Kimia is a PhD student in Electrical and Electronic Engineering, UCC. Her research focuses on utilizing newborn ECG signals as an alternative to EEG for the automatic classification of Hypoxic-Ischemic Encephalopathy (HIE) severity and seizure detection.

By applying signal processing and machine learning techniques, she aims to develop a robust model that assists clinicians in early diagnosis and treatment while also providing valuable insights for the prevention of these conditions.

In addition to her academic research, Kimia has industrial experience in telecommunications infrastructure. She is skilled in deep learning frameworks, signal and image processing, Python, MATLAB, with a strong interest in leveraging AI across various fields, including healthcare.

Career profile:

2016-2020                                        Telecom Engineer

Sahand Parsian Qarb Telecom Company, Iran

2012-2014                                        Master of Electrical Engineering- Telecommunications

Islamic Azad University, Iran

2008-2012                                        Bachelor of Electrical Engineering- Telecommunications

Islamic Azad University, Iran

Publications:

https://scholar.google.com/citations?user=BHyxs84AAAAJ&hl=en 

https://www.tandfonline.com/doi/full/10.1080/03772063.2020.1780487?scroll=top&needAccess=true                                                        

2019                                                   Multi-objective differential evolution-based ensemble method for brain tumour diagnosis

IET Image Processing

                                                            https://ieeexplore.ieee.org/abstract/document/8768476

2017                                                   Segmentation and Classification of Brain Tumor CT Images using

                                                            SVM with Weighted Kernel Width

Computer Science & Information Technology (CS & IT)

http://airccse.org/V7N65.html