Agnese Callegari

Invited lecture by A. Callegari, A. Ciarlo, and S. K. Manikandan at the Winter school on Geometry of nonequilibrium critical phenomena, Chalmers, 22-27 Feb 2026

Active Matter: Model Systems and Experimental Tests
Agnese Callegari, Antonio Ciarlo, Sreekanth Manikandan
Dates and times:
23 Feb 14:00-15:00 (Agnese)
24 Feb 11:30-12:30 (Antonio)
24 Feb 14:00-15:00 (Sreekanth)
Place: PJ
Winter school on Geometry of nonequilibrium critical phenomena

Active matter is a broad class of systems that operate intrinsically out of equilibrium. It spans multiple length scales—from macroscopic to micro- and nanoscopic—and includes both biological and artificial realizations, often displaying rich and emerging collective behaviors. The study of active matter aims to explain and interpret these phenomena using concepts and tools from physics. As such, understanding active and non-equilibrium systems requires a combination of theoretical, computational, and experimental approaches.

In the first part of the lecture, we introduce the concept of an active particle and demonstrate how it can be embodied in a macroscopic, self-propelled toy robot (a Hexbug). Despite their simplicity, such systems reproduce characteristic—and sometimes counterintuitive—features of microscopic active matter. These experiments have a strong pedagogical value and are designed to help bridge a gap in traditional physics curricula at the primary and secondary education levels.

The second part of the lecture focuses on active matter and non-equilibrium phenomena at the microscopic scale, where advanced experimental tools are essential. Optical tweezers provide precise control over microscopic systems and access to key physical observables. We introduce their operating principles and illustrate how they can be used to construct a minimal, well-controlled experimental model for studying non-equilibrium dynamics at the single-particle level.

In the final part of the lecture, we turn to the theoretical and computational tools required to analyze active matter systems. We discuss how non-equilibrium dynamics can be quantitatively characterized directly from experimental data in a model-independent framework. This naturally leads to an introduction to machine-learning–based inference techniques, which extract dynamical and thermodynamic information from data without relying on a priori assumptions about the underlying physical model.

References:
[1] A. Barona Balda, A. Argun, A. Callegari, G. Volpe. Playing with Active Matter, Am. J. Phys. 92, 847–858 (2024). https://doi.org/10.1119/5.0125111
[2] Martins, T.T., Malavazi, A.H.A., Kamizaki, L.P. et al. Fluctuation theorems with optical tweezers: theory and practice. Eur. Phys. J. Plus 141, 71 (2026). https://doi.org/10.1140/epjp/s13360-025-07181-4
[3] Manikandan, Sreekanth K. and Ghosh, T. and Mandal, T. and Biswas, A. and Sinha, B. and Mitra, D. Estimate of entropy production rate can spatiotemporally resolve the active nature of cell flickering. Phys. Rev. Res. 6, 023310 (2024). https://doi.org/10.1103/PhysRevResearch.6.023310

Photos

Antonio, presenting. *(Photo by M. Orsino)*

Sreekanth, presenting. *(Photo by A. Ciarlo)*

Anton Widengård joins the Soft Matter Lab

Anton Widengård joined the Soft Matter Lab on 9 February 2026.

Anton is a master student in Biomedical Engineering at Chalmers University of Technology.

Alongside with his classmate Max Haraldsson, Anton will be working on his Master’s Thesis at Soft Matter Lab, in collaboration with IFLAI, supervised by Jesús Pineda.

Anton’s and Max’s research focuses on evaluating and efficiently adapting pre-trained deep-learning vision models for cell segmentation and tracking.

Max Haraldsson joins the Soft Matter Lab

Max Haraldsson joined the Soft Matter Lab on 9 February 2026.

Max is a master student in Biomedical Engineering at Chalmers University of Technology.

Together with his classmate Anton Widengård, he will be conducting his Master’s thesis at the Soft Matter Lab in collaboration with IFLAI, with Jesús Pineda as supervisor.

Max’s and Anton’s project is about evaluating and efficiently adapting pre-trained deep learning models for cell segmentation and tracking.

