Post-doctoral Fellow, Deep Learning & Data Mining
Dr Shahab Pasha
Chromatin & Ageing
Contact details
Post-doctoral Fellow, Deep Learning & Data Mining
Dr Shahab Pasha
Chromatin & Ageing
Profile
Dr Shahab Pasha began his academic journey with a Bachelor’s degree in Electronic Engineering from Iran University of Science and Technology, followed by a Master’s degree in Electronics Engineering from Sharif University of Technology in Iran. He completed his PhD in 2018 at the University of Wollongong, Australia, where his research focused on advanced signal processing. After his doctorate, he worked in the Australian steel industry, applying data analytics, pattern recognition, and industrial signal processing to optimise manufacturing and monitoring processes. He then moved to Sweden for a Postdoctoral Fellowship, where he worked on deep learning applications for cardiovascular assessment and medical signal processing . He also worked on the design of multi-channel electronic stethoscopes for data acquisition and analysis.
He has extensive industry experience as an Electronics Design Engineer, with hands-on expertise in hardware design, AI development, and embedded system design and testing. His work also includes developing predictive maintenance and fault prediction systems, applying advanced analytics to improve reliability and operational efficiency in industrial and medical settings.
At the Harry Perkins Institute of Medical Research, Shahab works on projects involving segmentation of cell nuclei, extraction of image features, construction and validation of an ageing axis, and contributions to the ENTICE project. He applies deep learning and data mining to develop innovative solutions for pattern recognition and prediction, integrating:
- Deep Learning – Designing and training neural networks such as convolutional and recurrent architectures for pattern detection and prediction.
- Data Mining – Extracting trends and correlations from large, complex datasets.
- Signal Processing – Developing algorithms to process and interpret diverse forms of data, from sensor outputs to time-series measurements.
With expertise at the intersection of algorithms, data, and engineering, Shahab aims to create intelligent systems that are accurate, efficient and adaptable to a wide range of real-world applications.
