Mathematical Sciences

<h4>Statistical Modeling for Prediction of Vehicle Usage</h4>
<p>Location: MV:L15, Chalmers tvärgata 3</p>

<h4>A Spatial Cluster Process Model for the Regeneration of Epidermal Nerve Fiber Patterns</h4>
<p>Location: MV:L15, Chalmers tvärgata 3</p>
<p>Abstract: Epidermal nerve fibers (ENFs) play a key role in sensory function and are valuable indicators to detect different stages of diabetic neuropathy. Previous studies developed spatial point process models to describe the spatial structure of ENFs and the distribution of their end points. However, modeling how ENFs regrow after damage is still an open problem. In this thesis, we develop a branching cluster (BC) model to describe ENF regeneration by modeling multi-level branching points and end points based on entry points. The model aims to reconstruct nerve patterns in damaged areas, following hypotheses proposed by neurologists. According to these hypotheses, ENF regrowth involves two steps: existing nerve fibers grow into the damaged region, and new ENFs emerge within it.</p>
<p>To evaluate the model, we apply a global envelope test using Ripley's K-function to examine the spatial pattern of end points in the damaged region. The results suggest that the BC model captures the observed structure well within small distances. Coverage analysis also shows that the model can regenerate ENF patterns that reflect the healing process. Overall, the BC model offers a statistical framework that incorporates full branching structures to describe and simulate ENF regrowth in damaged skin areas.</p>

<h4>Prospects of Normalizing Flow-based Differentiable Particle Filters in Target Modelling: Taking Target Modelling in Complex and Diverse Target Environments to the Next Level</h4>
<p>Location: FL51</p>

<h4>Estimating Charging Time Using Machine Learning – Estimating the Remaining Charging Time for Electric Vehicles Using Machine Learning</h4>
<p>Location: MV:L15</p>
<p>Examiner: Patrik Albin</p>

<h4>Home Energy Management System (HEMS) Optimization for private homes with Battery Electric Vehicles (BEV)</h4>
<p>Location: MV:L15, Chalmers tvärgata 3</p>
<p>Supervisors: Viktor Larsson, Volvo Cars, Larisa Beilina<br>Examiner: Larisa Beilina</p>
<p>Abstract: This thesis explores the optimization of Home Energy Management Systems (HEMS) with a focus on battery electric vehicle (BEV) charging, using two different methods: Linear Programming (LP) and stochastic optimization. The BEV is modeled with vehicle-to-everything (V2X) functionality, allowing it to both charge and discharge energy to support the household or grid.</p>
<p>The primary aim is to incorporate uncertainty into the optimization process, acknowledging that in real-life scenarios, exact information about the vehicle’s arrival time and required state of energy (SoE) is rarely known in advance. We begin by examining uncertainty in both arrival time and SoE, then narrow the focus to uncertainty in SoE alone. In the final part of the thesis, we investigate the impact of power tariffs to determine whether incorporating them into the optimization yields meaningful benefits.</p>

<h4>Deep Learning for High-Frequency Price Prediction in Cryptocurrency Markets</h4>
<p>Location: MV:H11, Hörsalsvägen 1</p>

<h4>Towards More Accurate and Adaptable Surrogate Models: Evaluating Autoencoders, LSTMs, and Data-Driven Techniques in a Surrogate Model framework for Hydrodynamic Simulations</h4>
<p>Location: MVF23, Hörsalsvägen 1</p>

<h4>Stochastic Methods for Beam Weight Optimization for Large Antenna Arrays: A Time Efficient and Robust Method for Beam Weight Optimization Using Non-ideal Data</h4>
<p>Location: MV:L15, Chalmers tvärgata 3</p>
<p>The traditional methods for beam weight optimization suffer from poor convergence and long run times for large antenna arrays, and are unable to process non-ideal embedded subarray pattern data. There is therefore a need for new, efficient algorithms to allow for the continued development of larger and more complex antenna arrays. Previous research has shown genetic algorithm and particle swarm optimization to be promising alternatives for solving this problem.</p>
<p>This thesis investigates the tailoring of these stochastic optimization algorithms for beam weight optimization using realistic simulated data.</p>
<p>We propose the use of an absolute scale for the far field function as opposed to a relative scale. This allows us to remove the explicit taper loss component from the objective function and allows for a more efficient energy usage. We decrease runtimes by restricting the optimization to a low rank search space, using warm starts and utilizing GPUs.</p>
<p>We find that these tailored methods are robust and consistently converge significantly faster than current methods. Resulting beam weights also show better performance than those currently in use by Ericsson products. We successfully apply our methods to optimize beam weights for antenna arrays as large as 1152 subarrays.</p>

