جست‌وجوی استاد و پژوهشگر

Beatrix C. Hiesmayrمشاهده پروفایل

دانشیار

• Quantum Information Theory
• Entanglement Characterisation & Witnesses
• High-Energy Discrete-Symmetry Tests
• +۴ مورد دیگر

Beatrix C. Hiesmayr is a Privatdozent (habilitated Associate Professor) of theoretical physics at the University of Vienna, Faculty of Physics, where she has led the Quantum Particle Workgroup since 2008. She serves as elected member of the works-council for scientific staff and teaches a broad spectrum of courses from introductory lab sessions to advanced quantum-information theory. Education & qualifications: The text does not list prior degrees, but her title Mag. Dr. and the rank Privatdoz. indicate a doctorate and post-doctoral habilitation at an Austrian university. Research interests sit at the tripartite interface of quantum information science , high-energy particle theory and medical-imaging technology . Her group develops mathematical tools to detect and quantify entanglement in composite qudit systems, subjects discrete space-time symmetries to precision tests with positronium decays, and translates the resulting detector know-how into the world-first plastic-scintillator total-body PET scanner (J-PET). Across 15 representative papers (2023-2025) she explores entanglement witnesses, distillation protocols, quantum neural networks, and realistic PET engineering, signalling a coherent programme that moves from abstract quantum-structure theorems to hardware prototypes now entering clinical feasibility studies. Although no specific grants or honours are mentioned, she currently coordinates the COST action “Relativistic Quantum Information” (CA21106) and her group operates an international consortium on J-PET development. Student names are not disclosed in the supplied text. Contact: beatrix.hiesmayr@univie.ac.at — Boltzmanngasse 5, 1090 Wien, room 503.

• Operations Research
• Multi-Objective Optimization
• Transportation Networks
• +۳ مورد دیگر

Matthias Ehrgott is a Professor in the Department of Management Science at the Lancaster University Management School (LUMS). His research focuses on multi-objective optimization, operations research, transportation systems, and healthcare logistics. He leads the OptiWaSP project (2022–2025), which designs efficient walking school bus routes integrating optimization and instance generation. He is affiliated with the Centre for Transport & Logistics (CENTRAL), STOR-i Centre for Doctoral Training, and Health Systems Optimisation groups. His editorial roles include serving on the editorial boards of EURO Journal on Decision Processes , Optimization , 4OR , and others. Recent work emphasizes applications in railway timetabling, radiation therapy planning, and sustainable urban mobility. He advises PhD students like Malek Almousa, an Associate Lecturer. Key research themes include bi-objective optimization, algorithm design for multi-criteria problems, and real-world implementations in healthcare and transportation. His projects bridge theoretical advancements with practical solutions for complex decision-making environments.

• Artificial Intelligence
• Machine Learning
• Operations Research
• +۵ مورد دیگر

Dr. Yuan Sun is a Lecturer in Business Analytics and Artificial Intelligence at La Trobe University's La Trobe Business School. His research focuses on leveraging machine learning for combinatorial optimization, including problem reduction methods and hybrid algorithms. He has contributed to top-tier journals like IEEE Transactions on Pattern Analysis and Machine Intelligence and conferences such as ICML and NeurIPS. Sun collaborates with researchers from institutions like Monash University and Singapore Management University, and is affiliated with the ARC Training Centre in Optimisation Technologies (OPTIMA). He co-organized the ACM GECCO 2024 conference and serves on program committees for major AI conferences. Academic Position: Lecturer at La Trobe University (2022–present) Education: PhD in Artificial Intelligence (University of Melbourne), BSc in Applied Mathematics (Peking University) Research interests span machine learning, operations research, and optimization, with notable work on integrating AI with digital twins for sustainable power grids and federated learning frameworks like F3KM. Sun's methods enhance constraint programming and ant colony optimization through supervised learning, addressing complex real-world problems. Grants and collaborations include the 'Quantum Enhanced Optimisation for Energy Efficient Data Centres' project and consulting for the Australian mining workforce analysis. Teaching roles include Algorithms & Analysis and Evolutionary Computing, with joint supervision of PhD projects on combinatorial optimization and mixed-integer programming.

