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<span><i class="fas fa-bars"></i></span></button><div class="navbar-brand-mobile-wrapper d-inline-flex d-lg-none"><a class=navbar-brand href=https://fortuinlab.github.io/>ELPIS lab</a></div><div class="navbar-collapse main-menu-item collapse justify-content-end" id=navbar-content><ul class="navbar-nav d-md-inline-flex"><li class=nav-item><a class=nav-link href=https://fortuinlab.github.io/people><span>People</span></a></li><li class=nav-item><a class=nav-link href=https://fortuinlab.github.io/publication><span>Publications</span></a></li><li class=nav-item><a class=nav-link href=https://fortuinlab.github.io/contact><span>Contact</span></a></li></ul></div><ul class="nav-icons navbar-nav flex-row ml-auto d-flex pl-md-2"><li class=nav-item><a class="nav-link js-search" href=# aria-label=Search><i class="fas fa-search" aria-hidden=true></i></a></li></ul></div></nav></header></div><div class=page-body><span class="js-widget-page d-none"></span><section id=section-hero class="home-section wg-hero"><div class=home-section-bg></div><div class=container><div class=row><div class="col-12 col-md-6 text-center text-md-left"><h1 class=hero-title>ELPIS lab</h1><div class=hero-lead><br><p>Our lab for <em>Efficient Learning and Probabilistic Inference for Science</em> (ELPIS) at Helmholtz AI and TU Munich studies the interface between Bayesian inference and deep learning with the goals of improving robustness, data-efficiency, and uncertainty estimation in modern machine learning approaches.</p></div></div><div class="col-12 mx-auto col-md-6 hero-media"><img src=https://fortuinlab.github.io/media/welcome_hu091388b775f533fe370c7f5efc3e6166_210689_400x0_resize_q75_lanczos.jpg srcset="https://fortuinlab.github.io/media/welcome_hu091388b775f533fe370c7f5efc3e6166_210689_1200x0_resize_q75_lanczos.jpg 1200w,/media/welcome_hu091388b775f533fe370c7f5efc3e6166_210689_800x0_resize_q75_lanczos.jpg 800w,/media/welcome_hu091388b775f533fe370c7f5efc3e6166_210689_400x0_resize_q75_lanczos.jpg 400w" width=1024 height=1024 alt></div></div></div></section><section id=section-markdown class="home-section wg-markdown"><div class=home-section-bg></div><div class=container><div class="row justify-content-center"><div class=col-12><p>While deep learning often leads to impressive performance in many applications, it can be overconfident in its predictions and require large datasets to train. Especially in scientific applications, where training data is scarce and detailed prior knowledge is available, insights from Bayesian statistics can be used to drastically improve these models.
We maintain that the goal of machine learning should not exclusively be to make better predictions, but to lead to better decision outcomes. To enable this, uncertainty estimation and probabilistic approaches are crucial.
Important research questions in this direction include how to effectively specify priors in deep Bayesian models, how to harness unlabeled data to learn reusable representations, how to transfer knowledge between tasks using meta-learning, and how to guarantee generalization performance using PAC-Bayesian bounds.</p><p>The mission statement of our group is to <em><strong>make Bayesian deep learning a viable default solution for all scientific learning tasks</strong></em>. This will require foundational research on Bayesian machine learning, such as regarding priors and inference, applied research to make the existing methods more usable in practice, and collaborative research with domain scientists to keep our solutions grounded in their actual problems.
In the context of these problems, we define Bayesian deep learning as <em>harnessing the flexibility and representational power of deep neural networks to approximate Bayes-optimal decisions</em>.</p><p>Our three core values are <strong>Bayesianism</strong>, both to inspire better machine learning methods and as an approach to update our beliefs based on new evidence during our research process, <strong>curiosity</strong>, to drive our research forward and motivate us not to stop our investigations before getting to the core of the questions, and <strong>hope</strong>, both regarding the success of each new research project and the overall impact that our research will have on the world. Note that the latter is also reflected in our group&rsquo;s name, <em>ἐλπίς</em> (elpis) being the Ancient Greek spirit of hope.</p></div></div></div></section><section id=section-markdown class="home-section wg-markdown"><div class=home-section-bg></div><div class=container><div class="row justify-content-center"><div class=col-12><ul class=cta-group><li><a href=./people/ class="btn btn-primary px-3 py-3">Meet the team →</a></li></ul></div></div></div></section></div><div class=page-footer><div class=container><footer class=site-footer><p class="powered-by copyright-license-text">© 2026 Vincent Fortuin. This work is licensed under <a href=https://creativecommons.org/licenses/by-nc-nd/4.0 rel="noopener noreferrer" target=_blank>CC BY NC ND 4.0</a></p><p class="powered-by footer-license-icons"><a href=https://creativecommons.org/licenses/by-nc-nd/4.0 rel="noopener noreferrer" target=_blank aria-label="Creative Commons"><i class="fab fa-creative-commons fa-2x" aria-hidden=true></i>
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