<h4>Prediction of Vehicle ComponentWeights<br>using Multi-output Regression</h4>
<p>Location: MVL14, Hörsalsvägen 1</p>

<h4><span style="font-size: 12.0pt; font-family: 'Aptos',sans-serif; mso-ascii-theme-font: minor-latin; mso-fareast-font-family: Aptos; mso-fareast-theme-font: minor-latin; mso-hansi-theme-font: minor-latin; mso-bidi-font-family: 'Times New Roman'; mso-bidi-theme-font: minor-bidi; mso-ansi-language: SV; mso-fareast-language: EN-US; mso-bidi-language: AR-SA;">Hierarchical Classification of DNA Barcodes Using Transformers </span></h4>
<p>Location: MV:H12, Hörsalsvägen 1</p>

<h4>Learning Through Competition: Educational Outcomes for Architecture Students in Competitive Environment</h4>
<p>Location: Pascal, Hörsalsvägen 1</p>
<p>Supervisor: Caroline Ingelhammar<br>Opponent: Ali Nouri</p>

<h4>Science center as complementary learning environment towards inclusive mathematics education: An exploratory case field study</h4>
<p>Location: Pascal, Hörsalsvägen 1</p>
<p>Supervisor: Lena Pareto<br>Opponents: Elina Ryding and Bjarne Sihlbom</p>

<h4>Problem Solving In Thinking Classrooms: Fostering Critical Thinking and Collaboration through Problem-Solving Activities</h4>
<p>Location: Pascal, Hörsalsvägen 1</p>
<p>Supervisor: Éva Fülöp<br>Opponent: Julia Gunnarsson</p>

<h4>Learning the Fragment Size Distribution in Liquid Biopsy Sequencing</h4>
<p>Location: MV:L14; Chalmers tvärgata 3</p>
<p>Supervisor: Eszter Lakatos<br>Examiner: Erik Kristiansson</p>

<h4>Expanding the Scope of Football Analytics by Integrating Tracking Data and Utilizing Statistical Learning</h4>
<p>Location: MV:L14, Chalmers tvärgata 3</p>
<p>Examiner: Rebecka Jörnsten<br>Opponents: Malte Olsson, Carl Kronqvist</p>
<p>Abstract: The recent decade has seen a data revolution within football as data analytics and statistical learning have become established vital tools. The data revolution has also seen the emergence of a new type of football data called tracking data. This thesis first explores how information from tracking data can be integrated with established event data and improve an established statistical learning model providing an expectancy metric for passes called xP. Secondly, the thesis explores how it can used to create a new type of statistical expectancy metric for player playability previously unattainable with only event data.</p>
<p>Using event data and tracking data from 28 real football games, these separate datasets have been synchronized to extract new information and context for passing events. This information was used to train and compare a statistical learning model for the xP metric with a model only trained on the previously known event data. The results indicate that the added tracking data information provides a significantly improved xP model especially in terms of understanding passing events and therefore making more realistic pass probability predictions. Despite clear improvement, there exist possibilities to further improve the xP model in regards to for example a more accurate data synchronization process as well as further improved feature engineering.</p>
<p>Moreover, the synchronized tracking and event data in combination with the improved xP model were used to develop a completely novel metric that describe player playability expectancy called xPlay. The new metric provides a simple and elegant way of measuring player playability and results of various implementations indicate that the metric can serve as a great tool in both player and team evaluation. Although promising results the metric is in need of more evaluation on a bigger scale.</p>

