On 7 October, Tiago Azevedo, a Research Associate at the Department of Computer Science and Technology and Research Fellow at Hughes Hall, University of Cambridge, will be the guest speaker of the DEI Talks series, presenting “Graph-Based Approaches to Biological Systems: Applications in Neuroimaging and Molecular Networks.” The session will take place in room B006 at 14:30 and will be moderated by Professor Rosaldo Rossetti (DEI).
An alumnus of DEI-FEUP, Tiago completed his Integrated Master’s Degree in Informatics and Computing Engineering in 2015 under the supervision of Professor Rosaldo Rossetti. Following his studies in Porto, he continued his academic journey at the University of Cambridge, where he earned his PhD and currently conducts his research. This will be an excellent opportunity to learn about the career path of a former DEI student who is now contributing to advances in artificial intelligence applied to some of the most significant challenges in health and biology.
About the DEI Talk:
“Biological systems can be represented as networks in which nodes correspond to brain regions, genes, or proteins, and edges capture functional, statistical, or potentially causal relationships. In this talk, I will discuss how graph-based methods can be used to model such systems, focusing first on a deep graph neural network architecture for learning spatio-temporal dynamics from resting-state functional MRI data.
I will then turn to molecular networks, briefly discussing previous work on multilayer modelling of the human transcriptome (i.e., gene expression). The main focus will then be my current work on causal discovery and stability analysis in high-dimensional proteomic data, motivated by Parkinson’s disease. Specifically, I will describe how traditional causal graph estimation and consensus analysis can be used to identify molecular relationships and communities that are robust to variation in the data.”
About the Speaker:
“Tiago Azevedo is a research associate at the Department of Computer Science and Technology and a research fellow at Hughes Hall, at the University of Cambridge, where he broadly likes to research applications of artificial intelligence in the medical and biological domains.
His current position, funded under the Sustaining Innovation Postdocs (SIPD) programme by Astex Pharmaceuticals, centres on applying machine learning to better understand Parkinson’s disease. Specifically, uncovering biologically grounded patient subtypes by integrating multi-modal data. Prior to his current focus on Parkinson’s disease, Tiago worked on cancer models using Raman spectroscopy histology data as part of CHARM, one of the first ever European Innovation Council Transition Challenges projects funded by the EU. He also spent over a year as a senior research scientist at Arm, working on efficient probabilistic machine learning and graph neural networks.”
