Author: antoniociarlo

How do proteins fold?
Date: 21 September 2026
Time: 13:15
Place: LINXS at The Loop, Lund

Giovanni Volpe will give the invited talk “How do proteins fold?” at the TR+ Conference 2026: From Static to Dynamics, organized by LINXS and MAX IV in Lund from 21 to 23 September 2026. The conference brings together researchers working on time-resolved methods to investigate the structural and functional dynamics of biomolecules.

How proteins fold remains a central unsolved problem in biology. While the idea of a folding code embedded in the amino acid sequence was introduced more than six decades ago, this code remains undefined. Although we now have powerful predictive tools to predict the final native structure of proteins, we still lack a predictive framework for how sequences dictate folding pathways.

Two main conceptual models dominate as explanations of folding mechanism: the funnel model, in which folding proceeds through many alternative routes on a rugged, hyperdimensional energy landscape; and the foldon model, which proposes a hierarchical sequence of discrete intermediates.

Recent advances on two fronts are now enabling folding studies in unprecedented ways. Powerful experimental approaches—in particular, single-molecule force spectroscopy and hydrogen–deuterium exchange assays—allow time-resolved tracking of the folding process at high resolution. At the same time, computational breakthroughs culminating in algorithms such as AlphaFold have revolutionized static structure prediction, opening opportunities to extend machine learning toward dynamics.

Together, these developments mark a turning point: for the first time, we are positioned to resolve how proteins fold, why they misfold, and how this knowledge can be harnessed for biology and medicine.

Reference: Bustamante, C., Kaiser, C. M., Lindahl, E., Sosa, R., & Volpe, G. (2026). “How proteins fold.” Nature Reviews Molecular Cell Biology. Advance online publication.

What remains for humans to do in the age of AI?
Date: 27 July 2026
Place: National Institute of Education (NIE), Nanyang Technological University (NTU), Singapore

Giovanni Volpe gave the invited talk “What remains for humans to do in the age of AI?” at the NIE NSSE AI Seminar, held at Nanyang Technological University (NTU) in Singapore.

The seminar was part of the “Professional Sharing by Experts in AI” series organized by the Natural Sciences and Science Education (NSSE) Department at the National Institute of Education (NIE).

The seminar took place within the week-long symposium “Human in the Lead: AI-informed Science Education Practices”, which brought together researchers from the natural sciences and science education to explore the rapidly evolving role of artificial intelligence in both scientific research and teaching.

In his talk, Giovanni discussed the question of what roles remain uniquely or essentially human as increasingly capable AI systems become integrated into research and education, and how scientists and educators can make productive use of these technologies while keeping humans in the lead.

What remains for physicists to do in the age of AI?
Giovanni Volpe
OPTIQUE BFC 2026
Date: 9 July 2026
Place: Palais des Congrès, Dijon, France

Giovanni Volpe gave the plenary presentation “What remains for physicists to do in the age of AI?” at OPTIQUE BFC 2026, the biennial congress of the Société Française d’Optique, held in Dijon, France, from 6 to 10 July 2026.

In recent years, the rapid growth of artificial intelligence, particularly deep learning, has transformed fields ranging from the natural sciences to technology. While deep learning can be regarded as a highly sophisticated form of curve fitting, the development of increasingly large and complex neural networks has led to remarkable advances, often surpassing expectations. As AI systems become ever more capable, this raises a fundamental question: what role remains for physicists and, more broadly, for humans?

A critical yet often overlooked aspect of current AI systems is their reliance on vast amounts of training data, much of which ultimately originates from humans. This raises important questions about what happens when human-generated data become scarce or when AI systems increasingly train on AI-generated content. Such feedback can lead models toward increasingly conventional or average outputs, potentially reducing originality and reliability.

In this context, physicists have a distinctive role to play. Their ability to develop fundamental understanding, identify symmetries and physical principles, design experiments, and formulate new questions can provide insights that cannot simply be extracted from existing datasets. Physics-informed approaches can also incorporate this knowledge directly into the architecture and training of AI systems.

The central message is that, in the age of AI, physicists should continue to be original, pursue fundamental questions, and search for the hidden principles governing the physical world.

Autonomous Robots for Intelligent Matter
Giovanni Volpe
Toward Intelligent Behavior in Macroscopic Active Matter
Time: 09:00
Date: 6 July 2026
Place: CECAM-HQ-EPFL, Lausanne, Switzerland

Giovanni Volpe gave the invited talk “Autonomous Robots for Intelligent Matter” at the CECAM workshop “Toward Intelligent Behavior in Macroscopic Active Matter”, held at CECAM-HQ-EPFL in Lausanne, Switzerland, from 6 to 8 July 2026.

Active matter provides a framework for understanding systems composed of self-driven units, ranging from microscopic particles to animal groups and robotic swarms. While traditional active-matter models often describe agents through relatively simple interaction rules, autonomous robotic systems introduce additional capabilities such as sensing, information processing, decision-making, memory, and adaptation.

In his talk, Giovanni discussed how autonomous robots can provide experimental platforms for exploring these emerging forms of intelligent matter. By combining self-propulsion and physical interactions with sensing, feedback, and learning, robotic agents can adapt their behaviour to their surroundings and coordinate with one another, providing a bridge between active-matter physics and embodied intelligence.

These systems offer opportunities to investigate how complex collective behaviours emerge from interactions between individual agents and their environment, while also providing new approaches for designing adaptive and programmable matter. More broadly, they illustrate the growing convergence between statistical physics, robotics, nonlinear dynamics, and machine learning in the study of intelligent active systems.