<h4>Identifying Key Factors for a Successful Trainee Program: Anchored in the Perception of Former Trainees</h4>
<p>Location: Pascal, Hörsalsvägen 1</p>
<p>Supervisor: Maria Cervin-Ellqvist<br>Opponents: Lisa Lindhe and Leo Lahti</p>

<h4>Conscious Teaching: Strengthening teacher well-being and supporting sustainable classroom management</h4>
<p>Location: Pascal, Hörsalsvägen 1</p>
<p>Supervisor: Christine Räisänen<br>Opponent: Rebecca Corcoran</p>

<h4>Hands-On Algebra: An Experiential Learning Intervention in Middle School Using Physical and Virtual Manipulatives</h4>
<p>Location: Pascal, Hörsalsvägen 1</p>
<p>Supervisor: Samuel Bengmark<br>Opponents: Joel Nilsson and Madelene Törnqvist</p>

<h4>Deep Learning for Nonlinear Filtering: Adapting the Deep Backward Stochastic Differential Equation Method for Stochastic Filtering</h4>
<p>Location: MV:L15, Chalmers tvärgata 3</p>
<p>Supervisors: Kasper Bågmark and Moritz Schauer<br>Examiner: Stig Larsson</p>
<p>Abstract: This thesis addresses the filtering problem: estimating the probability density of a latent stochastic process given noisy observations by analysing the convergence of a novel deep learning-based filter, the deep backward stochastic differential equation filter. Rooted in the connection between backward stochastic differential equations and partial differential equations, the new filter is compared against classical filters, including the Kalman filter, the Extended Kalman filter, and a bootstrap particle filter. While particle filters can handle nonlinear, non-Gaussian systems, they suffer from the curse of dimensionality, which motivates the development of new filter approximations for nonlinear and high-dimensional settings. The new method is constructed by sequentially applying a deep learning-based method for backward stochastic differential equations while incorporating observations. The thesis studies the empirical strong convergence rate of the method in both the unconditional case (without observations) and the conditional case (with observations). It demonstrates promising results in three examples: an Ornstein–Uhlenbeck process, a bimodal stochastic differential equation, and a 4-dimensional spring-mass system. The observed convergence rate for the first two cases is approximately order 1/2, in accordance with the theoretical order. For the final example, it is lower, which may be due to statistical or approximation error.</p>

<h4>Sensitivity Study of Parameters in a Mathematical Model of HIV Infection Using Clinical Data</h4>
<p>Location: MV:L14, Chalmers tvärgata 3</p>
<p>Supervisor and examiner: Larisa Beilina</p>
<p>Abstract: Modeling the early stages of HIV infection is essential for understanding virus dynamics and improving treatment strategies. In this project, we solve a system of three ordinary differential equations (ODEs) that describe the interactions between uninfected immune cells, infected cells, and the viral load. Our goal is to reconstruct key parameters and optimize the model using real clinical data from four patients.</p>

<h4><span style="font-size: 12.0pt; font-family: 'Aptos',sans-serif; mso-ascii-theme-font: minor-latin; mso-fareast-font-family: Aptos; mso-fareast-theme-font: minor-latin; mso-hansi-theme-font: minor-latin; mso-bidi-font-family: 'Times New Roman'; mso-bidi-theme-font: minor-bidi; mso-ansi-language: SV; mso-fareast-language: EN-US; mso-bidi-language: AR-SA;">Identification of New Mobile Antibiotic Resistance Genes using Metagenomic Abundances</span></h4>
<p>Location: MV:H12, Hörsalsvägen 1</p>

<h4>Exploring Variations in Frequent Passing Grade Tests in Mathematics: Upper secondary school students’ perceptions</h4>
<p>Location: Pascal, Hörsalsvägen 1</p>
<p>Supervisor: Samuel Bengmark<br>Opponent: Jona Wilhelm</p>

<h4>Hands-On Algebra: An Experiential Learning Intervention in Middle School Using Physical and Virtual Manipulatives</h4>
<p>Location: Pascal, Hörsalsvägen 1</p>
<p>Supervisor: Robert Cumming<br>Opponent: Michael Persson and Elin Bryne</p>

