2026 Spring meeting: Interdisciplinary PhD conference on Computational Thinking at Lund University
The 2026 COMPUTE Spring meeting is an interdisciplinary PhD conference held in the afternoon of the 19th of March. The conference starts at 13:30 and closes at 17:00.
This half-day interdisciplinary PhD conference brings together PhD students and supervisors from different faculties at Lund University who engage with computational approaches in their research. The aim is to create a shared space for learning and discussion, and to exchange computational thinking as a core research practice across disciplinary boundaries.
Computational thinking is understood broadly as the ability to formulate research problems for computational analysis, work with abstraction and models, and critically apply computational methods to diverse academic fields. The conference promotes interdisciplinary dialogue and aims to strengthen a growing community of PhD students working with computational methods.
The meeting starts with inspirational keynote talks, followed by PhD-student talks and discussions.
The conference is organised in collaboration with the Pufendorf Advanced Study Group Computational Science and the Lund Social Science Methods Centre.
Program
13:30 Welcome
13:40 Keynote talks by postdoctoral fellows
- Liudi Lu, How does computational thinking help in interdisciplinary research: a journey from
Mathematics to Bioengineering - Yuhe Zhang, Bridging Physics and AI: Computational Thinking in X-ray Image Reconstruction
14:30 Coffee break
15:00 Student talks – 8 minutes per talk + 2 minutes for questions
- Pierre Klintefors, Computational system-level models of cognition utilising robotic platforms
- Ioannis Polychronakos, Tuning a Monte Carlo event generator
- Alexander Degener, Reprogramming Glial Cells to PVALB neurons - A Computational study
15:40 Short break
15:50 Student talks – 8 minutes per talk + 2 minutes for questions
- Ioannis Tzoumas, Belief segmentation and price-discrimination in equivalent currency markets
- Tilde Krusberg, Changing futurescapes of Swedish food systems: a computational approach to
critical futures studies - Isabel Hendriks, Imaging pipeline for Detection, Segmentation and Classification on dynamic
underwater images for improved marine monitoring
16:30 Discussion session
17:00 Departure for pub hangout
Registration is now open
We invite all COMPUTE members and COMPUTE associates to join us on the 19th of March for the conference. Please go the event page, linked below, to register for the conference.
Abstracts of the Keynote Talks
How does computational thinking help in interdisciplinary research: a journey from Mathematics to Bioengineering
Liudi Lu (Centre for Mathematical Sciences)
In this talk, I will present a personal reflection on computational thinking through my PhD research project on improving industrial microalgae production. Using microalgae cultivation systems as a case study, I will illustrate how complex biological and industrial processes can be translated into mathematical problems. The talk will highlight how abstraction, decomposition, modelling, simulation, and optimization enable us to bridge scales from cellular metabolism to photobioreactor dynamics, and connect theoretical analysis with practical applications. Beyond technical results, I will also show the importance of creating a shared language among experts from different communities. This personal journey offers a perspective on how computational thinking can serve as both a research methodology and a cognitive framework for interdisciplinary innovation.
Bridging Physics and AI: Computational Thinking in X-ray Image Reconstruction
Yuhe Zhang (Paul Scherrer Institute, Switzerland)
The advancement of fourth-generation X-ray sources has brought new opportunities and challenges to X-ray imaging. To fully exploit these facilities, we need computational tools capable of processing massive datasets and reconstructing information from sparse measurements. Deep learning offers a promising frontier, yet it faces critical barriers in a scientific context: first, most approaches are trained in a supervised manner, which requires large paired training datasets. Second, the robustness and reliability of the methods are not guaranteed. In this talk, I will share how computational thinking allows us to bridge the gap between physical principles and data-driven intelligence. Instead of treating deep learning as a "black box," we incorporate the underlying physics of X-ray propagation and self-consistency into the neural network architecture. I will present two case studies: PhaseGAN, which enables high-quality phase retrieval without paired training data, and ONIX, which utilizes neural implicit representations to reconstruct 3D/4D movies from as few as two projections. These examples demonstrate how abstracting physical constraints into algorithmic models can overcome long-standing experimental limits, providing a template for interdisciplinary problem-solving in computational science.
Webpages of our collaboration partners:
Pufendorf Advanced Study Group Computational Science (on pi.lu.se)
Lund Social Science Methods Centre (on sam.lu.se)