Frank Neumannمشاهده پروفایل

استاد

• Artificial Intelligence
• Bio-inspired Computing
• Cybersecurity
• +۳ مورد دیگر

Frank Neumann is a Professor in the School of Computer Science at The University of Adelaide. He leads the Optimisation and Logistics group and holds affiliations with the University of Melbourne as an Honorary Professorial Fellow. His expertise spans artificial intelligence, bio-inspired computing, and optimization. He has held leadership roles, including interim Head of School and Associate Dean (Research) in the Faculty of Engineering, Computer and Mathematical Sciences. Education: PhD in Computer Science (2006), Diplom (2002) from Christian-Albrechts-University of Kiel, Germany. Postdoctoral research at Max Planck Institute for Informatics (2006-2010). Research interests focus on evolutionary computation, cybersecurity, renewable energy optimization, and supply chain management. He has contributed to foundational theory in randomized search heuristics and practical applications in logistics and healthcare. Recent projects include NHMRC grants on personalized time-use interventions and ARC-funded initiatives in optimization and complex resource management. He has authored influential books on bio-inspired computation and theoretical evolutionary algorithms. Scientific Awards: ARC Future Fellowship, Humboldt Fellowship, NHMRC Grants. Grants: ARC Industrial Transformation Training Centre, Research Consortia Program.

Maximilien Gadouleauمشاهده پروفایل

دانشیار

• Coding theory
• Network coding
• Information theory
• +۱۳ مورد دیگر

Dr. Maximilien Gadouleau is an Associate Professor in the Department of Computer Science at Durham University, where he has been working since January 2012. He is a member of the Algorithms and Complexity research group, with secondary affiliations in Networks Engineering Science and Theory and Scientific Computing. Dr. Gadouleau received his MSc and PhD in Computer Engineering from Lehigh University in December 2005 and May 2009, respectively. After graduation, he held postdoctoral research positions at Université de Reims Champagne-Ardenne (until May 2010) and Queen Mary, University of London (until December 2011). Dr. Gadouleau's research spans multiple areas of theoretical computer science and discrete mathematics. His work focuses on the intersection of coding theory, network coding, and information theory with various branches of mathematics including combinatorics, graph theory, discrete optimization, matroid theory, logic, and group theory. He also investigates applications to network design, data storage, cryptography, data compression, and computation models. His recent work has particularly emphasized Boolean networks, finite dynamical systems, and semigroup theory. Dr. Gadouleau's extensive publication record demonstrates consistent contributions to theoretical computer science, with recent work showing a growing emphasis on the connections between discrete mathematics, dynamical systems, and coding theory. His research often bridges theoretical foundations with practical applications in network communication and data processing. Carnegie Trust for the Universities of Scotland: Research Assessor (2018) EPSRC Peer Review College member (2017) Dr. Gadouleau has successfully supervised PhD students including Izzy Friedlander (graduated July 2024) and Karl Southern (graduated July 2023). He was awarded an EPSRC first grant titled "Memoryless computation and network coding" which started on October 1, 2013. This grant supported research on computational models without memory and their applications to network coding problems. Dr. Gadouleau is an active member of the academic community, serving on editorial boards for journals including "Design, Codes, and Cryptography," "AIMS Mathematics," and "Mathematics and Systems Science." He regularly participates in international conferences and workshops, particularly in the areas of cellular automata, Boolean networks, and coding theory.

• computational mechanics
• topology optimisation
• machine learning
• +۷ مورد دیگر