<h4><span style="font-size: 12.0pt; font-family: 'Aptos',sans-serif; mso-ascii-theme-font: minor-latin; mso-fareast-font-family: Aptos; mso-fareast-theme-font: minor-latin; mso-hansi-theme-font: minor-latin; mso-bidi-font-family: 'Times New Roman'; mso-bidi-theme-font: minor-bidi; mso-ansi-language: SV; mso-fareast-language: EN-US; mso-bidi-language: AR-SA;">The Genetic Compatibility between Antibiotic Resistance Genes and Bacterial Hosts</span></h4>
<p>Location: MV:H12, Hörsalsvägen 1</p>

<h4>Partial Actions: Restriction and Globalization</h4>
<p>Location: MV:L15, Chalmers tvärgata 3</p>

<h4>Joint mixed-effects modelling of longitudinal health data for monitoring the progression of cystic fibrosis lung disease</h4>
<p>Location: MV:L14, Hörsalsvägen 1</p>

<h4>Development of a Lagrangean Dual Method for Solving Sequentially Connected 2D Assignment Problems</h4>
<p>Location: Pascal, Hörsalsvägen 1</p>

<h4>Investigation of k-rationality &amp; cutting plane methods in sequentially connected 2D assignment problems</h4>
<p>Location: Pascal, Hörsalsvägen 1</p>

<h4>Logistic route planning for electric vehicles using the Adaptive Large Neighborhood Search</h4>
<p>Location: Pascal, Hörsalsvägen 1</p>

<h4>Optimizing Market Engagement: Strategic Models for District Heating Companies’ Participation in Electricity Markets</h4>
<p>Location: Pascal, Hörsalsvägen 1</p>

<h4>Statistical Evaluation of Radar Simulation Models Towards Real Data – Method Implementation and Statistical Comparison</h4>
<p>Location: MV:L15, Chalmers tvärgata 3</p>
<p>Opponents: Erman Kulaglic och Jakob Bergman</p>
<p>Abstract: This thesis aimed to create a method that could be used by Volvo Car Corporation (VCC) to statistically evaluate the truthfulness and accuracy of the built-in radar models used by VCC. Using pre-collected data from European New Car Assessment Programme (Euro NCAP) scenarios, specifically Car-to-Car Rear moving (CCRm) and Car-to-Bicyclist Nearside Adult Obstructed (CBNAO), a virtual environment was created, enabling the recreation of real scenarios in a simulated environment. Furthermore, a comparison between the simulated and real data was conducted. This comparison was conducted using the statistical metric Double Validation Metric (DVM), which is a combination of the Area Validation Metric (AVM), Corrected Area Validation Metric (CAVM), and the model bias within the simulated data.</p>
<p>The developed method includes parsing of Hierarchical Data Format version 5 (HDF5) files, enabling reading and manipulation of these files. It also features a Graphical User Interface (GUI) that reads one real file and one simulated file, visualizes the desired detection columns within a given timestamp interval, and a statistical evaluator that performs all the calculations, plots the Empirical Cumulative Distribution Functions (eCDF), and analyzes their statistical characteristics.</p>
<p>The results demonstrated tendencies pointing towards both reliability and unreliability. Different parameters showed varying degrees of correlation for different scenarios, runs, and speeds. However, one clear trend was the high tendency for detection differences between the simulated model and the real model, with the simulated model outnumbering the real model. Based on this, the developed method can be used to some extent to relay information about whether the radar model is trustworthy. However, its effectiveness heavily depends on the specific area of usage.</p>
<p>In conclusion, the method effectively visualizes the detections, indicating where they occur and how they differ in number. While the model may not be suitable for final certification, it can serve as a valuable tool for statistical approximation of the radar model's performance.</p>

