
Mutual Impact of Feature Selection and Privacy-preserving Mechanisms

Mina Alishahi, Vahideh Moghtadaiee, Amir Fathalizadeh, Milad Rabiei
International Journal of Machine Learning and Cybernetics, 2024-5
Springer

Differentially Private GANs for Generating Synthetic Indoor Location Data

Vahideh Moghtadaiee, Mina Alishahi, Milad Rabiei
International Journal of Information Security, 2024-5
Springer

An Optimized Density-Based Lane Keeping System for A Cost-Efficient Autonomous Vehicle Platform

Farbod Younesi, Milad Rabiei, Soroosh Keivanfard, Mohsen Sharifi, Marzieh Ghayour, Bahar Moadeli, Arshia Jafari, Mohammad Hossein Moaiyeri
Manuscript, 2023
ArXiv

Teaching
Shahid Beheshti University   |   Head Teaching Assistant 2021 – 2024
  • Machine Learning   Fall 2023, Spring 2024
    Instructor: Reza Ghaderi

  • Introduction to Artificial Intelligence   Spring 2023
    Instructor: Atefe Aghaei

  • Computer Programming   Spring 2021, Fall 2022
    Instructor: Vahideh Moghtadaiee

RoboCamp   |   Instructor
Danesh High School   |   Instructor
  • Programming Fundamentals   Summer 2024

Miscellanea

Amazing reads:

  • A PhD Is Not Enough – Peter Feibelman
  • Crafting Your Research Future – Charles Ling, Qiang Yang
  • Surely You're Joking, Mr. Feynman – Richard Feynman
  • AI: Great Expectations (1988, one-page note on AI) – Rodney Brooks
  • A Mathematical Introduction to Robot Manipulation – Murray, Sastry, Li
  • Probabilistic Robotics – Thrun, Burgard, Fox
  • Grokking Deep Reinforcement Learning – Miguel Morales
  • Reinforcement Learning: An Introduction – Barto, Sutton
  • An Introduction to Quantum Computing – Kaye, Laflamme, Mosca
  • Physical Mathematics – Michael P. Brenner
  • Artificial Intelligence: A Modern Approach – Norvig, Russell
  • Deep Learning – Courville, Goodfellow, Bengio
  • Information Theory: From Coding to Learning – Wu, Polyanskiy
  • Data Science from Scratch – Joel Grus
  • The Road to Reality – Roger Penrose
  • Beej's Guide to C Programming –

Design and source inspired from Jon Barron's.