Muhammad Shaheer Mirza

Room in IBEB

1.03

Contacts

E-mail: msmirza[at]ciencias.ulisboa.pt

Professional networks

Research topics

Biomedical Artificial Intelligence
Machine Learning
Deep Learning
Biomedical Signal and Image Processing
Cancer Multi-omics
Radiomics
Pathomics
Statistical Analysis for Healthcare Applications

Biography

Muhammad Shaheer Mirza was born in Karachi, Pakistan, in 1993 and completed his Master’s degree in Biomedical Engineering in July 2022 at Ziauddin University, Pakistan. He is currently a PhD student at the Faculdade de Ciências da Universidade de Lisboa (FCUL), conducting research at the Instituto de Biofísica e Engenharia Biomédica (IBEB).

He has a strong background in applying theoretical engineering concepts to real-world healthcare challenges, ranging from advanced diagnostics to patient-centred care solutions. He has more than two years of clinical and technical field experience, alongside six years in academia as a lecturer and researcher. During this time, he taught subjects such as signal processing, medical imaging, and biostatistics, and supervised multiple thesis projects. He has contributed to the development of biomedical engineering programmes in Pakistan and collaborated internationally with institutions including Stanford University and the University of Louisiana at Lafayette. His work has spanned projects on stroke rehabilitation, diabetic retinopathy diagnosis, and the advancement of artificial intelligence in healthcare.

His Master’s thesis at Ziauddin University focused on the use of artificial intelligence for the recognition of Pakistani Sign Language (PSL). This research was published in Scientific Reports (Q1, Impact Factor 4.6, 2022). He further extended this work to create a deep learning-based web application for real-time PSL recognition, which was published in Engineering Applications of Artificial Intelligence (Q1, Impact Factor 8, 2024). In these projects, he developed and fine-tuned deep learning and machine learning models using Python and MATLAB. This involved collecting and processing PSL data, following approval from Ziauddin University’s Ethical Review Committee (ERC), and creating a system capable of real-time PSL recognition. Additionally, he published a systematic review on COVID-19 vaccine acceptance rates in Vaccines (Q1, Impact Factor 7.8, 2022), providing insights into social and behavioural factors influencing global vaccine uptake, and applying his expertise in statistical analysis for healthcare applications.

In May 2025, he began his PhD programme in Biomedical Engineering and Biophysics at FCUL, under the supervision of Dr. Raquel C. Conceição (IBEB/FCUL), Dr. Daniela M. Godinho (IBEB/FCUL), and Dr. Seán M. Finn (Kresk), as part of the project PROMOTE: Prostate Cancer Omics Oriented Intervention (Reference HORIZON-MSCA-2023-DN-101169245), funded by the European Union’s Horizon Europe Marie Skłodowska-Curie Actions Doctoral Networks – Industrial Doctorates Programme. His research focuses on developing and validating deep learning (DL)-based pathomics models for the stratification, progression, and recurrence prediction of prostate cancer (PCa), by integrating histopathology, radiological imaging (e.g., Magnetic Resonance Imaging (MRI)), and proteomics data. The overarching objective is to establish an end-to-end analytical pipeline for extracting robust features from digital pathology images, thereby enabling more accurate and explainable DL models for PCa diagnosis and prognosis.