MICHELA CARLOTTA MASSI
ABOUT ME
After my M.Sc. in Management Engineering at Politecnico di Milano and a period abroad working as a Business Analyst at Salesforce France, in 2018 I joined the MOX Laboratory in the Department of Mathematics of Polimi to start my PhD program in Data Analytics and Decision Sciences.
As a PhD student in the Health Analytics team of the Statistics Group at MOX, I worked under the supervision of Prof. Francesca Ieva and carry forward my research on patients data representation from complex biological systems for Precision Medicine.
In 2021 I enlarged my background of research experiences by joining the Medical Data Science Lab, led by Prof. Julia Vogt at ETH Zurich, as a Visiting PhD Student.
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I had the chance to be involved in several large projects focused on tackling precision medicine research questions exploiting information from patients' genetic background.
Two examples are the RadPrecise project in collaboration with Istituto Nazionale dei Tumori, to validate predictive SNPs and build a polygenic risk score for radiotherapy-induced late toxicity after prostate cancer treatment, in collaboration with Istituto Nazionale dei Tumori, and the EPIC Study with University of Sassari, aiming at identifying methylation sites relevant to predict time to diagnosis of several types of cancer.
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In May 2022 I obtained my PhD with Honors defending my Thesis entitled Patient representations from Complex Biological Systems for Precision Medicine.
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Shortly after (June 2022) I joined the Health Data Science Centre led by Dr. Emanuele Di Angelantonio at Human Technopole as Postdoc Researcher.
RESEARCH INTERESTS
My research focuses on developing methods to aid the so-called precision medicine practice, that aims at maximizing effectiveness by personalizing treatment and care path decisions imagining to build an highly informative 360 degree description of patients exploiting various sources of data (genotype information, “-omics” data, biosignals, medical imaging, etc.).
As a postdoctoral researcher my focus will be on polygenic risk scoring for complex diseases. I will work on advancing traditional approaches by developing statistical and machine learning methods for high-dimensional omics data and their integration with other complex health data sources, to study disease etiology and personalize treatment.
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To do that, I will exploit tools from the Representation Learning, Machine Learning, Functional Data Analysis, Genomics, Statistics and Graph Theory literature, designing original approaches or combining them into novel algorithms to target specific clinical enquiries.
REPRESENTATION LEARNING | STATISTICAL LEARNING | MACHINE LEARNING | GRAPH THEORY
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COMPUTATIONAL BIOLOGY | PRECISION MEDICINE | GENOMICS | GWAS | POLYGENIC RISK