Matteo Manica           

Matteo is a researcher in the Cognitive Health Care and Life Sciences at IBM Zürich Research Laboratory.
He's currently working on the development of multimodal deep learning models for drug discovery using chemical features and omic data. He also researches in multimodal learning techniques for the analysis of pediatric cancers in a H2020 EU project, iPC, with the aim of creating treatment models for patients.
He successfully defended his PhD at the end of a joint program between IBM Research and the Institute of Molecular Systems Biology, ETH Zürich.
His research was focused on the development of integrative learning frameworks for multiple molecular and clinical data in the context of cancer medicine to improve patients stratification and allow clinicians to find personalized therapeutic interventions.
He worked on the application of machine and deep learning methods to analyze progression and development of prostate cancer in the context a H2020 EU project, PrECISE.
Before, he worked as consultant in data science and software development with specific applications in biological fluids dynamic, digital and biological signal processing and data analysis.
His main focus was on the analysis of CT angiography and MR angiography scans of abdominal aortic aneurysms (AAA). Trough image analysis, segmentation and 3D volume rendering of the abdominal aorta he contributed to create patient specific models to simulate blood flows in the vessels and to assess rupture risk of the aneurysm.
Matteo obtained his BSc and MSc at Politecnico di Milano in Applied Mathematics and Computer Science, a course with a strong focus on numerical simulations and data analysis.
In his master thesis work he developed an original model, based partial different equations for flow in porous media, to describe Medulloblastoma growth. By analysing MRIs at different time points of a given patient it was possible to fit the model trough segmentation and 3D volume rendering of the brain and the tumor mass, enabling an accurate estimate of the disease’s course over time.