Find Academic Supervisors

• Numerical Linear Algebra
• Data Assimilation
• Covariance Matrices
• +3 more

Jemima M. Tabeart is an Assistant Professor at TU Eindhoven, affiliated with the Centre for Analysis, Scientific Computing and Applications . Her research focuses on numerical linear algebra, data assimilation, and covariance matrices. She actively contributes to outreach and scientific crafting, and advises students on final projects in computational science. Education: PhD (2016–2019): University of Reading (supervised by Prof. Sarah Dance and others) MRes (2015–2016): Mathematics of Planet Earth CDT (Imperial College London/Reading) MMath (2011–2015): University of Bath with study abroad at Université Grenoble Alpes Research Interests: Large-scale numerical linear algebra and its applications Development of efficient preconditioners for 4D-Var and data assimilation systems Treatment of covariance matrices in computational models Interdisciplinary applications in environmental and meteorological modeling Teaching & Leadership: Organizing a 2025 Research Semester Programme at CWI on numerical analysis and machine learning Teaching courses on calculus, linear algebra, and optimization at TU Eindhoven Co-lecturing the MasterMath course on Advanced Numerical Linear Algebra Professional Activities: Member of the 4TU.AMI SRI in Bridging Numerical Analysis and Machine Learning Principal investigator in a network funded by the INI Network Support for Mathematical Sciences Advocate for low-carbon travel, documenting her journeys on her blog

• Information Theory
• Coding Theory
• Communication Systems
• +8 more

Ragnar Thobaben is a Professor in Communication Theory at the Division of Information Science and Engineering, part of the School of Electrical Engineering and Computer Science (EECS) at KTH Royal Institute of Technology. He has held this position since December 2006 and is affiliated with the CERCES Center for Resilient Critical Infrastructures. His research focuses on information and coding theory, with applications to secure communication systems, machine learning generalization, and DNA-based data storage. He has also contributed to data-driven decision tools for critical care medicine, such as in sepsis management. Education: Ragnar earned a Dipl.-Ing. (M.Sc.) in Electrical Engineering from Christian Albrechts University, Kiel, Germany in 2001, followed by a Dr.-Ing. (Ph.D.) in Electrical Engineering in 2007. His academic journey is complemented by extensive editorial roles, including Editor for IEEE Transactions on Communications (2016–2020) and IEEE Transactions on Information Forensics and Security (2020–2023). He has also served as Technical Program Committee member for major IEEE conferences like GLOBECOM, ICC, and PIMRC, and held organizational roles such as Publicity Chair for the 2011 IEEE Swedish Communication Technologies Workshop and Local-Arrangement Chair for the 2019 IEEE Information Theory Workshop. Research Interests: His work bridges theoretical and applied domains, emphasizing robustness and reliability in communication systems. Key topics include physical-layer security for critical infrastructures, information-theoretic PAC-Bayes bounds in machine learning, and microwave engineering innovations like glide symmetry in antenna design. He also explores interdisciplinary applications, such as data-driven tools for ICU patient monitoring and secure wireless power transfer. Advising & Grants: Thobaben has advised multiple PhD students, including current co-supervision of Martin Lindström (machine learning) and collaborations with Karolinska Institute on medical informatics projects (Anna Sundelin and Navid Korah Soltani under main supervisor Johan Mårtensson). His prior advisees span topics from generalization theory to secure relaying in cognitive networks. His recent publications (2024–2022) highlight advancements in PAC-Bayes bounds, sepsis clinical studies, and antenna engineering.

