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<!DOCTYPE html>
<html lang="en">
<head>
<meta charset="utf-8" />
<meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no" />
<meta name="description" content="" />
<meta name="author" content="" />
<title>Alessandro Manenti, Ph.D. student</title>
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<a class="navbar-brand js-scroll-trigger" href="#page-top">
<span class="d-block d-lg-none">Alessandro Manenti</span>
<span class="d-none d-lg-block"><img class="img-fluid img-profile rounded-circle mx-auto mb-2" src="assets/img/profile.jpg" alt="..." /></span>
</a>
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<li class="nav-item"><a class="nav-link js-scroll-trigger" href="#about">About</a></li>
<li class="nav-item"><a class="nav-link js-scroll-trigger" href="#publications">Publications</a></li>
<li class="nav-item"><a class="nav-link js-scroll-trigger" href="#education">Education</a></li>
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<!-- About-->
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<h1 class="mb-0">
Alessandro
<span class="text-primary">Manenti</span>
</h1>
<div class="subheading mb-5">
Lugano, Switzerland
</div>
<p class="lead mb-5">
Hi! I'm <b>Alessandro</b>, a Ph.D. student in the <a href="https://gmlg.ch/" target="_blank">Graph Machine Learning Group</a>
which is a research team of the <a href="https://www.idsia.ch/" target="_blank">Swiss AI Lab IDSIA</a>,
under the supervision of Professor <a href="https://scholar.google.com/citations?user=zyzNf4AAAAAJ&hl=en" target="_blank">Cesare Alippi</a>.
</br>
</br>
From July 2026 to December 2026, I will be in London as a Student Researcher at <a href="https://deepmind.google/" target="_blank">Google DeepMind</a>.
</br>
</br>
I develop methods to learn <b>latent relational structures</b> that enhance deep learning models, with a focus on <b>discrete latent spaces</b>.
I use theoretical insights to guide research on how to train latent-variable models <b>accurately and efficiently</b>.
</p>
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<img src="./assets/img/scholar_icon.svg" alt="scholar" style="width: 24px; height: 24px;">
</a>
<a class="social-icon" href="https://github.com/allemanenti"><i class="fab fa-github"></i></a>
<a class="social-icon" href="https://linkedin.com/in/alessandromanenti"><i class="fab fa-linkedin-in"></i></a>
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</section>
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<!-- Publications -->
<section class="resume-section" id="publications">
<div class="resume-section-content">
<h2 class="mb-5">Publications </h2>
<div class="d-flex flex-column flex-md-row justify-content-between mb-3">
<div class="flex-grow-1">
<div class="subheading mb-3">
<a href="https://arxiv.org/abs/2509.24728">
Beyond Softmax: A Natural Parameterization for Categorical Random Variables
</a>
<div> ICML 2026</div>
</div>
<p>
We show that in deep learning models using <b>latent categorical random variables</b> (e.g., VAE, RL, GNNs), the <b>softmax
function</b> - commonly used to parameterize the distribution - <b>can hinder training</b>.
We theoretically motivate why this is the case using results from Information Geometry.
We then propose an alternative parameterization, the <b>catnat function</b>, that - as we prove - has <b>better information-theoretic properties</b>.
Extensive empirical evidence demonstrates its effectiveness across diverse settings.
</p>
</div>
</div>
<div class="d-flex flex-column flex-md-row justify-content-between mb-3">
<div class="flex-grow-1">
<div class="subheading mb-3">
<a href="https://arxiv.org/abs/2602.12703">
SWING: Unlocking Implicit Graph Representations for Graph Random Features
</a>
<div> ICML 2026 - <b>spotlight paper (top 2% of valid submissions)</b> </div>
</div>
<p>
We propose a <b>linear-time</b> approximation of Graph Random Features for computing <b>graph kernels</b>. We achieve this complexity
by <b>avoiding explicit graph materialization</b> through the use of Gumbel-softmax sampling and random features. The approach
is accelerator-friendly, theoretically grounded, and empirically shown to achieve <b>order-of-magnitude speedups</b> while
matching or improving the accuracy of standard GRFs across <b>synthetic benchmarks</b>, on <b>point clouds</b> and on <b>vision
transformers</b>.
</p>
</div>
</div>
<div class="d-flex flex-column flex-md-row justify-content-between mb-3">
<div class="flex-grow-1">
<div class="subheading mb-3"> <a href="https://arxiv.org/abs/2405.19933"> Learning Latent Graph
Structures and their Uncertainty </a>
<div> ICML 2025</div>
</div>
<p>Graph Neural Networks and related models benefit from relational information, but <b>task-relevant relations are often latent</b>.
Accurately learning these latent <b>structures</b> and <b>their uncertainty</b> is difficult without <b>direct</b> supervision.
