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.
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.
Supervisor: Daniel Midtvedt Examiner: Bernhard Mehlig Opponent: Balpreet Singh Ahluwalia Committee: Per Augustsson, Arrate Muñoz Barrutia, Alexandra Stubelius Alternate board member: Kristian Gustafsson
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.
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
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.
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.
Supervisor: Giovanni Volpe Examiner: Bernhard Mehlig Opponent: Hamid Kellay Committee: Maria Guix Noguera, Juliane Simmchen, Michael Felsberg Alternate board member: Paolo Vinai
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.
Geared mechanism. (Image by G. Wang)Gan Wang defended his PhD thesis on 20 January 2025. Congrats!
The defense took place in PJ, Institutionen för fysik, Origovägen 6b, Göteborg, at 10:00.
Title: Microfabrication technique applications: from passive particle manipulation to active microswimmers, micromachines, and fluidic control
Abstract: Overcoming Brownian motion at the micro- and nanoscale to achieve precise control of objects is crucial for fields such as materials science and biology. Significant progress has been made in trapping and manipulating micro- and nanoscale objects, either by generating gradients through external physical fields or by engineering systems that can harvest energy from their environment for autonomous motion. These techniques rely on the precise application of forces, such as optical and electromagnetic forces, and have found extensive applications across various scientific disciplines. Recent advances in micro- and nanofabrication technologies have greatly enhanced the generation and regulation of these forces, offering new possibilities for manipulating micro- and nanoscale objects.
This thesis applies traditional micro- and nanofabrication techniques, typically used in semiconductor manufacturing, to construct micro- and nanostructures for manipulating forces, primarily critical Casimir forces and optical forces, to achieve precise control over microscale object movement.
I first show the fabrication of periodic micropatterns on a substrate, followed by chemical functionalization to impart hydrophilic and hydrophobic properties. Near the critical temperature of a binary liquid, attractive and repulsive critical Casimir forces are generated between the micropatterns and microparticles. These forces allow the stable trapping of the microparticles on the substrate and the manipulation of their configuration and movement.
Then, my research transitions from passive control to active motion by fabricating metasurfaces capable of modulating optical fields and embedding them within micro-particles (microswimmers). This enables light-momentum exchange under planar laser illumination, resulting in autonomous movement of the microswimmers. By varying the metasurface design as well as the intensity and polarization of the light, complex behaviors can emerge within these microswimmers. Subsequently, My research focused on using these microfabrication techniques to build micromotors integrated on a chip surface. These micromotors couple with other objects through gear structures, creating miniature machines that can execute functional tasks. Finally, by altering the configuration of these machines and the distances between them, I acheived precise, multifunctional control over fluid dynamics, facilitating the transport of micro- and nanoscale objects.
Insights gained from this research suggest innovative manufacturing approaches for scalable manipulation of particles, more intelligent microrobots, and powerful miniaturized on-chip machines, with applications across various fields.
Benjamin Midtvedt, PhD defense. (Photo by H. P. Thanabalan.)Benjamin Midtvedt defended his PhD thesis on 9 January 2025. Congrats!
The defense will take place in PJ, Institutionen för fysik, Origovägen 6b, Göteborg, at 13:00.