Dr. Chi Wu serves as a Lecturer in Mechanical Engineering at the University of Newcastle's School of Engineering. With a PhD from the University of Sydney (2022) and postdoctoral experience there (2023-2024), he has established himself as a rising researcher in computational mechanics and advanced manufacturing. His work bridges engineering principles with biomedical applications, particularly in the development of machine learning-driven approaches for material and structural optimization. Education: Doctor of Philosophy, University of Sydney Dr. Wu's research spans computational mechanics, topology optimization, machine learning, biomechanics, and advanced manufacturing, with particular emphasis on developing novel machine learning-based approaches for design optimization of advanced materials and structures. His work focuses on creating functionally graded tissue scaffolds, optimizing composite structures, and developing phase field models for fracture analysis. His research experience spans academia, industry, and clinical applications, demonstrating strong interdisciplinary connections. Analysis of Dr. Wu's publication record reveals a strong focus on integrating machine learning with additive manufacturing, particularly for biomedical applications. His work shows increasing sophistication in combining computational mechanics with experimental validation, with recent publications emphasizing time-dependent optimization and multi-scale modeling approaches. The trend indicates growing recognition of his work in both computational mechanics and biomedical engineering communities. Scientific Awards: Acta Journal Award (2023) Inaugural Grant Steven Award for Early Career Researchers (2023) Best Paper Award at the 12th International Conference on Structural Integrity and Failure (2021) Wiley Top Cited Article Award for 2020-2021 Best Paper Award at the 4th Australasian Conference on Computational Mechanics (2019) Dr. Wu actively recruits PhD students for research in mechanical, materials, and manufacturing engineering, with particular interest in candidates with computational mechanics and optimization backgrounds. He co-supervises students with Prof. Qing Li (ARC Future Fellow, Highly Cited Researcher) at the University of Sydney and Dr. Jianguang Fang (ARC Future Fellow) at the University of Technology Sydney. The University of Newcastle offers various scholarships, with additional support available for candidates from mainland China applying for the CSC scholarship. Dr. Wu maintains extensive collaborative networks across multiple institutions including Harvard University, Max Planck Institute, University of Exeter, Tohoku University, and several Australian universities and medical institutions including Chris O'Brien Lifehouse cancer hospital and industry partners like Cochlear Australia and Zimmer Biomet Australia.

• Structural integrity assessment
• Solid mechanics and modeling
• Finite element analysis
• +۷ مورد دیگر

Dr. Tugrul Comlekci is a Principal Knowledge Exchange Fellow at the Mechanical and Aerospace Engineering Department within the Faculty of Engineering at the University of Strathclyde. He is also affiliated with the Weir Advanced Research Centre (WARC), where he specializes in research and knowledge exchange activities with industrial applications. As a chartered mechanical engineer (CEng MIMechE), his work bridges academic research with practical engineering solutions for industry. Dr. Comlekci's research focuses on structural integrity assessment, solid mechanics, and modeling, with expertise in developing both numerical (particularly finite element analysis) and experimental methodologies. His work has significant applications in: Pressure equipment for oil and gas industry applications Energy generation applications, particularly wind turbine structures Welding simulation for naval vessel fabrication Fatigue and corrosion fatigue analysis His research portfolio demonstrates a strong emphasis on practical industrial applications, with notable contributions including: Patented fracking pump fluidend valve seat design that improved pump fatigue life Autofrettage optimization solutions applied to numerous pump designs Wind turbine life assessment methodologies developed for SSE/Scottish Power Weld distortion analysis methodologies for BAE Systems Dr. Comlekci has successfully translated research into industrial practice, with technologies transferred to Weir SPM and methodologies adopted by major industry players. His publication record shows consistent output in high-impact journals focusing on structural steel performance, fatigue analysis, and crack propagation. As an educator, Dr. Comlekci teaches across all levels, with interests in structural integrity, computer-aided engineering design, finite element analysis (using ANSYS, PTC Creo, SolidWorks), aeromechanical engineering, and engineering business strategic analysis. He supervises undergraduate, MSc, and PhD projects, contributing to the development of next-generation engineers. His current research projects include: Development of a DEM tool for wear prediction and design optimisation of GET (2024-2027) Development of Sustainable Mining Equipment using Novel Fracture Mechanics based Structural Design Optimisation (2023-2026) Wind turbine fatigue life assessment and modelling (2016-2018) APESA (H2020 MSCA EID) project (2015-2019) Dr. Comlekci works closely with industry partners including Weir Group, SSE/Scottish Power, and BAE Systems, ensuring his research has direct practical applications and impact in the engineering sector.

• Corrosion Engineering
• Wear Prediction
• Materials Science
• +۳ مورد دیگر

Ashlee Espinoza serves as a W A R C Knowledge Exchange Fellow at the University of Strathclyde within the Department of Mechanical and Aerospace Engineering, Faculty of Engineering. She concurrently holds visiting researcher positions at Queen Mary University of London (effective March 2025) and the University of Glasgow (effective September 2024). Her primary research focuses on critical materials engineering challenges: Corrosion characterization of aluminum alloys and metallic materials Development of DEM-based wear prediction tools for ground engagement tools (GET) Sustainability optimization in minerals processing through natural rubber applications Innovative corrosive wear testing platform development Dr. Espinoza actively contributes as Co-investigator on two major EPSRC-funded projects extending through 2027: Development of a DEM tool for wear prediction and design optimisation of GET (January 2024-January 2027) with Principal Investigator Ali Anwar Development of a Novel Corrosive Wear Testing Platform (January 2024-January 2027) with Principal Investigator Alexander Galloway Her professional engagement includes keynote presentations on sustainable minerals processing, consultancy on aluminum corrosion systems, and participation in national innovation workshops including the EPSRC Critical Mass Scoping Workshop and Future of Hydrogen Innovation initiatives.

