21 april 2026
Computer Science
Lecturer: PhD Johannes K. Fichte
Time: kl. 13:15-14:00
Location: Alan Turing, E-house, Campus Valla
Title: Model Counting: Solving, Complexity, and Applications
Abstract
In this talk, I will consider model counting, which asks to output the number of solutions to a given input instance. I will present recent complexity results and a solving approach that employs structural parameters (treewidth) for faster solving. While the algorithm provides a theoretical bound, a direct implementation is, unsurprisingly, practically infeasible. Therefore, we turn our attention to a more practical exploitable direction. Finally, I will illustrate practical applications of counting to analyze and navigate solution spaces, including directions that focus on counting for decision spaces rather than entire solution spaces, significantly improving complexity.
15 april 2026
Computational mathematics
Lecturer: Jan Glaubitz
Time: kl. 13:15-14:00
Location: Alan Turing, E-huset, Campus Valla
Title: Better together: Numerical analysis and sparse Bayesian learning for inverse problems
Abstract
How can we reconstruct high-quality images from measurement data? How can we uncover biochemical reaction mechanisms from experiments? And how can we quantify uncertainty in predictions from mathematical models, such as neural networks?
Such questions arise across a wide range of applications—from medical imaging, remote sensing, and data assimilation to computational chemistry, biology, and neuroscience. Despite their differences, these problems share a common structure: they can all be posed as inverse problems, in which unknown quantities (such as tissue properties or reaction parameters) must be inferred from indirect, noisy observations.
In this talk, I will outline recent advances in sparsity-promoting computational methods for efficiently solving inverse problems. I will also show how adopting a Bayesian perspective—treating unknown quantities probabilistically—enables uncertainty quantification in reconstructions and downstream predictions. This, in turn, supports more trustworthy scientific inference and decision-making. Furthermore, I will outline some recently developed approaches for efficient inference in hierarchical sparsity-promoting Bayesian models, including sparse Bayesian learning, that blend ideas from numerical analysis, optimization, and measure-transport theory.
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27 March 2026
Industrial production
Lecturer: Jelena Kurilova-Palisaitiene
Time: 13:15-14:00
Location: ACAS, A-huset, Campus Valla
Title: From Bad to Best: Applying Remanufacturing Maturity to track Circular Economy Transition
Abstract:
Manufacturing companies face rising pressure to transition from linear production logics toward more sustainable and circular operational models. A central requirement in this transition is the integration of remanufacturing, a product value retention process, into core business models and production systems. Despite its strategic relevance, adoption remains limited: a decade ago, only 1.1% of European Original Equipment Manufacturers (OEMs) engaged in remanufacturing, and its diffusion within OEMs continues to be slow. Contemporary manufacturers encounter significant strategic, operational, and supply chain uncertainties when attempting to advance circular economy (CE) ambitions through remanufacturing.
Existing research attributes many of these challenges to insufficient organizational knowledge and the lack of a systems oriented approach capable of aligning remanufacturing with established manufacturing practices. My research addresses these challenges by examining remanufacturing integration from a CE systems perspective and positioning remanufacturing maturity as a key indicator of manufacturers’ progress toward CE. Drawing on literature review and case study methodologies, I assess OEMs’ remanufacturing maturity using the Remometer® and its Remanufacturing Readiness Level (RRL) tool, applying these instruments within industrial case companies to evaluate their readiness and capability for circular transition.
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26 March 2026
Engineering Materials
Lecturer: Jinghao Xu
Time: 10:15-11:00
Location: A25, A-huset, Campus Valla
Title: Powder bed fusion metal additive manufacturing: materials, defects, properties, and process monitoring
Abstract:
Additive manufacturing, often known as 3D printing, has attracted growing attention as a way to produce complex metal components directly from digital models while reducing material waste and increasing design flexibility. One of the most important techniques is powder bed fusion, where a focused energy source selectively melts thin layers of metal powder to form solid structures.
At the same time, the extreme thermal conditions involved lead to complex material behavior and the formation of defects that can influence the properties of the final component. This lecture introduces the fundamentals of powder bed fusion and discusses how materials, processing conditions, and defects determine mechanical performance. I will also present process monitoring methods that provide new insight into how parts form during manufacturing.
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20 March 2026
Electrical Engineering
Lecturer: PhD Diana Moya Osorio
Time: 14:15-15:00
Location: Systemet, B-huset, Campus Valla
Title: Integrated Sensing and Communications
Abstract:
Radar and wireless communication systems have traditionally evolved independently as core applications of electromagnetic waves. Recent advances in antenna architectures, hardware platforms, and new spectrum allocations have brought their designs much closer together. This development opens the door to systems that can simultaneously perform sensing and communication, offering potential gains in spectrum and energy efficiency, hardware reuse, and computational savings.
