Milad Rabiei

I'm pursuing an M.Sc. in 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
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.