Alexey Ignatievمشاهده پروفایل

دانشیار

• Artificial Intelligence
• Formal Methods
• Satisfiability Modulo Theories (SMT)
• +۳ مورد دیگر

Alexey Ignatiev is an Associate Professor in the Optimisation research group at Monash University's Faculty of Information Technology. Previously, he was a postdoctoral researcher and researcher at the University of Lisbon's Faculty of Sciences, focusing on SAT/SMT-based decision procedures. He holds a Ph.D. from the Matrosov Institute for System Dynamics and Control Theory (Russian Academy of Sciences), where his thesis explored parallel CDCL-BDD integration. His research emphasizes formal methods in AI, including explainable AI (XAI), SAT-based reasoning, and optimization for applications like software upgradability, model-based diagnosis, and fault localization. His work spans over 100 publications, with notable contributions to MaxSAT solving (RC2 solver), neuro-symbolic frameworks (NEUSIS), and rigorous explanations for machine learning models. He has collaborated extensively with institutions like the University of Lisbon and Monash University, contributing to advancements in formal verification and interpretable machine learning.

Johan Wesselooمشاهده پروفایل

استاد

• Mining geotechnical engineering
• Mining induced seismicity
• Rock Mechanics
• +۶ مورد دیگر

Johan Wesseloo is a Professor at The University of Western Australia, affiliated with the School of Engineering and the Australian Centre for Geomechanics. His academic credentials include a BE, ME, and PhD from the University of Pretoria. His research focuses on critical aspects of mining geomechanics with emphasis on safety and optimization in mining operations. Professor Wesseloo's research interests span mining geotechnical engineering, mining-induced seismicity, rock mechanics, rock engineering, and ground support systems. His work particularly addresses mine-induced seismicity and rockburst behaviour, risk-based geotechnical design, and both open pit and underground geomechanics. His research integrates theoretical modeling with practical applications to solve complex mining challenges. His publication portfolio demonstrates a strong focus on seismic risk assessment, rock failure mechanisms, and optimization of mining operations. Recent work shows increasing attention to probabilistic approaches in geomechanical design and the integration of numerical modeling with field observations to improve mining safety and efficiency. SAIMM Gold Medal (2020) SAIMM Gold Medal (2017) SAIMM Silver Medal (2015) SAIMM Gold Medal (2006) Professor Wesseloo leads an extensive research program with significant industry funding, currently managing multiple active projects through 2028 with major mining companies including Gold Fields, OZ Minerals, Newmont, Agnico Eagle Mines, and Alamos Gold Inc. His research group focuses on mining-induced seismicity and rockburst risk management in hard rock mines. He has also contributed substantially to academic discourse through editorial roles for major conferences including Ground Support 2023, the Second International Conference on Underground Mining Technology, the 1st International Conference on Mining Geomechanical Risk, and the 8th International Conference on Deep and High Stress Mining. The Australian Centre for Geomechanics serves as the primary research hub for Professor Wesseloo's work, where he leads the mXrap development project, Ground Support Systems Optimisation (GSSO), probabilistic stope support design initiatives, and physical and numerical modeling of caving geomechanics. His team collaborates extensively with industry partners to translate research findings into practical mining applications.

Leonid Chindelevitchمشاهده پروفایل

استادیار

• Genetics
• Microbiology
• Numerical and Computational Mathematics
• +۶ مورد دیگر