This lecture motivates the evolution toward the convergence of communication and radar technologies by highlighting their benefits and emerging use cases. We introduce the fundamental concepts that link radar and communication systems, emphasizing their differing objectives, the distinctions in waveforms and signal processing, and the trade-offs that arise when jointly designing both functionalities. We then outline key technical challenges in integrated sensing and communication, together with the main opportunities for future systems. In particular, we discuss how upcoming wireless networks may operate as distributed sensor arrays capable of environmental perception and high accuracy localization, capabilities expected to be essential for many future applications.
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9 March 2026
Biology with specialisation in Ecology
Title: Drivers of biodiversity loss and conservation of threatened species: from global trends to butterflies on Gotland
Lecturer: Victor Johansson
Time: 13:15-14:00
Location: Nobel/BL32, B-huset, Campus Valla
Abstract
Biodiversity is declining at an unprecedented rate worldwide, driven primarily by habitat loss, land-use intensification, and climate change. In Sweden, intensive forestry has reduced structural complexity and dead wood availability, with negative consequences for numerous forest-dependent species. Semi-natural grasslands face a parallel decline, where changes in management and habitat degradation have led to losses of characteristic species, such as butterflies. Effective conservation requires detailed knowledge of species' habitat requirements, population sizes, and responses to land-use change. This lecture explores the major drivers of species decline from global to local scales, and discusses what ecological knowledge is needed to prevent extinction. Using examples from over a decade of research on threatened butterfly populations on Gotland, I show how long-term data on population dynamics, habitat preferences, and management effects can guide practical conservation strategies.
3 March 2026
Organic Chemistry
Title: Designing and Synthesizing Molecules to See and Treat Disease
Lecturer: Principal Research Engineer Marcus Bäck
Time: 13:15-14:00
Location: Schrödinger, Fysikhuset, Campus Valla
Abstract
This docent lecture focuses on research at the interface of chemistry and biology, where I use organic synthesis to create small molecules that enable us to study and influence biological systems. The lecture will begin with a brief introduction to organic chemistry and synthesis, illustrating how custom-designed molecules can be used to visualize and control biological processes. These tools make it possible to investigate dynamic processes that are otherwise difficult to observe.
I will present how fluorescent thiophene-based ligands have been developed to study diseases linked to protein aggregation, particularly Alzheimer’s disease, where misfolded proteins accumulate and cause cellular damage. I will provide a brief historical background of these molecules and describe how they have been structurally optimized to specifically detect protein aggregates.
The lecture will also cover the design of protease inhibitors, demonstrating how medicinal chemistry approaches were used to synthesize molecules that led to one of the first effective treatments for hepatitis C.
Overall, the topics presented illustrate how organic synthesis provides a powerful strategy for understanding disease biology and developing more precise therapeutic approaches.
24 February 2026
Computer Science
Title: Designing Engaging and Effective Casual Exergames
Lecturer: Erik Berglund
Time: 14:15 – 15:00
Location: Alan Turing, E-huset
Abstract
Casual exergames can make exercise more motivating, serving as a valuable tool for improving quality of life among clinical patient populations and combating sedentary lifestyles. To be successful and well-suited to target user groups, the design of game mechanics and full-body game controllers must be adapted to how the body and mind work. Full-body interaction presents particular challenges in adapting game mechanics and controllers to achieve both effective and enjoyable exergaming experiences. This lecture provides an overview of these challenges and presents insights drawn from my research studies. To address these questions, I have developed several casual exergames to study different aspects of exergaming and advance knowledge about design guidelines, particularly for touchless full-body interaction using MediaPipe skeletal tracking and body interaction using mobile Augmented Reality (AR). The lecture concludes with a forward-looking discussion on future research challenges and upcoming work.
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23 February 2026
Computational Fluid Dynamics
Title: Efficient computational and data-driven fluid dynamics for physics discovery and Control
Lecturer: Saeed Salehi
Time: 10:15-11:00
Location: A33, A-huset, Campus Valla
Abstract
High-fidelity computational fluid dynamics (CFD) enables detailed investigation of complex unsteady fluid flows, but the resulting data are often difficult to interpret and exploit for modeling and control. This lecture presents a research program that combines computational and data-driven fluid dynamics for efficient physics discovery and control of complex flows. High-fidelity CFD and data-driven methods, implemented within open-source frameworks, are used as a computational testbed to extract coherent structures, reveal dominant flow mechanisms, and construct reduced representations suitable for flow control. Learning-based control, particularly deep reinforcement learning, is explored as a means to control complex flows beyond the reach of classical approaches. Emphasis is placed on efficiency in learning-based control, achieved through intrusive frameworks, multifidelity, and transfer learning strategies that reduce the cost of learning while enabling scalable flow control.
