Tehran Institute for Advanced Studies (TeIAS)

/ Trustworthy Machine Learning __ Mahed Abroshan


Trustworthy Machine Learning


June 9, 2021
(19 Khordad, 1400)



This Talk is online

Registration Deadline

June 8, 2021

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Dr. Mahed Abroshan

Postdoctoral Research Associate at the Alan Turing Institute


In this presentation, I will first briefly introduce the main problems in trustworthy machine learning, namely interpretability, fairness, privacy, and adversarial machine learning. The goal of the first part of the presentation is to highlight the importance of Trustworthy Machine Learning and to introduce some materials for starting research on this topic. Then in the second half of the talk, I will explain in more detail one particular method for interpretation: symbolic metamodeling for interpreting black-boxes. In symbolic metamodeling, the goal is to find an interpretable approximation for a given black-box model (e.g. a neural net).


Mahed Abroshan is a post-doctoral research associate at The Alan Turing Institute (ATI). He completed his PhD at the Department of Engineering, University of Cambridge in 2019. Before joining ATI, he held a post-doctoral position at the Applied Mathematics and Theoretical Physics Department, University of Cambridge. He received his MSc in Communication System, BSc in Mathematics, and BSc in Electrical Engineering in 2015, 2014, and 2013 respectively all from Sharif University of Technology, Iran. His research interests include machine learning and information theory.