• Particle Physics
• High-Energy Physics
• Collider Physics
• +3 more

Magdalena Vande Voorde is a Lecturer and Doctoral student at the Particle Physics, Astrophysics and Medical Imaging Unit of KTH Royal Institute of Technology. She holds teaching roles in courses such as Engineering Skills (SA1007), Modern Physics (SH1012), and Subatomic Physics (SH2103), demonstrating her dual role in both education and research. Her research focuses on high-energy physics experiments, particularly involving the ATLAS detector at the Large Hadron Collider (LHC), with an emphasis on Higgs boson properties, vector boson interactions, and searches for beyond the Standard Model physics. She contributes to detector calibration, jet flavor tagging, and event reconstruction methodologies. Recent work includes studies on WbWb production, CP violation in Higgs decays, and long-lived particle signatures at future colliders like FCC-ee. Her interdisciplinary interests also extend to the environmental sustainability of computing infrastructure in large-scale physics experiments. Her research portfolio includes over 50 publications since 2023, focusing on precision measurements of particle interactions and novel analysis techniques for collider data. Notable contributions include the observation of double parton scattering in W-boson pair production and the development of neural simulation-based inference tools for parameter estimation. She actively participates in international collaborations like the ECFA Higgs Factory Study, exploring future collider strategies for precision physics. Teaching: Engineering Skills (SA1007), Modern Physics (SH1012), Subatomic Physics (SH2103) Research Themes: ATLAS Experiment, Higgs Physics, Collider Phenomenology Key Projects: FCC-ee Long-Lived Scalar Searches, Top-Quark Mass Calibration Her work bridges experimental particle physics with computational innovation, addressing both theoretical challenges and practical sustainability concerns in large-scale scientific endeavors.

Iris-Corinna SchwarzView profile

Lecturer

• Language Development
• Infant Cognition
• Neurolinguistics
• +3 more

Iris-Corinna Schwarz is a Lecturer in Special Education at Stockholm University , affiliated with the Department of Special Education and Stockholm Babylab . She teaches child language development, neurolinguistics, and research methodology in both English and Swedish. Stockholm Babylab Stockholm University Special Education Department Her research focuses on early language development in infants, toddlers, and preschoolers, with emphasis on cognitive neuroscience methods. Key projects include: L3WO : Hyperarticulation's effects on word learning HELD : Clear articulation in child-directed speech VAICA : Vocal affect in infant-directed speech DIDI : Distributional learning mechanisms LETO : Tone perception development PALL : Affect in infant learning SoundStart : Statistical learning foundations CAPSL : Auditory predictability studies Recent publications analyze lexical tone discrimination , vocal affect dynamics , and global speech exposure patterns . Her work connects speech perception with neural mechanisms using electrophysiological measures . While no specific awards are mentioned, her international collaborations and methodological innovations in infant-directed speech underscore significant contributions.

• Fluid Mechanics
• Turbulence
• Combustion Physics
• +3 more

Geert Brethouwer is a Researcher at KTH Royal Institute of Technology, working within the FLOW MECHANICS group. He focuses on fluid mechanics, turbulence, and computational fluid dynamics, with expertise in reactive flows, atmospheric boundary layers, and large-eddy simulation (LES) modeling. His research emphasizes turbulence modeling, scalar transport in complex flows, and the effects of rotation, stratification, and roughness on flow dynamics. He contributes to courses such as Technical Fluid Mechanics (SG1220) and Thermodynamics (SG1216) as a teaching assistant, reflecting his commitment to both research and education in engineering sciences. His work integrates advanced numerical methods like DNS and LES to study transitional and turbulent flows, with applications in environmental fluid mechanics and combustion physics. Key research interests include Reynolds-stress modeling for stratified flows, passive scalar transport in rotating systems, and improving LES accuracy through subgrid-scale closures. His studies often address anisotropic turbulence, boundary layer transitions, and the interplay between flow instabilities and scalar mixing processes. Brethouwer’s publications span over two decades, with recent work focusing on heat/mass transfer disparities in rotating systems, turbulent combustion physics, and the explicit algebraic modeling of atmospheric boundary layers. He actively engages with computational tools like OpenFOAM to enhance LES fidelity while minimizing numerical dissipation. His research group collaborates on projects involving wall-jet flows, particle-laden turbulent flows, and the statistical analysis of high-order velocity fluctuations. Despite no awards explicitly listed in the provided texts, his extensive publication record underscores significant contributions to fluid dynamics methodologies.