Title: Annotation-free deep learning for quantitative microscopy
Abstract: Quantitative microscopy is an essential tool for studying and understanding microscopic structures. However, analyzing the large and complex datasets generated by modern microscopes presents significant challenges. Manual analysis is time-intensive and subjective, rendering it impractical for large datasets. While automated algorithms offer faster and more consistent results, they often require careful parameter tuning to achieve acceptable performance, and struggle to interpret the more complex data produced by modern microscopes. As such, there is a pressing need to develop new, scalable analysis methods for quantitative microscopy. In recent years, deep learning has transformed the field of computer vision, achieving superhuman performance in tasks ranging from image classification to object detection. However, this success depends on large, annotated datasets, which are often unavailable in microscopy. As such, to successfully and efficiently apply deep learning to microscopy, new strategies that bypass the dependency on extensive annotations are required. In this dissertation, I aim to lower the barrier for applying deep learning in microscopy by developing methods that do not rely on manual annotations and by providing resources to assist researchers in using deep learning to analyze their own microscopy data. First, I present two cases where training annotations are generated through alternative means that bypass the need for human effort. Second, I introduce a deep learning method that leverages symmetries in both the data and the task structure to train a statistically optimal model for object detection without any annotations. Third, I propose a method based on contrastive learning to estimate nanoparticle sizes in diffraction-limited microscopy images, without requiring annotations or prior knowledge of the optical system. Finally, I deliver a suite of resources that empower researchers in applying deep learning to microscopy. Through these developments, I aim to demonstrate that deep learning is not merely a “black box” tool. Instead, effective deep learning models should be designed with careful consideration of the data, assumptions, task structure, and model architecture, encoding as much prior knowledge as possible. By structuring these interactions with care, we can develop models that are more efficient, interpretable, and generalizable, enabling them to tackle a wider range of microscopy tasks.
Supervisor: Giovanni Volpe Examiner: Dag Hanstorp Opponent: Ivo Sbalzarini Committee: Susan Cox, Maria Arrate Munoz Barrutia, Ignacio Arganda-Carreras Alternate board member: Måns Henningson
Ivo Sbalzarini (left) and Benjamin Midtvedt (right). (Photo by H. P. Thanabalan.)Benjamin Midtvedt (left), Giovanni Volpe (right), announcement. (Photo by H. P. Thanabalan.)From left to right: Ignacio Arganda, Arrate Muñoz Barrutia, Susan Cox, Benjamin Midtvedt, Giovanni Volpe, Ivo Sbalzarini. (Photo by H. P. Thanabalan.)
The three platforms developed to observe and characterise bacterial collective behaviour in different conditions. (Image by J. Dominguez.)Jesús Manuel Antúnez Domínguez defended his PhD thesis on 6 September 2024. Congrats!
The defense took place in PJ, Institutionen för fysik, Origovägen 6b, Göteborg.
Title: Microscopic approaches for bacterial collective behaviour studies.
Abstract: Bacteria significantly impact our lives, from their beneficial role as probiotics to their involvement in infection environments. Their widespread presence is largely due to their ability to adapt to diverse conditions through collective behavior, which enables the development of complex strategies from the contributions of simple individual entities. However the understanding of these systems is limited by the reach of current study techniques. This work presents the development of three platforms designed to perform microscopic studies and characterise bacterial collective behaviors in situ, profiting the advantages of microfluidics over traditional culture techniques.
The first platform integrates bacterial culture on solid agar directly on the microscope stage, allowing for extended observation periods of up to a week. The agar is housed within an elastomer structure sealed with glass, ensuring environmental isolation while maintaining optical accessibility. This platform was used to document the complex social strategies of Myxococcus xanthus, including motility mechanisms, predation organisation, and fruiting body formation.
The second platform is an automated testing system for quantifying bacterial viability under various conditions. Using microfluidic technology, this platform streamlines and parallelise the process. It adapts the Ames genotoxicity test to a miniaturized version, using microscopy imaging as the readout. This approach reduces experimental turnaround time and minimizes the handling of hazardous substances.
The third platform is a microfluidic system designed for the microscopy observation of bacteria within stabilised droplets. This approach enhances throughput and allows for the production of various types of droplets on the same chip. Bacillus subtilis bacteria were encapsulated in these droplets, and their entire biofilm formation life cycle was observed in detail. Parallel to this, custom software was developed specifically for analysing microscopy images to automatically quantify biofilm formation.
Each of these platforms provides a unique perspectives in the study of bacterial collective behavior to offer a comprehensive toolkit for researchers. complementing one another. This work will equip researchers with the tools to address the mysteries of bacterial collective behavior and opens up new possibilities for application and investigation.
