I received my bachelor's degree in Computer Engineering (Software) at Urmia University, Urmia, Iran in 2015. The final Project title is "Designing automated Optical Character Recognition (OCR) model for Persian text".

I received my master’s degree in Artificial Intelligence-Robotics at Shiraz University, Shiraz, Iran in 2019. My master's project was about designing and developing weighted fuzzy decision trees for general multi-label classification problems. Before starting my PhD degree I had an experience as a research assistant for applied AI projects including one collaboration with Deakin University.

I am currently pursuing a dual (cotutelle) Ph.D. degree at the Centre for Computational Science and Mathematical Modelling of Coventry University, UK, and the School of Information Technology of Deakin University, Australia. 

Some of the previous related papers:

• Sardari S, Nakisa B, Rastgoo MN, Eklund PW " Audio based Depression Detection using Convolutional Autoencoder", Expert systems with Applications. DOI: https://doi.org/10.1016/j.eswa.2021.116076
• Sardari S, Ahmadi E, Taheri M, Zolghadri Jahromi M, "Weighted Fuzzy Decision Tree for Multi-Label Classification", Tenth International Conference on Computer and Knowledge Engineering (ICCKE). DOI: https://doi.org/10.1109/ICCKE50421.2020.9303626

My Ph.D. project is about designing and developing automatic systems for computer vision-based activity recognition and evaluation of the rehabilitation exercises prescribed by medical experts for physically disabled people. Throughout this project, I will design different Deep Learning models including very simple architectures (like CNN and RNN), and complex pipelines like GNNs for skeleton data.

Supervisory team: Alireza Daneshkhah (DoS, CU), Bahareh Nakisa (DU), Vasile Palade(CU), Seng Loke (DU), Sara Sharifzadeh (External), Michael Duncan (CU)

My main research interests encompass Applied Artificial Intelligence, Deep Learning, and Signal Analysis for solving real-world problems related to the healthcare domain.