• Non-Destructive Testing
• Materials Engineering
• Quality Management
• +3 more

Peter Hammersberg is a Senior Lecturer at the Division of Quality Sciences within the Department of Technology Management and Economics at Chalmers University of Technology. His expertise spans non-destructive testing, materials engineering, and quality management. He has over a decade of experience in industrial quality development as a certified Six Sigma Master Black Belt, leading to significant cost savings through projects. He teaches material engineering and Lean Six Sigma methodologies, and oversees the master’s program in Materials Engineering. His research focuses on integrating quantitative methods with material science to improve manufacturing processes and product quality. Ongoing work includes predictive modeling using Barkhausen noise for material hardening analysis and studies on surface integrity of advanced alloys like Alloy 718. He also explores educational tools for circular economy concepts, emphasizing practical applications in engineering design. Peter’s publications reflect a blend of experimental and computational approaches to quality assurance, welding optimization, and sustainable manufacturing. His earlier work includes foundational studies on X-ray imaging and computerized tomography performance in the late 1990s, showcasing his long-standing engagement with advanced testing technologies. His advisory role includes supervising over 70 industrial projects, emphasizing Lean Six Sigma and process optimization. While no formal awards are listed, his contributions to bridging academic research and industrial applications are notable. He actively collaborates with interdisciplinary teams across material science, manufacturing, and quality systems.

• Geometric Variation
• Robust Design
• Statistical Variation Simulation
• +7 more

Rikard Söderberg is a Professor of Product and Production Development at Chalmers University of Technology , where he earned his PhD in 1995. As director of the Wingquist Laboratory , he leads research initiatives in Geometry Assurance & Robust Design . His work bridges academia and industry through collaborations with automotive and aerospace sectors, focusing on minimizing geometric variations in manufacturing. Research Focus: Robust design, variation simulation, assembly optimization Key Collaborations: Automotive (Volvo, CEVT) and aerospace industries Scientific Leadership: Director of Wingquist Laboratory (since reorganization) Research Trends in his recent publications reveal interdisciplinary efforts combining digital twin technology , physics-based modeling , and evolutionary optimization . Articles address challenges in additive manufacturing , welding precision , and perceived product quality through computational frameworks. Scientific Recognition: Fellow, American Society of Mechanical Engineers (ASME) Fellow, International Academy for Production Engineering (CIRP) Member, Royal Swedish Academy of Engineering Sciences (IVA)

Krzysztof PodgórskiView profile

Professor

• Stochastic Processes
• Random Spatio-temporal Fields
• Non-Gaussian Stochastic Models
• +5 more

Krzysztof Podgórski is a Professor and Head of the Department of Statistics at Lund University School of Economics and Management (LUSEM). His research spans applied probability, statistics, and interdisciplinary applications in engineering, finance, and environmental sciences. Key Research Areas: Multivariate non-Gaussian stochastic models Statistical analysis of spatio-temporal random fields Distributions at random crossing events Applications: Mechanical engineering (road modeling) Ocean engineering (wave/ship reliability) Financial econometrics (market linkages, risk analysis) Actuarial sciences (non-Gaussian claims) Methodological Contributions: Include ergodic theory of stochastic processes, extreme value theory, and computational statistics. His recent work explores functional data analysis with periodic splines and matrix variate distributions. Collaborative Networks: Extensive partnerships across theoretical and applied disciplines, with projects involving road dynamics, stochastic fields, and uncertainty quantification.

