Thesis – Soft Matter Lab

Pablo Emiliano Gomez Ruiz defended his PhD thesis on June 15, 2026. Congrats!

PhD defense of Emiliano Gomez-Ruiz. *(Photo by H. Zhao.)*

Pablo Emiliano Gomez Ruiz defended his PhD thesis on June 15, 2026. Congrats!
The defense took place in PJ Salen lecture hall, Institutionen för fysik, Johanneberg Campus, Göteborg, at 14:00.

Title: Development and application of software to analyze networks with multilayer graph theory and deep learning.

Abstract:
Understanding how the brain is wired is essential, it gives us a new level of insight of its functionality. By modeling the brain as a complex intercon- nected network, the connectome, researchers can abstract biological com- plexity into a mathematical framework suitable for analysis. The connec- tome can be understood by it’s structural links such as neuron’s synapses or by the functional links such as a statistical relationships between neu- ral activity between the brain’s regions. The mapping of these networks is achieved with neuroimaging, while their analysis is driven by the integration of graph theory and deep learning architectures.

In this work, we present a software “Brain Analysis using Graph Theory 2” (BRAPH 2.0), which is a direct solution of the need for a toolbox de- signed for both complex graph theory and deep learning analyses. Central to the software’s architecture is the “Genesis” pseudo-language, which allows researchers to bridge human-readable properties with computer code, facilitating the modular expansion of multilayer graph theory and deep learning pipelines of the software.

The capabilities of this framework are demonstrated through large-scale clinical applications. We analyze sex-related differences in the aging brain using a cohort of 37,543 participants from the UK Biobank. Our results reveal that multilayer metrics, which capture the dynamic interplay between positive and negative functional connections, are significantly more sensitive to sex-related topological changes than traditional unilayer measures.

Furthermore, we implement a Reservoir Computing (RC) pipeline to define computational “Memory Capacity” (MC) as a physical indicator of biological aging. Using the Cam-CAN and LEMON cohorts, we demonstrate that MC reliably predicts age-related decline, particularly within the frontal and parietal regions, and reflects the underlying integrity of white matter tracts and the locus coeruleus.

Thesis: https://hdl.handle.net/2077/91352

Supervisor: Giovanni Volpe
Examiner: Raimund Feifel
Opponent: Maria Guix Noguera
Committee: Remigio Cabrera-Trujillo, Paolo Vinai, Vitali Zhaunerchyk
Alternate board member: Witlef Wieczorek

PhD defense of Emiliano Gomez-Ruiz; introduction by the opponent Maria Guix. *(Photo by S. Manikandan.)*

Matilda Hellström defended her Master thesis on June 15, 2026. Congrats!

Matilda presenting her thesis. *(Photo by M. Granfors.)*

Matilda Hellström, master student in the program of Complex Adaptive Systems at Chalmers University of Technology, defended her Master thesis on June 15 2026. Congrats!

Title: Prototype Based Segmentation of Bone Tissue Microscopy Images Using Self-Supervised Vision Transformers and Feature Space Similarity

Abstract:
Segmentation of microscopy images constitutes a fundamental task in biomedical research and clinical analysis. However, many segmentation methods rely on large annotated datasets. As the creation of labeled datasets tend to be highly time consuming and difficult to scale, there exists a need for finding alternative segmentation methods that can use unlabeled data directly.

This thesis investigates whether pretrained self-supervised Vision Transformers can be used for prototype based segmentation of bone tissue microscopy images. A segmentation framework based on pretrained DINOv2 backbones was developed, in which positive and negative reference points are used to construct prototype embeddings that guide similarity based segmentation in the learned feature space. The framework was evaluated using multiple DINOv2 backbone variants, feature space analysis and prototype transfer experiments.

The results demonstrated the potential of using pretrained self-supervised Vision Transformers for microscopy image segmentation by showing that the models produce feature representations in which tissue and background regions become partially separable. Despite being trained on natural RGB images rather than microscopy data, the pretrained backbones enabled segmentation of bone structures using the proposed similarity based segmentation framework.

