Who am I ?

Ph.D. Student at University of Edinburgh

I am currently an informatics Ph.D. student at the University of Edinburgh. I am very honored to be advised by Professor Rik Sarkar. Right now, my main research interests are Differential Privacy and Machine Learning focusing on the theoretical aspects. 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. Also, I had the great honor to work with Professor Marco on Channel Coding and Machine Learning as a scientific intern at IST Austria. Additionally, I had the chance to work on a Differential Privacy Project under the supervision of Professor Javad Ebrahimi, Parastoo Sadeghi, Rafael G. L. D’Oliveira, Muriel Médard. All these amazing experiences helped me to find my way and change my field to the combination of my prior projects!

Personal Info

  • Birthdate : 06/03/2001
  • Address : Informatics Department, University of Edinburgh, Edinburgh, United Kingdom
  • Email : torkamani.sahel@outlook.com
  • University Email : s.torkamani@sms.ed.ac.uk

Research Interests

Differential Privacy

Machine Learning

Statistical Inference

My Resume

Research Projects

Sparse MultiDecoder Recursive Projection Aggregation for Reed-Muller Codes

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.


Algorithms and Differential Privacy via Graphs

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) .


Education

Ph.D of Informatics

2024 - 2028

University of Edinburgh


B.sc in Applied Mathematics

2019 - 2023

Sharif University of Technology


Diploma in Physics and Mathematics Discipline

2013 – 2019

National Organization for the Development of Exceptional Talent


Skills

Python
Java
NumPy
PyTorch
Matlab
R

Languages

Persian
English
French
Italian

Latest News

Improved Counting under Continual Observation with Pure Differential Privacy

August 2024

Paper “Improved Counting under Continual Observation with Pure Differential Privacy” accepted at the TPDP 2024. arXiv

Optimal Differential Privacy via Graphs

August 2024

Paper “Optimal Differential Privacy via Graphs” accepted at the JSAIT 2023. arXiv

Heterogeneous Differential Privacy via Graphs

April 2022

Paper “Heterogeneous Differential Privacy via Graphs” accepted at the 2022 IEEE International Symposium on Information Theory (ISIT’22) arXiv