<h4>Inversion Methods for Malignant Melanoma Detection</h4>
<p>Location: MV:L14, Chalmers tvärgata 3</p>
<p>Join Zoom Meeting <a href="https://gu-se.zoom.us/j/66996343061?pwd=fL4ojHwQmVZ6USLbAkAe1P7nNVpDGe.1">https://gu-se.zoom.us/j/66996343061?pwd=fL4ojHwQmVZ6USLbAkAe1P7nNVpDGe.1</a></p>
<p>Meeting ID: 669 9634 3061<br>Passcode: 017994</p>
<p>Supervisor: Larisa Beilina<br>Examiner: Irina Pettersson</p>
<p>Abstract: In this thesis, we will study several gradient based reconstruction algorithms for qualitative and quantitative determination of the relative dielectric permittivity and effective conductivity functions using time-dependent backscattered data of the electric field measured at the boundary of investigated domain. One of the most important applications of the studied algorithms is microwave medical imaging and we will test our algorithms on the real 3D model of skin for Malign Melanoma detection.</p>

<h4>Financial News Event Prediction Using Temporal Knowledge Graphs</h4>
<p>Location: MV:H12, Hörsalsvägen 1</p>
<p>Opponents: Simon Lanngren, Martin Toremark</p>

<h4>Evaluating Machine Learning Models for Cost Prediction in Swedish Domestic Shipping</h4>
<p>Location: MV:L15, Chalmers tvärgata 3</p>
<p>Abstract: Sendify’s business model relies on aggregating shipments to negotiate favorable carrier rates (e.g. for DHL, FedEx). In this context, precise shipment cost estimation is essential for providing both transparent and competitive quotes to customers.</p>
<p>We reformulate the shipment‐cost estimation problem as a supervised learning task, evaluate three models — multiple linear regression (MLR), Extreme Gradient Boosting (XGBoost), and LightGBM — and benchmark them against Sendify’s existing algorithm.</p>
<p>A historical database of domestic parcel shipments in Sweden — including the attributes used for quoting, Sendify’s predicted quotes, and the actual carrier bills — was compiled. Two DHL service products were selected for a detailed case study. For each product, we constructed two feature sets: one containing only the generic shipment attributes, and one combining those generic attributes with carrier‐specific features.</p>
<p>Each of the three models was then trained on both datasets, and their predictive performance was compared to Sendify’s existing algorithm.</p>

<h4>Conformal Predictive Decision Making: A Comparative Study with Bayesian and Point-Predictive Methods</h4>
<p>Location: MV:H12, Hörsalsvägen 1</p>
<p>Abstract: In many real-world settings, machine learning (ML) predictions serve as intermediate outputs used to inform decision-making. However, quantifying and accounting for uncertainty in these decisions remains a fundamental challenge. Conformal Predictive Decision Making (CPDM) is a framework for decision-making under uncertainty that leverages Conformal Predictive Distributions (CPDs) to optimize outcomes over a specified utility function. In this work, we evaluate two CPDM variants on synthetic datasets and compare their performance to two alternative approaches: Bayesian Decision Theory (BDT) and Point Predictive Decision Making (PPDM). Our proposed CPDM algorithm significantly outperforms the previously established one in most cases, while also offering computational advantages. It also showed greater robustness than BDT and PPDM in scenarios involving noisy data and skewed utility functions.</p>

<h4>Extending Large-Scale Regression to Negative-Binomial GRN Analysis</h4>
<p>Location: MV:L14, Chalmers tvärgata 3</p>
<p>Abstract: In this thesis, we extend the functionality of the bioinformatics software scregclust by replacing its underlying Ordinary Least Squares (OLS) regression framework with a Negative Binomial (NB) regression model — more suitable for modeling overdispersed count data, such as single-cell RNA sequencing (scRNA-seq). We present theoretical and computational solutions for adapting the software to NB assumptions, including a likelihood-based formulation of the Cooperative Lasso (Coop Lasso) problem and a method for performing non-negative NB regression. The algorithmic implementation relies on iterative reweighted least squares (IRLS), adaptive relaxed ADMM, and efficient C++ routines to manage the increased computational demands. We validate the method on synthetic data, demonstrating that the extended software, scregclust_nb, can accurately recover ground truth gene regulatory networks (GRNs). Finally, we illustrate how the tool can be applied to real scRNA-seq data and interpreted through an interactive Shiny application.</p>

