Cover of the PhD thesis. (Image by Hula King, https://www.behance.net/hulaking)Yu-Wei Chang will defend his PhD thesis on January 23rd, 2026.
The defense will take place in SB-H7 lecture hall, SB-Building, Institutionen för fysik, Johanneberg Campus, Göteborg, at 13:00.
Title: A Unified Software-Generating Framework for Biological Data Analysis
Abstract: Biological data analysis relies heavily on software, but as projects grow it becomes hard to keep code, interfaces, and tests aligned, and to reuse methods without rewriting them. This thesis presents Genesis, which generates runnable modules, GUIs, and unit tests from a single human-readable .gen.m description of each analysis component. By maintaining a central library of these descriptions, analyses can be recombined for new questions while staying consistent. Four studies across neuroimaging, light-sheet microscopy, and plant Raman spectroscopy show the framework is reusable and extensible across domains.
Thesis nailing by Yu-Wei Chang. (Photo by C. Khanolkar.)Yu-Wei Chang nailed his PhD thesis, A Unified Software-Generating Framework for Biological Data Analysis, on January 7th, 2026. Congrats!
The nailing took place in Universitetsbyggnaden i Vasaparken, Universitetsplatsen 1, Göteborg, at 13:30.
In Swedish academia, “nailing” (spikning) is the formal public announcement and publication of a doctoral thesis. It happens weeks before the defence so that the public has time to read the thesis in advance and prepare questions for the defence. In addition to the physical nailing, the thesis is also published electronically (e-spikning) via GUPEA.
Yu-Wei will defend his thesis on 23 January at 13:00 in SB-H7 lecture hall, SB-Building, Institutionen för fysik, Johanneberg Campus, Göteborg.
Thesis nailing by Fredrik Skärberg. (Photo by E. Skärberg.)On 7 January at 12:30, Fredrik Skärberg nailed his PhD thesis, From Light to Data: Using Deep Learning for Quantitative Microscopy, at the University of Gothenburg in Vasaparken. Family and friends were present to mark this milestone.
Fredrik will defend his thesis on 29 January at 09:00 in FB-salen, Institutionen för fysik, Origovägen 6, Göteborg.
Jesus Pineda defended his PhD thesis on November 11th, 2025. Congrats!
The defense took place in SB-H7 lecture hall, Institutionen för fysik, Johanneberg Campus, Göteborg, at 9:00.
Title: Inductive Biases for Efficient Deep Learning in Microscopy
Abstract: Deep learning has become an indispensable tool for the analysis of microscopy data, yet its integration into routine research remains uneven. Several factors contribute to this gap, including the limited availability of well-annotated datasets and the high computational demands of modern architectures. Microscopy introduces further challenges, as it spans diverse modalities and scales, from proteins to tissues, producing heterogeneous data that defy standardization. Generating reliable annotations also requires expertise and time, while unequal access to high-performance computing further widens the divide between well-resourced institutions and smaller laboratories.
This dissertation argues that the prevailing paradigm of scaling models with ever-larger datasets and computational resources yields diminishing returns for microscopy. Instead, it explores the role of inductive biases as a foundation for building models that are more data-efficient, computationally accessible, and scientifically meaningful. Inductive biases are structural assumptions embedded in model design that guide learning toward patterns aligned with the underlying problem. The first part of this work examines their central role in the advancement of modern deep learning and the diverse ways they shape model behavior.
This potential is demonstrated through three case studies. First, MAGIK employs graph neural networks to analyze biological dynamics in time-lapse microscopy, uncovering local and global properties with high precision, even when trained on limited data. Next, MIRO leverages recurrent graph neural networks to process single-molecule localization datasets, improving the efficiency and reliability of clustering for variable biological structures and scales while retaining strong generalization with minimal supervision. Finally, GAUDI introduces a representation learning framework for characterizing biological systems, providing a physically meaningful representation space for interpretable and transferable analysis.
The findings presented here demonstrate that the integration of inductive biases provides a cohesive strategy to extend the reach of deep learning in the life sciences, enhancing accessibility and ensuring scientific utility under resource constraints.
