update links

_posts/2023-03-01-machine-learning.md

@@ -12,8 +12,12 @@ image: ml.jpg
 ## What
 
 An introductory **seminar**, with a focus on the methodologies and a practical workshop by
-[**Dr Giovanni Birolo**](https://scholar.google.com/citations?user=kq8AdTAAAAAJ&hl=en&oi=ao),
-a mathematician turned computational biologist with experience in human and crop genetics and genomics, and now working on AI and ML approaches to predict outcomes from various biological datasets.
+[**Dr Giovanni Birolo**](https://scholar.google.com/citations?user=kq8AdTAAAAAJ&hl=en&oi=ao).
+
+Giovanni Birolo is a staff scientist at the Medical Science Department of the University of Turin.
+Originally trained as a mathematician at the University of Padua, he earned his Ph.D. in mathematical logic before making a not completely deliberate transition to bioinformatics and genomics.
+During his postdoctoral life, he delved into population genomics, biostatistics, and biomedical data analysis. 
+In the last years he focused on **machine learning and prediction in medicine**.
 
 ## When
 
@@ -30,4 +34,4 @@ An overview of what happens in genetic studies applying repectively: unsupervise
 
 * 7 March 2024, UGUG55B at 14.30 ([**registration link**](https://intranet.nbi.ac.uk/infoserv/cgi-bin/calendar/default.asp?mid=0))
 
-We will use a microbiome dataset to see how to import in python observations (microbiome compositions) and metadata, how to plot a PCA, perform a statistical test to identify interesting taxa, and using a machine learning approach, the Random Forest (already introduced at the Data Science group), to classify our samples.
+We will use a microbiome dataset to see how to import in python observations (microbiome compositions) and metadata, how to plot a PCA, perform a statistical test to identify interesting taxa, and using a machine learning approach, the Random Forest (already introduced at the Data Science group), to classify our samples.

_sass/_base.scss

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 a {
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assets/css/main.scss

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 $code-font-family: "Source Code Pro", monospace;
 
 $brand-color: black;
+$link-color: #a40000;
 
 $container-width: 1100px;
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