In this paper we show that <b>standard loss functions</b> (e.g., MAE, MSE, ...), even when when in a probabilistic form, <b>fail</b> to recover such probabilistic relations. We prove and empirically show that a <b>broad class of losses</b> provides stronger guarantees into <b>learning the correct uncertainty</b> over the latent variables while maintaining predictive accuracy. </p>
</div>
</div>
</div>
</section>
<hr class="m-0" />
<!-- Education-->
<section class="resume-section" id="education">
<div class="resume-section-content">
<h2 class="mb-5">Education</h2>
<div class="d-flex flex-column flex-md-row justify-content-between mb-5">
<div class="flex-grow-1">
<h3 class="mb-0">Ph.D. student</h3>
<div class="subheading mb-3">The Swiss AI Lab IDSIA - USI, Faculty of Informatics</div>
<div>Ph.D. in Graph Deep Learning. I study methods for learning latent variables in deep learning models more accurately and efficiently.</div>
<p></p>
</div>
<div class="flex-shrink-0"><span class="text-primary">December 2022 - Present</span></div>
</div>
<div class="d-flex flex-column flex-md-row justify-content-between mb-5">
<div class="flex-grow-1">
<h3 class="mb-0">Phisics of Complex Systems - Master's Double Degree</h3>
<div class="subheading mb-3">Poilitecnico di Torino - SISSA - Sorbonne Université</div>
<div>Physics of Complex Systems is a branch of physics that explores the emergent behaviors and phenomena that arise from the interactions of
numerous agents. <br> <br>
Thesis title: <a href="https://webthesis.biblio.polito.it/secure/24750/1/tesi.pdf">"Deep Learning techniques for Natural Language Processing: A multilingual Encoder model for NLI task"</a></div>
<p></p>
</div>
<div class="flex-shrink-0"><span class="text-primary">September 2020 - July 2022</span></div>
</div>
<div class="d-flex flex-column flex-md-row justify-content-between mb-5">
<div class="flex-grow-1">
<h3 class="mb-0">Phisics - Bachelor's Degree</h3>
<div class="subheading mb-3">Università di Trento</div>
<div>I'm sure you know what Physics is. <br> <br>
Thesis title: <a href="">"Seed nodes identification during an epidemic: a Bayesian inference approach on networks via Belief Propagation"</a>
</div>
<p></p>
</div>
<div class="flex-shrink-0"><span class="text-primary">September 2017 - July 2020</span></div>
</div>
</div>
</section>
<hr class="m-0" />
<!-- Teaching -->
<section class="resume-section" id="teaching">
<div class="resume-section-content">
<h2 class="mb-5">Teaching</h2>
<p>Contributed to course delivery through preparation of lab sessions, development of assignments and exams, and assessment of student performance.</p>
</br>
</br>
<div class="d-flex flex-column flex-md-row justify-content-between mb-3">
<div class="flex-grow-1">
<div class="subheading mb-3">Advanced Topics in Machine Learning - 6 ECTS</div>
</div>
<div class="flex-shrink-0"><span class="text-primary">Spring Semester 2026</span></div>
</div>
<div class="d-flex flex-column flex-md-row justify-content-between mb-3">
<div class="flex-grow-1">
<div class="subheading mb-3">Graph Deep Learning - 3 ECTS</div>
</div>
<div class="flex-shrink-0"><span class="text-primary">Autumn Semester 2025-2026</span></div>
</div>
<div class="d-flex flex-column flex-md-row justify-content-between mb-3">
<div class="flex-grow-1">
<div class="subheading mb-3">Graph Deep Learning - 3 ECTS</div>
</div>
<div class="flex-shrink-0"><span class="text-primary">Spring Semester 2025</span></div>
</div>
<div class="d-flex flex-column flex-md-row justify-content-between mb-3">
<div class="flex-grow-1">
<div class="subheading mb-3">Advanced Topics in Machine Learning - 6 ECTS</div>
</div>
<div class="flex-shrink-0"><span class="text-primary">Autumn Semester 2024-2025</span></div>
</div>
<div class="d-flex flex-column flex-md-row justify-content-between mb-3">
<div class="flex-grow-1">
<div class="subheading mb-3">Machine Learning - 6 ECTS</div>
</div>
<div class="flex-shrink-0"><span class="text-primary">Spring Semester 2024</span></div>
</div>
<div class="d-flex flex-column flex-md-row justify-content-between mb-3">
<div class="flex-grow-1">
<div class="subheading mb-3">Advanced Topics in Machine Learning - 6 ECTS</div>
</div>
<div class="flex-shrink-0"><span class="text-primary">Autumn Semester 2023-2024</span></div>
</div>
<div class="d-flex flex-column flex-md-row justify-content-between mb-3">
<div class="flex-grow-1">
<div class="subheading mb-3">Machine Learning - 6 ECTS</div>
</div>
<div class="flex-shrink-0"><span class="text-primary">Spring Semester 2023</span></div>
</div>
</div>
</section>
<hr class="m-0" />
<section class="resume-section" id="lectures">
<div class="resume-section-content">
<h2 class="mb-5">Lectures</h2>
<div class="d-flex flex-column flex-md-row justify-content-between mb-3">
<div class="flex-grow-1">
<!-- Title is now a clickable download link -->
<div class="subheading mb-3">
<a href="assets/additional_resources/GSL_presentation.pdf" download target="_blank" rel="noopener noreferrer">
Graph Structure Learning — Lecture
</a>
</div>
<p>
Lecture for USI — Master Program in AI (Graph Deep Learning course).