<p><strong>Using Machine Learning for Predicting Interchanges: Leveraging Machine Learning Methods for Finding Equivalent Products Based on Competitor Products</strong></p>
<p>Location: MV:F23, Hörsalsvägen 1</p>

<p>Location: MV:L14, Chalmers tvärgata 3</p>
<p>Abstract: Glioblastoma, GBM, is among the most aggressive and deadly cancers, characterized by invasive growth, pronounced heterogeneity, and high recurrence rates. Its invasive nature complicates surgical resection, while the redundancy of its invasion-associated pathways presents significant challenges for therapeutic targeting. Recent advances in single-cell RNA sequencing, scRNA-seq, and CRISPR/Cas9 technologies have opened new avenues for dissecting the molecular underpinnings of GBM invasion by enabling the mapping of genotype-phenotype relationships at single-cell resolution. This thesis leverages single-cell Perturb-seq data to uncover gRNA induced gene expression variations in GBM by implementing on/off-target analysis and a regression-based framework with elastic net and nuclear norm penalties. The computational framework addresses critical aspects of modeling biological data, including the detection of guideRNAs, gRNAs, descriptive regression framework penalties and choosing stability metrics for evaluating parameters. Cell signatures, derived from gene expression profiles, enable the characterization of GBM subtypes and cellular states, that serve as the response variable in the regression. To further explore the biological structure of the data, a signature space is constructed and Maximum Mean Discrepancy, MMD, values are computed to assess the distinctiveness of gRNA-induced effects within the signature subspace. This thesis specifically examines genes with potential roles in invasive behavior in GBM.</p>
<p>Marked differences in gRNA content and signature distributions are observed between in vivo and in vitro systems, with in vivo cells exhibiting a shift from mesenchymal-like to oligodendocyte- and neural progenitor-like states. Analysis of on-target and off-target effects reveals both expected and broader transcriptional consequences of perturbations. Using MMD, a greater degree of state divergence is detected in vivo, possibly reflecting environmental influences on plasticity. Guides such as PRDX2, TCF12, ANPEP and KLF5 show consistent effects across analysis, highlighting candidates for further functional study. Distinct clustering patterns of regression profiles are found, reflecting signature-level organization, suggesting coordinated perturbation effects. As a pilot data study, these findings motivate the generation of additional in vivo and in vitro datasets, including appropriate controls, to support biological validation and refine the interpretability of perturbation models in complex tissues. Moving forward, expanding the computational framework with cell state covariates and interaction modeling may offer deeper insights into gene function and perturbation dynamics.</p>
<p>Glioblastoma, GBM, is among the most aggressive and deadly cancers, characterized by invasive growth, pronounced heterogeneity, and high recurrence rates. Its invasive nature complicates surgical resection, while the redundancy of its invasion-associated pathways presents significant challenges for therapeutic targeting. Recent advances in single-cell RNA sequencing, scRNA-seq, and CRISPR/Cas9 technologies have opened new avenues for dissecting the molecular underpinnings of GBM invasion by enabling the mapping of genotype-phenotype relationships at single-cell resolution. This thesis leverages single-cell Perturb-seq data to uncover gRNA induced gene expression variations in GBM by implementing on/off-target analysis and a regression-based framework with elastic net and nuclear norm penalties. The computational framework addresses critical aspects of modeling biological data, including the detection of guideRNAs, gRNAs, descriptive regression framework penalties and choosing stability metrics for evaluating parameters. Cell signatures, derived from gene expression profiles, enable the characterization of GBM subtypes and cellular states, that serve as the response variable in the regression. To further explore the biological structure of the data, a signature space is constructed and Maximum Mean Discrepancy, MMD, values are computed to assess the distinctiveness of gRNA-induced effects within the signature subspace. This thesis specifically examines genes with potential roles in invasive behavior in GBM.</p>
<p>Marked differences in gRNA content and signature distributions are observed between in vivo and in vitro systems, with in vivo cells exhibiting a shift from mesenchymal-like to oligodendocyte- and neural progenitor-like states. Analysis of on-target and off-target effects reveals both expected and broader transcriptional consequences of perturbations. Using MMD, a greater degree of state divergence is detected in vivo, possibly reflecting environmental influences on plasticity. Guides such as PRDX2, TCF12, ANPEP and KLF5 show consistent effects across analysis, highlighting candidates for further functional study. Distinct clustering patterns of regression profiles are found, reflecting signature-level organization, suggesting coordinated perturbation effects. As a pilot data study, these findings motivate the generation of additional in vivo and in vitro datasets, including appropriate controls, to support biological validation and refine the interpretability of perturbation models in complex tissues. Moving forward, expanding the computational framework with cell state covariates and interaction modeling may offer deeper insights into gene function and perturbation dynamics.</p>

