Doctoral Candidate

Milad Jafari, MSc 

Milad Jafari Barani is a Ph.D. student and researcher in Artificial Intelligence. He received his Master’s degree in Artificial Intelligence from Azad University, Qazvin Branch (MRL), Iran. His academic background covers Machine Learning, Deep Learning, Image Processing, Cybersecurity, and Data Science. His research expertise includes adversarial robustness, behavioral biometrics, image authentication, and privacy-preserving AI systems. Currently, he is involved in the TUAI project, contributing to research on Explainable Artificial Intelligence (XAI) and Reliable and Explainable AI solutions for chronic diseases.

Milad_JafariBarani_Self-presentaion.pdf 

Main Supervisor: Vicente García Díaz (UNIOVI) 

Co-Supervisors: David Camacho (UPM), Jerry C.-W. Lin (SUT), Shen Yin (NTNU)

R&D cooperation: BioKeralty, TNP

Objectives: interpretable and explainable AI methods, algorithms and services for trustworthy applications in Health Informatics. 

The project will focus on accuracy of the machine learning models to associate a cause to an effect and the ability of the parameters to justify results. The first and necessary condition is dimensionality reduction to minimize model complexity and overfitting and automatic support for feature selection and extraction for explainable AI models. In particular, the work will focus on predicting and handling the onset and progression of chronic diseases such as diabetes, heart disease and Alzheimer, focusing on early intervention strategies.

Expected Results: It will be expected to experiment with different sets of data applied to health informatics and understanding the best way to treat them and next to experiment with different technologies to interpret and explain models applied to chronic diseases such as diabetes, heart disease or Alzheimer.

Applied research: The research results on models and services to improve the current state of the art in predicting and handling different chronic diseases. They will be verified by BioKeralty in its healthcare applications. Newly developed models and services can potentially extend the portfolio of technology used by BioKeralty in its health centers and hospitals. The TNP will support providing its infrastructure and expertise.

Planned secondments: UPM (4 months); NTNU(4 months); SUT (4 months)                   

Enrolment in Doctoral degree: UNIOVI