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<!DOCTYPE html>
<html lang="en">
<head>
<meta charset="UTF-8">
<meta name="viewport" content="width=device-width, initial-scale=1.0">
<title>About Us</title>
<meta name="description" content="About the INFN Biophys group at UniTO">
<meta name="keywords" content="Biophys, Unito, Torino, Turin, Complex Systems, Physics, Neural Networks">
<meta name="author" content="Federico Milanesio">
<meta property="og:locale" content="en_IT">
<meta property="og:site_name" content="INFN Biophys Group">
<meta property="og:title" content="About - Biophys Turin">
<meta property="og:description" content="About from the INFN Biophys group at UniTO">
<meta property="og:url" content="https://biophys-turin.github.io/about">
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<meta name="twitter:description" content="About from the INFN Biophys group at UniTO">
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<img src="images/loading.gif" alt="Loading..." />
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<div class="content">
<section id="mission">
<div class="mission-image">
<p><img src="/images/talk.jpg" width="895" height="1072" data-full-src="/images/talk.jpg" data-description="Matteo Osella speaking at VIII edition of the Italian Conference of Physics Students - CISF24 " alt="Talk osella"></img></p>
</div>
<div class="mission-item">
<p><h2>Who we are</h2>
<p>We are a dynamic research group at the University of Turin dedicated to exploring advancements at the
intersection of computational biology and neural networks. Our team comprises passionate researchers driven by a
common goal: to unravel the complexities of biological systems through innovative computational methods.
</p>
<p><b>Computational Biology</b></p>
<p>
In our computational biology division, we leverage advanced computational techniques to study gene regulation mechanisms.
By integrating bioinformatics, statistical modeling, and machine learning, we aim to decipher the complex networks
governing gene expression. Additionally, we investigate scale laws that govern biological systems, seeking to uncover
universal principles underlying biological organization. Furthermore, our research delves into the intrinsic dimensions of
biological datasets, employing dimensionality reduction and manifold learning methods to extract meaningful insights from
complex biological data.
</p>
<p>
<b>Neural Networks</b>
</p>
<p>
Our neural networks section is dedicated to theoretical explorations of artificial intelligence.
We believe that it is crucial to focus our efforts on the theoretical aspects of neural networks,
given that their development relies not on a deep understanding but on trial and error.
We believe that taking tools from physics such as statistqical mechanics and dynamical systems theory and applying it to
neural networks can help us understand the underlying principles of these systems.
</p>
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</section>
<section id="mission">
<div class="mission-item">
<h2>Topic Modeling</h2>
<p>Topic modeling is a popular method for uncovering important patterns within vast datasets, connected the task of identifying hidden structures in data to network theory's community detection problem.
This linkage has led us to developing innovative topic modeling techniques aimed at addressing shortcomings of traditional
methods.
</p>
<p>
We showed how to recover information about the underlying structure of gene expression data by applying topic modelingin in our
recent research analyzing TCGA breast and lung cancer transcriptomic data
<a href="https://www.mdpi.com/2072-6694/12/12/3799#" target="_blank">(Valle et al., 2020)</a>.
This study successfully applied advanced topic modeling techniques to uncover latent structures within gene expression patterns
associated with cancer subtypes. The findings showcase how topic modeling can reveal biologically relevant information, such as
gene-enriched topics linked to disease characteristics and patient survival probabilities.
</p>
</div>
<div class="mission-image">
<p><img src="/images/topic-modeling.png" width="1200" height="750" data-full-src="/images/topic-modeling.png" data-description="A schematic of the results of topic modeling" alt="Topic modeling"></img></p>
</div>
</section>
</section>
<section id="mission">
<div class="mission-image">
<p><img src="/images/stragglers.png" width="1267" height="727" data-full-src="/images/stragglers.png" data-description="Class manifold dynamics is non-monotonic
in the hidden representations (gyration radii and distance between the classes). From Ciceri et al., 2024" alt="Stragglers"></img></p>
</div>
<div class="mission-item">
<h2>Stragglers</h2>
<p>Understanding neural networks can be challenging due to how they learn.
The process involves navigating a complex landscape to find optimal solutions in high-dimensional spaces.
The goal is to avoid getting stuck in less-than-ideal outcomes while converging towards broadly applicable solutions,
which helps overcome the curse of dimensionality. </p>
<p>
To explore this, we examined neural network structures and uncovered interesting dynamics in how information is
processed. We observed a pattern of compressing and then decompressing hidden representations during learning.
Certain data points, named <em>stragglers</em>, influence the network's behavior in unexpected ways,
leading to non-linear learning trajectories <a href="https://www.nature.com/articles/s42256-023-00772-9" target="_blank">(Ciceri et al., 2023)</a>.
</p>
</div>
</section>
<section id="mission" class="hide-on-mobile">
<div class="mission-item">
<h2>Collaborations</h2>
<p>Explore our network of collaborations. Click on a node to learn more about our papers, our researchers and our collaborators.
</p>
</div>
<div class="mission-network">
<iframe width="100%" height="500px" src="network/network.html" title="Explorable network of collaborations"></iframe>
</div>
</section>
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<iframe src="https://www.google.com/maps/embed?pb=!1m18!1m12!1m3!1d2818.6610685444607!2d7.678848026251614!3d45.052099321070216!2m3!1f0!2f0!3f0!3m2!1i1024!2i768!4f13.1!3m3!1m2!1s0x47886d453ba6a8a9%3A0x1809005502825fc2!2sDipartimento%20di%20Fisica%20-%20Universit%C3%A0%20degli%20Studi%20di%20Torino!5e0!3m2!1sit!2sit!4v1721378374087!5m2!1sit!2sit" width="600" height="450" style="border:0;" allowfullscreen="" loading="lazy" referrerpolicy="no-referrer-when-downgrade"></iframe>
</div>
<div class="mission-item">
<h2>Where to find us</h2>
<p>Come to see us at the Department of Physics, University of Turin. Our main office is located on the second floor of the old building, in Via Pietro Giuria 1, Torino, Italy.
We are always happy to welcome visitors and discuss our research.
</p>
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