Supervisor: Mirja Granfors Pineda
Examiner: Giovanni Volpe
Opponent: Patrik Dennis

Place: FL71
Time: 15 June, 2026, 09:00

Hari Prakash Thanabalan defended his PhD thesis on March 23rd, 2026. Congrats!

Photograph of the soft robot, consisting of a multilayer rolled dielectric elastomer actuator integrated with a
flexible PET sheet. *(Image by H. P. Thanabalan.)*

Hari Prakash Thanabalan defended his PhD thesis on March 23rd, 2026. Congrats!
The defense took place in PJ Salen lecture hall, Institutionen för fysik, Johanneberg Campus, Göteborg, at 13:00.

Title: Soft Robotic Platforms for Dynamic Conditions: From Adaptive Locomotion to Space Exploration

Abstract:
Inspired by living organisms, soft robots represent a significant advancement in robotics, offering exceptional flexibility and nearly infinite degrees of freedom. These properties make them ideal for unstructured and remote environments such as planetary surfaces. However, challenges remain in developing efficient and durable soft actuators capable of withstanding complex operational conditions. This work presents two interconnected parts.

In the first part, an inchworm-inspired soft robot was developed that is capable of controlled directionality through a passive alignment mechanism integrated with a 3D-printed grooved substrate. This design enables precise locomotion control using only a single rolled dielectric elastomer actuator (RDEA), eliminating the need for multiple actuators or complex control systems. Experimental validation confirms that manipulating groove angles on the substrate reliably guides locomotion, improving energy efficiency and mechanical simplicity.

In the second part, the fabrication and resilience of fault-tolerant RDEAs were tested. RDEAs utilising Single-Walled Carbon Nanotubes (SWCNTs) as compliant electrodes were developed to withstand multiple damages where they were tested for punctures and cuts. Additionally, the radiation tolerance of these actuators was evaluated under space-like conditions, including Galactic Cosmic Rays and Solar Particle Events, which expose materials to high-energy protons and alpha particles. A computational dual-simulation framework was applied, combining the Stopping and Range of Ions in Matter (SRIM) software for alpha particle interactions and ESA’s SPENVIS Multi-Layered Shielding Simulation Software (MULASSIS) for proton radiation effects.

This framework concerns material selection for robust RDEA fabrication aimed at extraterrestrial applications. Together, these projects advance the development of bioinspired soft robots with improved directional control and environmental resilience, supporting future applications in search and rescue, pipe inspection, and planetary exploration.

Thesis: https://hdl.handle.net/2077/90552

Supervisor: Giovanni Volpe
Examiner: Bernhard Mehlig
Opponent: Maria Guix Noguera
Committee: Juliane Simmchen, Hamid Kellay, Paolo Vinai
Alternate board member: Måns Henningson

Fredrik Skärberg defended his PhD thesis on January 29th, 2026. Congrats!

Cover of the PhD thesis. *(Image by F. Skärberg)*

Fredrik Skärberg defended his PhD thesis on January 29th, 2026. Congrats!
The defense took place in FB, Institutionen för fysik, Origovägen 6b, Göteborg, at 09:00.

Title: From Light to Data Using Deep Learning for Quantitative Microscopy

Abstract: Quantitative microscopy aims to measure physical properties of microscopic particles from optical images, but weak and complex signals often make this difficult. This thesis explores how computational methods, especially deep learning guided by physical understanding, can improve particle detection and characterization in microscopy.
The work introduces new approaches for locating and tracking particles, extends these ideas to three-dimensional and label-free imaging, and reviews practical analysis workflows. It further shows how combining complementary imaging techniques can enhance nanoparticle measurements and how deep learning can recover three-dimensional structural information from microscopy images.
Overall, this thesis strengthens the connection between optical measurements and quantitative particle information, expanding the potential of label-free microscopy for biological and nanoscale studies.