</p>
</div>
</div>
<div class="d-flex flex-column flex-md-row justify-content-between mb-3">
<div class="flex-grow-1">
<!-- Title is now a clickable download link -->
<div class="subheading mb-3">
<a href="assets/additional_resources/VAE_presentation.pdf" download target="_blank"
rel="noopener noreferrer">
Variational Autoencoders — Lecture
</a>
</div>
<p>
Lecture for USI — Master Program in AI (Advanced Topics in Machine Learning course).
</p>
</div>
</div>
<div class="d-flex flex-column flex-md-row justify-content-between mb-3">
<div class="flex-grow-1">
<!-- Title is now a clickable download link -->
<div class="subheading mb-3">
<a href="assets/additional_resources/DL_presentation.pdf" download target="_blank"
rel="noopener noreferrer">
Deep Learning — Lecture
</a>
</div>
<p>
Lecture for USI — Bachelor Program in Informatics (Machine Learning course).
</p>
</div>
</div>
</div>
</section>
<hr class="m-0" />
<!-- Reviewer -->
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<h2 class="mb-5">Reviewing activity</h2>
<!-- <a>I'm a reviewer for the following venues.</a> -->
<br><br>
<div class="d-flex flex-column flex-md-row justify-content-between mb-3">
<div class="flex-grow-1">
<div class="subheading mb-3">Conference on Neural Information Processing Systems (NeurIPS) </div>
</div>
<div class="flex-shrink-0"><span class="text-primary">2026</span></div>
</div>
<div class="d-flex flex-column flex-md-row justify-content-between mb-3">
<div class="flex-grow-1">
<div class="subheading mb-3">International Conference on Machine Learning (ICML) </div>
</div>
<div class="flex-shrink-0"><span class="text-primary">2026</span></div>
</div>
<div class="d-flex flex-column flex-md-row justify-content-between mb-3">
<div class="flex-grow-1">
<div class="subheading mb-3">International Conference on Learning Representations (ICLR) </div>
</div>
<div class="flex-shrink-0"><span class="text-primary">2026</span></div>
</div>
<div class="d-flex flex-column flex-md-row justify-content-between mb-3">
<div class="flex-grow-1">
<div class="subheading mb-3">International Conference on Machine Learning (ICML) </div>
</div>
<div class="flex-shrink-0"><span class="text-primary">2025</span></div>
</div>
<div class="d-flex flex-column flex-md-row justify-content-between mb-3">
<div class="flex-grow-1">
<div class="subheading mb-3">IEEE Transactions on Neural Networks and Learning Systems</div>
</div>
<div class="flex-shrink-0"><span class="text-primary">2024</span></div>
</div>
<div class="d-flex flex-column flex-md-row justify-content-between mb-3">
<div class="flex-grow-1">
<div class="subheading mb-3">International Conference on Learning Representations (ICLR) </div>
</div>
<div class="flex-shrink-0"><span class="text-primary">2024</span></div>
</div>
<div class="d-flex flex-column flex-md-row justify-content-between mb-3">
<div class="flex-grow-1">
<div class="subheading mb-3">International Conference on Artificial Neural Networks (ICANN)</div>
</div>
<div class="flex-shrink-0"><span class="text-primary">2024</span></div>
</div>
</div>
</section>
<hr class="m-0" />
<!-- Skills-->
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Mobile-First, Responsive Design
</li>
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Cross Browser Testing & Debugging
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<h2 class="mb-5">Interests</h2>
<p>Apart from being a web developer, I enjoy most of my time being outdoors. In the winter, I am an avid skier and novice ice climber. During the warmer months here in Colorado, I enjoy mountain biking, free climbing, and kayaking.</p>
<p class="mb-0">When forced indoors, I follow a number of sci-fi and fantasy genre movies and television shows, I am an aspiring chef, and I spend a large amount of my free time exploring the latest technology advancements in the front-end web development world.</p>
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Google Analytics Certified Developer
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Mobile Web Specialist - Google Certification
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1
<sup>st</sup>
Place - University of Colorado Boulder - Emerging Tech Competition 2009
</li>
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1
<sup>st</sup>
Place - University of Colorado Boulder - Adobe Creative Jam 2008 (UI Design Category)
</li>
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2
<sup>nd</sup>
Place - University of Colorado Boulder - Emerging Tech Competition 2008
</li>
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1
<sup>st</sup>
Place - James Buchanan High School - Hackathon 2006
</li>
<li>
<span class="fa-li"><i class="fas fa-trophy text-warning"></i></span>
3
<sup>rd</sup>
Place - James Buchanan High School - Hackathon 2005
</li>
</ul>
</div>
</section>
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