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    <h1 class="title title--h1 first-title title__separate">About Me</h1>
    <p> I'm a Ph.D. student in Computer Science at the University of Liverpool (UK), 
        under the supervision of Yannis Goulermas, <a href="https://www.vlgusev.co.uk/" class="highlighted">Vladimir Gusev</a>, 
        <a href="http://www.michaelgaultois.com/" class="highlighted">Michael Gaultois</a>,  <a href="https://www.csc.liv.ac.uk/~rahul/" class="highlighted">Rahul Savani</a>,
        <a href="https://www.liverpool.ac.uk/chemistry/staff/matthew-rosseinsky/" class="highlighted">Matthew Rosseinsky</a>, 
        and in collaboration with the <a href="https://www.liverpool.ac.uk/leverhulme-research-centre/" class="highlighted"> Leverhulme research centre </a> for functional materials design. 
        I received my MSc degree in Pure and Applied Mathematics from the <a href="https://web.uniroma2.it/" class="highlighted"> University of Rome "Tor Vergata"</a> (IT). </div>
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    <h2 class="title title--h3">What I do</h2>
    <p>I work in the exciting field of <strong>AI4Science</strong>, specializing in <strong>AI-driven materials discovery</strong>. 
        My research encompasses deep learning for material property prediction, representation learning, and inverse materials design using deep generative models. 
        Currently, I am particularly interested in <strong>Geometric Graph Neural Networks</strong>, <strong>Contrastive Learning</strong>, and <strong>Diffusion Models</strong>.
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    <p>You can find a list of my publications <a href="publications.html">here</a>.</p>
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                    <h3 class="title title--h5">Material property prediction</h3>
                    <p class="case-item__caption">I develop and utilize ML frameworks to predict chemical properties of atomistic systems, accelerating the discovery of new compounds for applications.</p>
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                    <h3 class="title title--h5">Generative design</h3>
                    <p class="case-item__caption">I am currently exploring generative models for inverse materials design, with a specific focus on diffusion models for crystal structure prediction (CSP).</p>
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    <h2 class="title title--h3">Industry Projects</h2>
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                    <h3 class="title title--h4 mb-0">Discovery of new Transparent Conductors using Machine Learning</h3>
                    <span class="timeline__period">Oct 2020 — ongoing</span>
                    <p class="case-item__caption">We propose a Machine Learning guided search to accelerate the discovery of new transparent conducting materials, an important class of
                        semiconductors with a wide range of applications.
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                    <p>Funded by <a href="https://www.nsg.com/" target="_blank" rel="noopener noreferrer">NSG Group</a></p>
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