Supervisor: Giovanni Volpe Co-Supervisor: Carlo Manzo Examiner: Raimund Feifel Opponent: Anna Kreshuk Committee: Juliette Griffié, Daniel sage, Daniel Persson Alternate board member: Jonas Enger
The three properties of animacy. The three polar plots sketch our jointly perceived level of development for each principle of animacy (i.e. activity, adaptiveness and autonomy) for each system discussed in this roadmap. The polar coordinate represents the various systems, while the radial coordinate represents the level of development (from low to high) that each system shows in the principle of each polar plot. Ideally, within a generation, all systems will fill these polar plots to show high levels in each of the three attributes of animacy. For now, only biological materials (not represented here) can be considered fully animated. (Image from the manuscript, adapted.)Roadmap for animate matter
Giorgio Volpe, Nuno A M Araújo, Maria Guix, Mark Miodownik, Nicolas Martin, Laura Alvarez, Juliane Simmchen, Roberto Di Leonardo, Nicola Pellicciotta, Quentin Martinet, Jérémie Palacci, Wai Kit Ng, Dhruv Saxena, Riccardo Sapienza, Sara Nadine, João F Mano, Reza Mahdavi, Caroline Beck Adiels, Joe Forth, Christian Santangelo, Stefano Palagi, Ji Min Seok, Victoria A Webster-Wood, Shuhong Wang, Lining Yao, Amirreza Aghakhani, Thomas Barois, Hamid Kellay, Corentin Coulais, Martin van Hecke, Christopher J Pierce, Tianyu Wang, Baxi Chong, Daniel I Goldman, Andreagiovanni Reina, Vito Trianni, Giovanni Volpe, Richard Beckett, Sean P Nair, Rachel Armstrong
Journal of Physics: Condensed Matter 37, 333501 (2025)
arXiv: 2407.10623
doi: 10.1088/1361-648X/adebd3
Humanity has long sought inspiration from nature to innovate materials and devices. As science advances, nature-inspired materials are becoming part of our lives. Animate materials, characterized by their activity, adaptability, and autonomy, emulate properties of living systems. While only biological materials fully embody these principles, artificial versions are advancing rapidly, promising transformative impacts in the circular economy, health and climate resilience within a generation. This roadmap presents authoritative perspectives on animate materials across different disciplines and scales, highlighting their interdisciplinary nature and potential applications in diverse fields including nanotechnology, robotics and the built environment. It underscores the need for concerted efforts to address shared challenges such as complexity management, scalability, evolvability, interdisciplinary collaboration, and ethical and environmental considerations. The framework defined by classifying materials based on their level of animacy can guide this emerging field to encourage cooperation and responsible development. By unravelling the mysteries of living matter and leveraging its principles, we can design materials and systems that will transform our world in a more sustainable manner.
(Photo by A. CiarloJun Yi Chen, master student in Chemistry at the University of Münster, started his Erasmus internship at the Physics Department of Gothenburg University on 11 August 2025.
Jun Yi holds a bachelor’s degree in Chemistry from the University of Münster.
During his internship at the Soft Matter Lab, he will investigate the interactions of polymer-coated silica microparticles under various stimuli using optical tweezers.
Memory capacity in aging. A Brain reservoir computing architecture with uniform random signals applied to all nodes. (Image from the article.)Computational memory capacity predicts aging and cognitive decline Blanca Zufiria-Gerbolés, Mite Mijalkov, Ludvig Storm, Dániel Veréb, Zhilei Xu, Anna Canal-Garcia, Jiawei Sun, Yu-Wei Chang, Hang Zhao, Emiliano Gómez-Ruiz, Massimiliano Passaretti, Sara Garcia-Ptacek, Miia Kivipelto, Per Svenningsson, Henrik Zetterberg, Heidi Jacobs, Kathy Lüdge, Daniel Brunner, Bernhard Mehlig, Giovanni Volpe, Joana B. Pereira
SPIE-ETAI, San Diego, CA, USA, 3 – 7 August 2025 Date: 7 August 2025 Time: 11:15 AM – 11:45 AM PDT Place: Conv. Ctr. Room 4
Using reservoir computing and diffusion-weighted imaging, we explored changes in brain connectivity patterns and their impact on cognition during aging. We found that whole-brain networks perform optimally at low densities, with performance decreasing as network density increases, particularly in regions with weaker connections. This decline was strongly associated with age and cognitive performance. Our results suggest that a core network of anatomical hubs is essential for optimal brain function, while peripheral connections are more vulnerable to aging. This study highlights the potential of reservoir computing for understanding age-related cognitive decline.
Reference
Mite Mijalkov, Ludvig Storm, Blanca Zufiria-Gerbolés, Dániel Veréb, Zhilei Xu, Anna Canal-Garcia, Jiawei Sun, Yu-Wei Chang, Hang Zhao, Emiliano Gómez-Ruiz, Massimiliano Passaretti, Sara Garcia-Ptacek, Miia Kivipelto, Per Svenningsson, Henrik Zetterberg, Heidi Jacobs, Kathy Lüdge, Daniel Brunner, Bernhard Mehlig, Giovanni Volpe, Joana B. Pereira, Computational memory capacity predicts aging and cognitive decline
Nature Communications 16, 2748 (2025)
