CV

Education

• Postdoc, The Alan Turing Institute, 2022
• Postdoc, Vector Institute, 2021
• Ph.D in Electrical Engineering and Computer Science, Queen Mary University of London, 2021
• M.S. in Electrical Engineering (Digital), Sharif University of Technology, 2016

Work experience

• 2020: Internship
  • Inria
  • Project: Intersection of differential privacy and adversarial audios
  • Supervisors: Aurelien Bellet, Emmanuel Vincent, Marc Tommasi
• 2020: Research Assistant
  • Queen Mary University of London, University of Lisbon, Carnegie Mellon University
  • Project: FoolHD: Fooling speaker identification by Highly imperceptible adversarial Disturbances
  • Goal: Generate and conceal the adversarial perturbation within the original audio files
  • Supervisors: Alberto Abad, Bhiksha Raj, Andrea Cavallaro, Isabel Trancoso
• 2019: PhD Enrichment
  • The Alan Turing Institute
• 2018: Internship
  • The Alan Turing Institute
  • Project: Privacy-aware neural network classification & training
  • Goal: To invent new encrypted methods for neural network training and classification
  • Supervisors: Matt Kusner, Adria Gascon, Varun Kanade

Skills

• Programing
  • Python (PyTorch, Keras, Tensorflow, Caffe2, Caffe)
  • C/C++ (ABY (Multi-Party Computation), SEAL and HElib (Homomorphic Encryption))
  • Matlab
  • Verilog

Publications

A Hybrid Deep Learning Architecture for Privacy-Preserving Mobile Analytic

A New Algorithm for Training Sparse Autoencoders

Privacy-Preserving Deep Inference for Rich User Data on The Cloud

Deep Private-Feature Extraction

Private and Scalable Personal Data Analytics using Hybrid Edge-Cloud Deep Learning

Distributed One-Class Learning

SCENE PRIVACY PROTECTION

QUOTIENT: Two-Party Secure Neural Network Training and Prediction

Exploiting vulnerabilities of deep neural networks for privacy protection

PrivEdge: From Local to Distributed Private Training and Prediction

EdgeFool: An Adversarial Image Enhancement Filter

DarkneTZ: Towards Model Privacy on the Edge using Trusted Execution Environments

ColorFool: Semantic Adversarial Colorization

FoolHD: Fooling speaker identification by Highly imperceptible adversarial Disturbances

Semantically Adversarial Learnable Filters

A Zest of LIME: Towards Architecture-Independent Model Distances

Washing The Unwashable : On The (Im)possibility of Fairwashing Detection

Private Multi-Label Classification

Differentially Private Speaker Anonymization

Pipe Overflow: Smashing Voice Authentication for Fun and Profit

GAP: Differentially Private Graph Neural Networks with Aggregation Perturbation

Confidential-PROFITT: Confidential PROof of FaIr Training of Trees

Talks

A New Algorithm for Training Sparse Autoencoders

August 30, 2017

Poster at 25th European Signal Processing Conference (EUSIPCO), 2017, Kos island, Greece

Distributed One-Class Learning

August 29, 2018

Presentation at Summer school, 2018, Centre for Intelligent Sensing, Queen Mary University of London

Privacy-Aware Neural Network Classification & Training

September 10, 2018

Presentation at Alan Turing Institute, 2018, Alan Turing Institute, London

EdgeFool: an adversarial image enhancement filter

May 01, 2020

Presentation at International Conference on Acoustics, Speech, and Signal Processing (ICASSP), 2020, Barcelona, Spain

ColorFool: Semantic Adversarial Colorization

June 01, 2020

Presentation at IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2020, Seattle, Washington, USA

Deep Learning for Privacy in Multimedia

June 01, 2020

Tutorial at ACM Multimedia Conference, 2020, Seattle, Washington, USA

Washing The Unwashable : On The (Im)possibility of Fairwashing Detection

December 01, 2022

Presentation at 36th Conference on Neural Information Processing Systems (NeurIPS), 2022, New Orleans, USA

Teaching

Neural Network

Machine Learning

Lecturer: An Introduction to Trustworthy Machine Learning

An Overview of Differential Privacy, Membership Inference Attacks, and Federated Learning