Leonid Chindelevitch is an Assistant Professor in Infectious Disease Epidemiology at the School of Public Health, part of the Faculty of Medicine at Imperial College London. He is affiliated with the Artificial Intelligence Network, the Centre for eXplainable Artificial Intelligence (XAI), and the MRC Centre for Global Infectious Disease Analysis. His research focuses on mathematical and computational modeling of antimicrobial resistance in infectious diseases, combining molecular-level analyses (computational biology, systems biology) with population-level approaches (epidemiology, population genetics). He also applies science to policy to improve healthcare outcomes, particularly in low-resource settings. Education: PhD in Applied Mathematics from MIT (supervised by Bonnie Berger) BSc in Mathematics and Computer Science from McGill University Research Interests: Dr. Chindelevitch’s work spans computational biology, algorithm development, discrete optimization, machine learning, and artificial intelligence. He investigates genomic determinants of drug resistance in pathogens like M. tuberculosis and B. burgdorferi , leveraging methods such as exact optimization and deep neural networks. His team integrates MLST typing and tandem repeat copy number analysis to understand resistance mechanisms. Awards: Alfred P. Sloan Research Fellowship (2015) Advising & Grants: Previously a faculty member at Simon Fraser University (2015–2020), he transitioned to Imperial College in 2020. He consults for the Foundation for Innovative New Diagnostics on M. tuberculosis drug resistance and co-led a global genomics catalog project. His industry experience includes computational roles at Pfizer and the Massachusetts General Hospital. Labs & Teams: Active in the MRC Centre for Global Infectious Disease Analysis and collaborates with the Imperial College’s AI Network to advance explainable AI in healthcare.

• Graph Theory
• Combinatorics
• Mathematical Analysis
• +۲ مورد دیگر

Alex Newcombe serves as a Lecturer in the College of Science and Engineering at Flinders University, where he also holds a Research Staff position with the Centre for Defence Engineering Research and Training. His academic profile shows strong connections between theoretical mathematics and practical defense applications. His educational background includes: PhD in Mathematics from Flinders University (2019) Bachelor of Science with First Class Honours in Mathematics from Flinders University (2015) Newcombe's research centers on graph theory and combinatorics, with particular expertise in domination problems, crossing numbers, and Cartesian graph products. His work bridges theoretical mathematics with practical applications in defense and geolocation systems through collaborations with the Centre for Defence Research and Training. His publications demonstrate sophisticated approaches to complex graph structures using methods like cross-entropy optimization and binary programming formulations. Analysis of his recent publications reveals a consistent research trajectory focused on domination variants and graph product structures. His work shows increasing sophistication in handling complex graph problems with applications to network security and optimization. The publications span specialized mathematics journals including Entropy, Journal of Combinatorial Mathematics and Combinatorial Computing, and Ars Mathematica Contemporanea. His notable achievements include: Vice Chancellor's Award for Doctoral Thesis Excellence (2019) Participation in the Artificial Intelligence for Decision Making Initiative through the Defence Innovation Partnership (2020-2022) Newcombe teaches several engineering mathematics courses including ENGR2711 Engineering Mathematics, MATH3703 Optimisation, and ENGR8761 Engineering Mathematics. His research grants indicate strong institutional support for his work at the intersection of theoretical mathematics and defense applications. The Centre for Defence Engineering Research and Training serves as his primary research hub, facilitating collaborations between academic mathematics and practical defense challenges. His research activities are centered at the Tonsley campus of Flinders University, working within the College of Science and Engineering's research ecosystem. The Centre for Defence Engineering Research and Training provides the institutional framework for his defense-related collaborations, connecting mathematical theory with real-world security applications.

Marthe Bonamyمشاهده پروفایل

پژوهشگر

• Combinatorics
• Graph theory
• Planar graphs
• +۶ مورد دیگر

Marthe Bonamy is a CNRS researcher at LaBRI (Laboratoire Bordelais de Recherche en Informatique) in Bordeaux, France, holding a permanent research position since December 1, 2015. She is a member of the CombAlgo team, head of the Graphes et Optimisation research group, and also a member of the AlgoDist group. Her research focuses on combinatorics and graph theory, particularly planar graphs, algorithms, graph decompositions, and reconfiguration problems. Dr. Bonamy is actively involved in the academic community, serving as vice-Editor-in-chief of the European Journal of Combinatorics since January 2021, a member of the Board of Editors of Discrete Mathematics since January 2021, an editor of Innovations in Graph Theory since August 2023, and an associate editor of the SIAM Journal of Discrete Mathematics since January 2024. She is also part of the scientific board of the GT COA and the steering committee of STACS. Her research interests span various aspects of combinatorics and graph theory, with a particular emphasis on structural graph properties, coloring problems, and reconfiguration processes. She has made significant contributions to understanding graph recoloring, Kempe changes, and various decomposition techniques for graphs. Her work often bridges theoretical computer science and pure mathematics, providing both theoretical insights and algorithmic applications. Dr. Bonamy has supervised numerous PhD students, both currently and in the past, demonstrating her commitment to mentoring the next generation of researchers in combinatorics and graph theory. Her students have gone on to academic positions and industry roles, reflecting the breadth of applicability of their research. She has received recognition through prestigious editorial positions and research grants, including her current involvement in the ANR Project ENEDISC (2024-2027) and previous leadership of multiple research projects. Dr. Bonamy maintains an active research agenda with numerous publications in top-tier journals and conferences, reflecting her standing as a leading researcher in combinatorics and graph theory.