<h4>Sensor Cloud Correlation with Body Volume and Position</h4>
<p>Location: MV:L15, Chalmers tvärgata 3</p>

<h4>Tensor decomposition for time-resolved immune cell sequencing in cancer</h4>
<p>Location: MV:L14, Chalmers tvärgata 3</p>
<p>Supervisor: Eszter Lakatos<br>Examiner: Marina Axelson-Fisk</p>

<h4>Communication Relaying Networks Using Autonomous Drones</h4>
<p>Location: MV:L15, Chalmers tvärgata 3</p>

<h4>Distance to Singularity for Skew-Symmetric Matrix Pencils</h4>
<p>Location: MV:L15, Chalmers tvärgata 3</p>

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Master thesis presentations at Mathematical Sciences

23 May 2025

9:00–10:00 Mathis Wiesner

26 May 2025

10:00–11:00 Chupei Lin

10:00–11:00 Simon Nygren and Gustav Mosséen

27 May 2025

11:00–12:00 Elliot Källander and Shahir Imam

13:15–14:15 Jessica El-Jabaoui and Edvin Sand

28 May 2025

10:00–11:00 Noah Trädgårdh

10:00–11:00 Elsa Delshammar and Adina Hellström

10:00–11:00 Matilda Selvaraj and Johan Ödesjö

13:30–14:30 Elinor Funk and Frida Sjögren

2 June 2025

09:00–11:00 Marcus Axelsson and Filip Berntsson

9:00–10:00 Julia Gunnarsson

10:00–11:00 Michael Persson and Elin Bryne

11:00–12:00 Ali Nouri

11:00–12:00 Anna Svensson Fehér and Samuel Kronqvist

13:00–14:00 Albin Steen

13:00–14:00 Joel Nilsson and Madelene Törnqvist

14:00–15:00 Jona Wilhelm

15:00–16:00 Lisa Lindhe and Leo Lahti

15:00–16:00 Melker Bildh

15:15–16:15 Messaoud Shaker

3 June 2025

9:00–10:00 Ida Berg and Ellen Koningen

9:00–10:00 Rebecca Corcoran

10:00–11:00 Elina Ryding and Bjarne Sihlbom

10:00–11:00 Enya Arvidsson and Johanna Lundström

11:00–12:00 Danai Kiewwan

13:15–14:15 Mattis Hallberg

13:15–14:00 Louise Chastine Christensen

14:15–15:00 Albert Vesterlund

15:15–16:00 Regina Gustavsson and Ellinor Sorpola Svenningsson

16:15–17:00 Jacob Burman

4 June 2025

10:00–11:00 Gustav Wikman and William Nilsen

13:15–14:15 Georg F. M. Kyhn

5 June 2025

10:00–11:00 Axel Blom and Andreas Helgesson

10:00–11:00 Jan Pawel Kula

11:00–12:00 Simon Lanngren and Martin Toremark

13:00–14:00 André Lasses Armantowski

14:00–15:00 Jesper Wiström

14:30–15:30 Filippa Kruse

June 9 2025

9:00–10:00 Anxhela Hysa and Ashwini Arun Kumar

11:00–12:00 Pablo Vadillo Berganza and Violant Moreno Creixell

June 10 2025

13:15–14:15 Jacob Ljungberg

June 11 2025

13:15–14:00 Rakel Hellberg