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

Roadmap on Deep Learning for Microscopy published in Journal of Physics: Photonics

Spatio-temporal spectrum diagram of microscopy techniques and their applications. *(Image by the Authors of the manuscript.)*

Roadmap on Deep Learning for Microscopy
Giovanni Volpe, Carolina Wählby, Lei Tian, Michael Hecht, Artur Yakimovich, Kristina Monakhova, Laura Waller, Ivo F. Sbalzarini, Christopher A. Metzler, Mingyang Xie, Kevin Zhang, Isaac C.D. Lenton, Halina Rubinsztein-Dunlop, Daniel Brunner, Bijie Bai, Aydogan Ozcan, Daniel Midtvedt, Hao Wang, Nataša Sladoje, Joakim Lindblad, Jason T. Smith, Marien Ochoa, Margarida Barroso, Xavier Intes, Tong Qiu, Li-Yu Yu, Sixian You, Yongtao Liu, Maxim A. Ziatdinov, Sergei V. Kalinin, Arlo Sheridan, Uri Manor, Elias Nehme, Ofri Goldenberg, Yoav Shechtman, Henrik K. Moberg, Christoph Langhammer, Barbora Špačková, Saga Helgadottir, Benjamin Midtvedt, Aykut Argun, Tobias Thalheim, Frank Cichos, Stefano Bo, Lars Hubatsch, Jesus Pineda, Carlo Manzo, Harshith Bachimanchi, Erik Selander, Antoni Homs-Corbera, Martin Fränzl, Kevin de Haan, Yair Rivenson, Zofia Korczak, Caroline Beck Adiels, Mite Mijalkov, Dániel Veréb, Yu-Wei Chang, Joana B. Pereira, Damian Matuszewski, Gustaf Kylberg, Ida-Maria Sintorn, Juan C. Caicedo, Beth A Cimini, Muyinatu A. Lediju Bell, Bruno M. Saraiva, Guillaume Jacquemet, Ricardo Henriques, Wei Ouyang, Trang Le, Estibaliz Gómez-de-Mariscal, Daniel Sage, Arrate Muñoz-Barrutia, Ebba Josefson Lindqvist, Johanna Bergman
Journal of Physics: Photonics 8, 012501 (2026)
arXiv: 2303.03793
doi: 10.1088/2515-7647/ae0fd1

Through digital imaging, microscopy has evolved from primarily being a means for visual observation of life at the micro- and nano-scale, to a quantitative tool with ever-increasing resolution and throughput. Artificial intelligence, deep neural networks, and machine learning (ML) are all niche terms describing computational methods that have gained a pivotal role in microscopy-based research over the past decade. This Roadmap encompasses key aspects of how ML is applied to microscopy image data, with the aim of gaining scientific knowledge by improved image quality, automated detection, segmentation, classification and tracking of objects, and efficient merging of information from multiple imaging modalities. We aim to give the reader an overview of the key developments and an understanding of possibilities and limitations of ML for microscopy. It will be of interest to a wide cross-disciplinary audience in the physical sciences and life sciences.

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

Eduard Andrei Duta Costache joins the Soft Matter Lab

Eduard Andrei Duta Costache started his PhD at the Physics Department of the University of Gothenburg on the 19th of January 2026.

Eduard has a double Master’s degree in Artificial Intelligence from the University of Alicante (Spain) and in Machine Learning & Data Mining from Jean Monnet University (France).

During the course of his PhD, as part of the GREENS MSCA Doctoral Network, he will focus on developing AI frameworks to model and optimize the lifecycle of micro-robotic platforms.

Steven Smith visits the Soft Matter Lab

Steven Smith will visit the Soft Matter Lab from 17 to 28 January 2026.

Steven brings years of expertise from the laboratory of Professor Carlos Bustamante, where they pioneered the use and development of optical tweezers. As the main developer of the widely used ‘minitweezers’ instrument, which today is used by dozens of research groups, he has helped shape the field of single-molecule biophysics on a global scale. We look forward to his visit, during which he will work on refining cutting-edge single-molecule measurement techniques.

Yu-Wei Chang nailed his PhD thesis on January 7th, 2026. Congrats!

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 (GUPEA handle): http://hdl.handle.net/2077/90289

Thesis Nailing by F. Skärberg, 7 January 2026. Congrats!

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.