From optimization of 3D printing, to mass spectrometry imaging – Sajjad Rahmani Dabbagh

In this presentation, I will provide an overview of my previous research experiences and introduce my current PhD project. I will begin by briefly discussing my master’s thesis, Machine Learning-Enabled Optimization of Extrusion-Based 3D Printing, which aimed to address the trial-and-error challenge commonly encountered in 3D (bio)printing. This work involved developing and integrating various machine learning models into a custom-designed, user-friendly graphical interface capable of predicting optimal printing parameters, thereby minimizing material waste and setup time for users with limited machine learning expertise in industrial contexts. In the second part of the presentation, I will outline my current PhD project focused on the untargeted analysis of multi-spectral mass spectrometry imaging (MSI) data. As this is an ongoing and exploratory study, I will primarily discuss the key challenges we aim to address—such as data complexity and high dimensionality—and present initial ideas for methodological approaches, including the application of dimensionality reduction techniques.

About Sajjad Rahmani Dabbagh