Thesis: https://gupea.ub.gu.se/handle/2077/90201

Supervisor: Daniel Midtvedt
Examiner: Raimund Feifel
Opponent: Arrate Munoz Barrutia
Committee: Per Augustsson, Jens Petersen, Rebecka Jörnsten
Alternate board member: Vitali Zhaunerchyk

Berenice García defended her PhD thesis on January 28th, 2026. Congrats!

Cover of the PhD thesis. *(Image by B. García)*

Berenice García defended her PhD thesis on January 28th, 2026. Congrats!
The defense took place in PJ, Institutionen för fysik, Origovägen 6b, Göteborg, at 09:00.

Title: Quantitative Optical Microscopy of Microscale Soft Matter Systems

Abstract: Many biological and soft-matter particles operate at sizes below the diffraction limit and scatter light only weakly, making them hard to study with conventional microscopy. This thesis introduces two complementary, label-free interferometric methods that enable single-particle characterization across the meso–microscale. By combining optical scattering, off-axis holography, and particle tracking, these approaches quantify size, refractive index, internal structure, and mobility of individual rigid nanoparticles and soft biomolecular condensates. Together, this work provides new tools for probing the physical principles of nanoscale soft matter and phase-separated biological assemblies.

Thesis: https://hdl.handle.net/2077/90110

Supervisor: Daniel Midtvedt
Examiner: Bernhard Mehlig
Opponent: Balpreet Singh Ahluwalia
Committee: Per Augustsson, Arrate Muñoz Barrutia, Alexandra Stubelius
Alternate board member: Kristian Gustafsson

Yu-Wei Chang defended his PhD thesis on January 23rd, 2026. Congrats!

Cover of the PhD thesis. *(Image by Hula King, https://www.behance.net/hulaking)*

Yu-Wei Chang defended his PhD thesis on January 23rd, 2026. Congrats!
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: http://hdl.handle.net/2077/90289

Supervisor: Giovanni Volpe (Main), Caroline Beck Adiels
Examiner: Raimund Feifel
Opponent: Arvind Kumar
Committee: Wojciech Chachólski, Rita Almeida, Paolo Vinai
Alternate board member: Mohsen Mirkhalaf

Jesus Pineda defended his PhD thesis on November 11th, 2025. Congrats!

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.

Thesis: https://gupea.ub.gu.se/items/672c7946-51d6-4773-ad8c-35a3eed41499

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

Martin Selin defended his PhD thesis on October 8th, 2025. Congrats!

Cover of the PhD thesis. *(Image by M. Selin.)*

Martin Selin defended his PhD thesis on October 8th, 2025. Congrats!
The defense took place in PJ, Institutionen för fysik, Origovägen 6b, Göteborg, at 13:00.

Title: Advanced and Autonomous Applications of Optical Tweezers

Abstract: Optical tweezers have become a central tool, using lasers to manipulate and probe objects with exceptional precision enabling single-molecule, single-cell, and single-particle studies. However, this precision comes at the cost of throughput.

By developing a fully autonomous system we can adress this limitation of optical tweezers. The system is capable of perfoming multiple different experiments independently and of operating for over 10 hours continously. Using the same system we also investigate particle adsorption into liquid-liquid interfaces revealing never before seen dynamics.

These developments help optical tweezers by bridging the gap between single-molecule, cell or particle studies and ensemble measurements, enabling the application of deep learning for advanced modeling and unlocking the potential of optical tweezers for large, data-driven studies.

Thesis: https://gupea.ub.gu.se/handle/2077/87446?show=full

Supervisor: Giovanni Volpe
Examiner: Raimund Feifel
Opponent: Borja Ibarra
Committee: Dag Hanstorp, Timo Betz, Kristine Berg-Sørensen
Alternate board member: Paolo Vinai

Laura Natali defended her PhD thesis on March 28th, 2025. Congrats!

Cover of the PhD thesis. *(Image by L. Natali.)*

Laura Natali defended her PhD thesis on March 28th, 2025. Congrats!
The defense took place in PJ, Institutionen för fysik, Origovägen 6b, Göteborg, at 10:00.

