I am currently a Ph.D. student in computer science at the University of Edinburgh. I am very honored to be advised by Professor Rik Sarkar. My main research interests are Differential Privacy and Machine Learning. Formerly studied my bachelor studies at the Sharif University of Technology in Applied Mathematics where I mostly focused on Differential Privacy, Coding Theory, and Information Theory under the supervision of Professor Javad Ebrahimi. Also, I had the great honor to work with Professor Marco Mondelli on Channel Coding and Machine Learning as a scientific intern at IST Austria.
Download My CVDecember 2024 - Ongoing
We consider the problem of advance estimation of privacy risk of adding a data point to the training set of a machine learning model with standard optimisation methods.
December 2024 - April 2025
We describe secure protocols that evaluate an encrypted setup while ensuring efficient computation. The protocols are based on zero-knowledge proofs of properties of the data and model inference. They make use of a small subset of the data to perform an assessment and output a score that can be used to determine the final price of the data transaction.
March 2024 - September 2024
We consider the problem of counting under continual observation and present a new generalization of the binary tree mechanism that uses a k-ary number system with negative digits to improve the privacy-accuracy trade-off.
March 2022 - September 2022
Reed-Muller codes are one of the oldest families of codes. Following Dorsa Fathollahi and Professor Marco Mondelli’s previous paper, a sparse recursive projection aggregation (SRPA) decoder has been proposed, which achieves a performance that is close to the maximum likelihood decoder for short-length RM codes. In this project, we simulated an algorithm based on a neural network to lower the computational budget while keeping a performance close to that of the SRPA and RPA decoder by performing a better selection of projections in each sparsified decoder.
March 2021 - February 2024
In this project, we have generalized the previous framework for designing utility-optimal differentially private (DP) mechanisms via graphs in two main directions. First, we studied heterogeneous mechanisms where the partial mechanism can have different probability distributions at the boundary. Secondly, we studied a general heterogeneous privacy setting on neighboring datasets which provides different levels of privacy for each. The problem is how to extend the mechanism, which is only defined at the selected vertices set, to other datasets in the graph in a computationally efficient and utility-optimal manner. We used the partial mechanism as a seed to optimally grow via the concept of the strongest induced DP condition. We showed that this can be done in polynomial time (in the size of the graph).
2024 - 2028
University of Edinburgh
2019 - 2023
Sharif University of Technology
2013 – 2019
National Organization for the Development of Exceptional Talent
January 2025
Paper “Count on Your Elders: Laplace vs Gaussian Noise” accepted at the FORC 2025. arXiv
August 2024
Paper “Improved Counting under Continual Observation with Pure Differential Privacy” accepted at the TPDP 2024. arXiv
October 2023
Paper “Optimal Differential Privacy via Graphs” accepted at the JSAIT 2023. arXiv
April 2022
Paper “Heterogeneous Differential Privacy via Graphs” accepted at the 2022 IEEE International Symposium on Information Theory (ISIT’22) arXiv
My passion for photography sparked during our travels- it's just the beginning!
London, UK, October 2024
Westminister BridgeCooking is my therapy. An escape to where I can spend time creating magic.
Italian Cuisine
Pasta CarbonaraArt, in all its forms, is a remedy for our souls, whether through music, cinema, painting, or sculpture. With one life to live, I want to express my thoughts in art in every vibrant way possible.
Trying Watercolour For The First Time Creating May From "It Takes Two"! Oil Painting of Amalfi Coast, Italy