• Medical Education
• Cardiovascular Disease
• Health Care Management
• +3 more

Anna Kiessling is a Professor in Medical Education and Senior Physician at Karolinska Institutet (KI), specializing in cardiology and internal medicine. She holds a prominent role as Programme Director for KI's new six-year medical program and leads the Coherent Quality Assurance System. Her academic leadership includes chairing the National Working Group for Cardiovascular Prevention and Lifestyle (Swedish Society of Cardiology) and serving as Scientific Secretary for the KIPRIME Award Committee. Education: PhD in Internal Medicine (KI, 2005), National Cardiology Diploma (1993), and Medical Licensure (1986). Awards include the KI Pedagogy Prize (2019) and Fellow of the Karolinska Pedagogy Academy (2012). She is an internationally recognized expert as a Fellow of the European Society of Cardiology (FESC). Research focuses on cardiovascular prevention (e.g., lifestyle interventions and patient decision-making post-MI) and medical education (professional development, interprofessional learning, and clinical decision-making). Methodologies include randomized controlled trials, qualitative studies, and megavariate statistics. Teaching spans undergraduate, graduate, and continuing education. Notable projects include developing the Centre for Clinical Education (CKU) and leading the Lifestyle Project (Socialstyrelsen-funded). Supervised PhD students have defended theses in medical education (2014, 2016, 2022). Grants: Led the Perfect-CR study on cardiac rehabilitation outcomes (2020-2022), funded by the Swedish Research Council and Heart-Lung Foundation. Awards: KI Pedagogy Prize, FESC Fellowship, NCPC Sweden Coordinator (2013-2021). Key initiatives include bridging clinical practice and education through projects like Perfect-CR and the Lifestyle Project, emphasizing evidence-based practice and preventive cardiology.

• Robotics
• Computational Geometry
• Topology in Robotics
• +3 more

Anastasiia Varava is an affiliated faculty member and PhD student at KTH Royal Institute of Technology, working within the Division of Robotics, Perception and Learning. Her research focuses on computational geometry and topology applied to robotic manipulation, particularly caging theory and its practical implementations for handling rigid and deformable objects. She collaborates with advisors Florian Pokorny and Danica Kragic on theoretical and applied robotics challenges. Her work integrates robotics with interdisciplinary fields such as topology, machine learning, and computer vision. Key topics include path planning, deformable object manipulation, and geometric algorithms for motion verification. Recent contributions emphasize data-driven approaches for latent space modeling and visual action planning in robotics systems. Varava has authored or co-authored over 30 peer-reviewed articles and conference papers, covering topics from caging-based motion planning to molecular screening algorithms inspired by robotics. Her publications span journals like IEEE Transactions on Robotics and conferences such as IROS and ICRA, reflecting a strong focus on practical robotic systems with theoretical rigor. Her doctoral thesis, Path-Connectivity of the Free Space: Caging and Path Existence (2019), established foundational work on caging and path non-existence in robotics. Current projects involve advancing geometric evaluation techniques for data representations and exploring diffusion models for robotic skill learning.

• Partial Differential Equations
• Numerical Analysis
• Stochastic Differential Equations
• +5 more

Anders Szepessy is a Professor in Mathematics and Numerical Analysis at the Department of Mathematics, KTH Royal Institute of Technology. His research centers on partial differential equations and numerical methods for differential equations, with significant contributions to molecular dynamics, quantum mechanics, and stochastic systems. His primary research domains include: Partial Differential Equations Numerical Analysis Stochastic Differential Equations Molecular Dynamics and Quantum Mechanics Optimal Control and Finite Element Methods Machine Learning Applications Recent publications reveal a pronounced shift toward integrating machine learning with computational physics, particularly in neural network approximations for molecular dynamics and quantum systems. His work on adaptive methods for high-dimensional stochastic and partial differential equations demonstrates consistent innovation in numerical analysis, with applications spanning quantum chemistry, fluid dynamics, and energy systems. Professor Szepessy actively contributes to academic instruction as course responsible and examiner for advanced mathematics courses including Analytical and Numerical Methods for Partial Differential Equations, Computational Methods for Stochastic Differential Equations and Machine Learning, and multiple degree projects in mathematics and scientific computing at KTH.