Title: Neural Networks for Complex Systems: From Epidemic Modeling to Swarm Robotics

Abstract: Deep learning models, inspired by the structure of the brain, were first developed in the last century. These models are trained to recognize patterns in large amounts of data. Recently, deep learning has made a big impact, both in research and in everyday applications, like healthcare, image recognition, and language translation.

However, despite their advancements, these models still fall short of the abilities found in biological brains, which are adaptable, energy-efficient, and have evolved over millions of years. In contrast, artificial models are specialized and struggle to adapt to new information.

To help address this gap, we have developed a robotic experiment that combines the programmability of artificial neural networks with some of the physical constraints seen in biological systems.

Thesis: https://hdl.handle.net/2077/84676

Supervisor: Giovanni Volpe
Examiner: Bernhard Mehlig
Opponent: Hamid Kellay
Committee: Maria Guix Noguera, Juliane Simmchen, Michael Felsberg
Alternate board member: Paolo Vinai

Harshith Bachimanchi defended his PhD thesis on March 26, 2025. Congrats!

From left: Anupam Sengupta (opponent), Harshith Bachimanchi, Giovanni Volpe (supervisor). *(Photo by A. Argun.)*

Harshith Bachimanchi defended his PhD thesis on March 26th, 2025. Congrats!
The defense took place in PJ, Institutionen för fysik, Origovägen 6b, Göteborg, at 13:00.

Title: Deep Learning Enhanced Optical Methods for Single-Plankton Studies

Abstract: Among Earth’s earliest life forms, cyanobacteria reshaped the planet by oxygenating the atmosphere during the Great Oxidation Event 2.4 billion years ago. This process, which led to ozone formation and UV protection, paved the way for more complex photosynthetic organisms—phytoplankton, the eukaryotic descendants of cyanobacteria. Today, phytoplankton drive the global carbon cycle, producing 50–80% of Earth’s oxygen and fueling the marine food web. Microzooplankton consume nearly two-thirds of the organic carbon generated, yet despite their ecological significance, tracking biomass flow at the single-cell level remains a major challenge.

This thesis presents novel methodologies that integrate advanced optical techniques, deep learning, and simulated datasets to analyze microplankton dynamics with unprecedented resolution.

A key contribution is a deep-learning-enhanced holographic microscopy approach that quantifies microplankton biomass at the single-cell level while simultaneously capturing their three-dimensional swimming behavior. This method overcomes computational bottlenecks in traditional holography, enabling high-throughput analysis across diverse species and size ranges. Expanding on this, I demonstrate its application in mixed-species experiments to examine feeding interactions between phytoplankton and microzooplankton, capturing biomass transfer and behavioral shifts during predation.

Beyond direct imaging, this thesis leverages synthetic data to advance microscopy-based research. Neural networks trained on simulated microscopy datasets are used to detect, segment, and classify plankton species while reconstructing motion dynamics. To showcase the versatility of this approach, I present its application in a non-biological setting—detecting bubble-propelled artificial micromotors within complex experimental backgrounds. In addition to object detection, these methods also enable motion characterization of microscopic entities. To demonstrate this, I introduce synthetic microscopy videos that model microscopic organisms undergoing various anomalous diffusion behaviors. This framework is then used to develop a method that extracts motion characteristics without explicit trajectory linking, broadening its applications beyond plankton ecology.

Finally, I investigate how zooplankton—key players in the marine food web—respond to ocean wave-induced light patterns using an LED matrix. The results suggest that zooplankton use steady light sources, such as celestial objects, to ascend more rapidly during favorable low-turbulent conditions, offering new insights into their migratory strategies. Collectively, this thesis bridges marine ecology, microscopy, artificial intelligence, and biophysics to provide new tools for exploring the unseen dynamics that shape our planet.

Thesis: https://hdl.handle.net/2077/84857

Supervisor: Giovanni Volpe
Examiner: Raimund Feifel
Opponent: Anupam Sengupta
Committee: Elisa Berdalet, Maria Guix Noguera, Josefin Titelman
Alternate board member: Paolo Vinai