• Optimisation Under Uncertainty
• Discrete Choice
• Social Choice
• +۴ مورد دیگر

Chung Piaw Teo is the Stephen Riady Professor and Executive Director of the Institute of Operations Research and Analytics (IORA) at the National University of Singapore (NUS) Business School. He has held significant academic roles including Head of Department, Acting Deputy Dean, Vice-Dean of Research and Ph.D. Programs, and Chair of the Ph.D. Committee at NUS. PhD in Operations Research from MIT (1996) Bachelor of Science (Honors) in Mathematics from NUS (1990) Research Interests: Optimisation Under Uncertainty Discrete Choice Modeling Social Choice Theory Inventory Theory Supply Chain Management Combinatorial Optimisation Operations Research Publications focus on stochastic optimization, supply chain resilience, and network design, with recent works spanning 2020–2015 in journals like Management Science , Operations Research , and Mathematical Programming . Key themes include urban logistics, risk mitigation, and decision analytics. Scientific Awards: Stephen Riady Professor (2024) Provost’s Chair (2014) Faculty Outstanding Researcher Awards (2014, 2006, 2003) Nominee for University Outstanding Researcher Award (2006) Grants & Leadership: Served on international committees (INFORMS, LANCHESTER Prize, Fudan Prize) and held visiting/fellow positions at MIT, Northwestern, and Sungkyunkwan University. Currently a department editor for Management Science and associate editor for multiple journals.

• Numerical linear algebra
• Matrix polynomials and spectral theory
• Stability and perturbation analysis
• +۳ مورد دیگر

Aleksandr Malyshev is Professor of Mathematics at the University of Bergen. His research integrates numerical linear algebra, stability theory, optimisation-based control, and image-processing algorithms, yielding a portfolio of more than 60 peer-reviewed articles and conference contributions. Education & affiliations: Professor, Department of Mathematics, University of Bergen, Norway (present) Previous research and teaching engagements in informatics and applied mathematics at the same university Research interests: Malyshev’s core interest is the theoretical and algorithmic analysis of matrix problems arising in stability, control and imaging. He develops numerically reliable tools for assessing the distance to instability of dynamical systems, constructs preconditioners that accelerate optimisation solvers in real-time model predictive control, and designs variational models for 3-D reconstruction and image denoising. His work frequently combines spectral theory of matrix polynomials with practical issues such as high-performance implementation and medical-image quantification. Across the last decade his articles reveal three dominant strands: (i) stability and perturbation of time-delay and periodic systems, (ii) preconditioned iterative solvers for interior-point and MPC formulations, and (iii) variational and learning-based approaches to depth estimation, surface reconstruction and glenoid-bone assessment. These themes are unified by a common mathematical substrate—exploitation of matrix structure to obtain computationally efficient, numerically trustworthy solutions. Scientific awards & recognition: Regular invited speaker at international workshops on numerical linear algebra and control (e.g., SK Godunov conference 2009, IFAC 2018) Funded principal investigator / co-investigator on Research Council of Norway and EU Horizon Europe grants Advising & grants: Malyshev has supervised numerous MSc and PhD candidates in numerical analysis and scientific computing and currently advises graduate researchers on projects ranging from 3-D machine-vision algorithms to Krylov-subspace preconditioning. Recent grant participation includes EU project 101373 (3-D quantification of glenoid bone loss) and the Norwegian Research Council project 262203 on perfusion-flow simulation. Labs & collaboration: He collaborates closely with the Group for Numerical Methods and Applications at UiB, the Visual Computing cluster at the Department of Informatics, and maintains international partnerships with the Universities of Brest, Lübeck, and several US institutions. These joint efforts feed cross-disciplinary projects combining rigorous matrix analysis with real-world applications in biomechanics, process control, and computer vision.