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10 February 2026
Computer Science
Title: Testing and Test Environments for Large-Scale Cyber-Physical Systems
Lecturer: PhD Torvald Mårtensson
Location: Ada Lovelace, B-huset
Abstract
Large-scale cyber-physical systems—such as self-driving vehicles, advanced telecom infrastructure, and modern video surveillance platforms—combine complex software with tightly integrated electronic and mechanical components. Their scale and deep coupling with hardware introduce additional challenges for testing and for designing effective test environments. This lecture provides an overview of these challenges and presents insights drawn from research studies with eleven multinational companies across the telecom, defense, logistics, surveillance, and automotive sectors. We will explore strategies for overcoming continuous integration impediments, constructing continuous integration and delivery pipelines, and involving engineers with different knowledge, skills, and personalities to improve test efficiency and effectiveness. The lecture concludes with a forward-looking discussion on emerging testing challenges posed by autonomous systems and system of systems.
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9 February 2026
Computer Science
Title: Data-Efficient Machine Learning
Lecturer: PhD Sebastian Mair
Time: 10:15-11:00
Location: Alan Turing, E-huset
Abstract
Recent progress in machine learning has been largely driven by scale. Increasing data sizes and model capacity have enabled major performance gains, but at the cost of growing storage requirements, computational demand, and energy consumption. This lecture introduces the principle of data frugality: achieving strong predictive performance while using only as much data as necessary. Drawing on our research on data subset selection across multiple learning settings, we show how carefully chosen training data can reduce storage requirements, training time, and the carbon footprint, with negligible performance drops. The lecture concludes with an outlook on emerging research directions and open challenges in data-efficient machine learning.
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3 February 2026
Materials Science
Title: Ultra-wide bandgap gallium oxide semiconductor for next generation high-power electronics: growth, doping and defects study
Lecturer: Daniela Gogova
Time: 14:15-15:00
Location: Laplace, Fysikhuset, Campus Valla
Abstract
Power electronics is the enabling technology for efficient use, distribution, and conversion of electrical energy. The larger breakdown voltage inherent to wide band gap semiconductors leads directly to reduced dimensions and hence to reduced resistive losses. Recently, the ultra-wide bandgap gallium oxide has emerged as novel material with tremendous potential, exhibiting a figure of merits overperformed only by diamond. β-Ga2O3 is the thermodynamically stable polymorph of gallium oxide and has impressive properties, e.g., a bandgap of ~4.9 eV, large radiation stability, a breakdown field of 8 MV/cm, and the Baliga figure of merit (3444) - an order of magnitude higher than that of SiC and 4 times larger than of GaN.
The unique properties of this material, combined with the availability of low-cost, in comparison to the vapor and solution grown GaN and SiC substrates, real bulk growth methods from melt have made β-Ga2O3 a strong candidate for the next generation high-power devices.
In this presentation, I will demonstrate homo- and heteroepitaxial β-Ga2O3 growth employing two different MOVPE approaches and will discuss the material structural and electronic properties in result of Si (Sn) doping.
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22 January 2026
Theoretical Physics
Title: From Imperfections to Qubits: Explorating Defects in Semiconductors for Quantum Technologies
Lecturer: Joel Davidsson
Time: 10:15-11:00
Location: Nobel (BL32) B-huset, Campus Valla
Abstrac
Point defects in semiconductors play a crucial role in electronic devices. Shallow defects enable transistors that drive the modern information age, while deep-level defects facilitate quantum information processing. For quantum technology, a handful of point defects in a few semiconducting materials are being studied, with the nitrogen vacancy (NV) center in diamond at the forefront. However, could there be undiscovered defects with better properties? To address this question, I developed the ADAQ (Automatic Defect Analysis and Qualification) software and performed high-throughput simulations for various defect properties. The online ADAQ database currently contains results for approximately 50,000 defects in 80 host materials (https://defects.anyterial.se/). Exploring this database has yielded promising defects suitable for quantum technologies. In this presentation, I will discuss different defect systems predicted from the ADAQ database, highlight their unique properties, and demonstrate that data-driven screening uncovers experimentally tractable qubit candidates.