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Lam JamesView profile

Professor

• Networked control systems
• Vibration control
• Control theory
• +7 more

Lam James is a Chair Professor of Control Engineering at the University of Hong Kong (HKU), affiliated with the Faculty of Engineering. He holds a BSc (1st Hons.) in Mechanical Engineering from the University of Manchester, and MPhil/PhD degrees from the University of Cambridge. His academic journey includes roles as a Croucher Fellow, Lecturer at the University of Melbourne, and faculty member at City University of Hong Kong before joining HKU in 1993. Professor Lam serves as Editor-in-Chief for four international journals including IET Control Theory and Applications and Journal of The Franklin Institute . His research focuses on networked control systems, vibration control, control theory, and multi-agent systems. With 600+ peer-reviewed publications, he maintains an H-index of 102 (Web of Science) and 108 (Scopus), recognized as a Highly Cited Researcher in multiple fields. Education: BSc Manchester (Mechanical Engineering), MPhil/PhD Cambridge Editorial Roles: 20+ journals, including leadership roles since 2012 His awards include Foreign Membership of Academia Europaea (2024), National Academy of Artificial Intelligence membership (2025), and two State Natural Science Awards (2015, 2019). He is a Fellow of multiple institutions including IEEE, IMechE, and HKIE.

Vladimir KazeevView profile

Assistant Professor

• Numerical Analysis
• Tensor Methods
• Partial Differential Equ to the data-driven solution of PDEs
• +3 more

Vladimir Kazeev is an Assistant Professor at the Faculty of Mathematics, University of Vienna , where he has held a faculty position since 2019. He also held previous academic appointments as a Szegő Assistant Professor at Stanford University (2017–2019), a postdoctoral researcher at the University of Geneva (2015–2017), and research positions at ETH Zurich (2011–2015), Russian Academy of Sciences (2008–2011), and Moscow Institute of Physics and Technology (2009). His research focuses on adaptive, data-driven numerical methods for differential equations, nonlinear low-parametric approximation, and numerical linear algebra. His work intersects computational mathematics, tensor methods, and high-dimensional problem-solving, particularly in the context of partial differential equations (PDEs) and stochastic modeling. The 15 most recent publications reveal a strong emphasis on quantized tensor-structured methods for PDEs, low-rank approximations, and high-dimensional numerical analysis. His research spans theoretical advancements in tensor decomposition, practical applications in chemical reaction networks, and novel discretization techniques for multiscale and degenerate diffusion problems. Scientific awards include the prestigious ETH Medal for outstanding doctoral theses (2016) Russian Academy of Sciences Medal for outstanding student works in mathematics (2011) Advising and teaching activities include supervising Jason Zhu (Stanford, 2019) and Simon Etter (ETH Zurich, 2014), as well as teaching advanced courses in tensor methods, numerical analysis, and PDEs at the University of Vienna, Stanford University, and the University of Geneva. His service to the community includes peer review for 15+ journals and co-organizing minisymposia at SIAM meetings.

• Numerical Analysis
• Partial Differential Equations
• Finite Element Method
• +3 more

Dirk Praetorius is a Professor of Numerics of Partial Differential Equations (PDEs) at the Technische Universität Wien (TU Wien) , affiliated with the Institute for Analysis and Scientific Computing (ASC) within the Faculty of Mathematics and Geoinformation . He leads the research group on Numerics of PDEs and has held various leadership roles, including Institute Director (since 2020) and head of the Numerics research area. His work focuses on numerical methods for PDEs, including Finite Element Methods (FEM), Boundary Element Methods (BEM), adaptive algorithms, and computational micromagnetics. Education and Career: Praetorius earned his Diplom in Mathematics (2000) and PhD in Applied Mathematics (2003) from TU Wien, followed by a Habilitation in Numerical Analysis (2005). He has been a faculty member at TU Wien since 2005, progressing from Assistant Professor to full Professor in 2017. He has also held visiting positions at institutions such as the University of Jyväskylä and RICAM (Linz). Research Interests: His research spans numerical analysis, adaptive FEM/BEM, a-posteriori error estimation, matrix compression, and computational micromagnetics. He has contributed to modeling spin dynamics, magnetic skyrmions, and multiscale systems. His work emphasizes efficient algorithms for large-scale problems and optimal computational complexity. Awards and Editorial Roles: Praetorius received the TU Best Teacher Award (2021) and TU Best Lecture Award (2019). He serves as Senior Editor for Computational Methods in Applied Mathematics (CMAM) and on the editorial board of Applied Numerical Mathematics (APNUM) . He co-founded the outreach initiative TUForMath to promote mathematics education. Grants and Projects: He leads or co-leads several research projects funded by the Austrian Science Fund (FWF), including the collaborative SFB "Taming Complexity in Partial Differential Systems" (2017–2025) and international collaborations with Germany. His work addresses topics like functional error estimates, nonlinear PDEs, and computational design of magnetic devices. Labs and Teams: He contributes to the ASC Institute and coordinates interdisciplinary projects involving computational physics and engineering. His team develops software tools like MooAFEM and Commics for micromagnetic simulations.

Peter ZollerView profile

Professor

• Theoretical Physics
• Quantum Optics
• Many-Body Quantum Physics
• +3 more

Peter Zoller is a Professor of theoretical physics at the University of Innsbruck and Scientific Director at IQOQI Innsbruck (Austrian Academy of Sciences). His research focuses on quantum optics, many-body quantum physics, and quantum information science, with a strong emphasis on quantum simulation of gauge theories and atomic systems. He has trained 34 PhD students and hosted 57 postdoctoral researchers, fostering collaborations between theory and experiment. His group, the Zoller Group, explores quantum phenomena such as lattice gauge theories, entanglement dynamics, and topological order using advanced quantum simulation techniques. Key research interests include atomic physics, quantum gases, and applications of quantum technologies to high-energy physics problems. Recent work addresses string breaking in quantum simulators, entanglement Hamiltonians, and scalable architectures for fermionic quantum processors. Collaborations span institutions like Harvard, MIT, and the University of Innsbruck’s experimental teams. His contributions bridge foundational physics with cutting-edge quantum technologies, aiming to solve problems inaccessible to classical methods.

• Antenna Design
• Microwave Engineering
• Dielectric Materials
• +2 more

Wolfgang Bösch is a Professor at Graz University of Technology's Institute of Microwave and Photonic Engineering, specializing in advanced RF components and measurement techniques. His research advances high-frequency systems through innovations in antenna technology and electromagnetic theory. Research domains include: metamaterial-based antennas, precision measurement calibration, microwave filter optimization, and 3D-printed RF components. Recent work demonstrates strong focus on millimeter-wave systems and reconfigurable antenna arrays. Publications highlight expertise in: machine learning for filter design, metasurface applications, PCB transitions for high-frequency systems, and uncertainty quantification in RF engineering. Research consistently addresses miniaturization and performance optimization challenges. Awards recognize contributions to measurement science and antenna design: Fellow of IET, Houska Prize, and best paper awards. Current laboratories investigate liquid crystal antenna systems and error calibration methodologies for next-generation wireless systems.

Efstathia BuraView profile

Professor

• Dimension Reduction in Regression
• High-Dimensional Statistics
• Multivariate Analysis
• +5 more

Efstathia Bura is a Professor heading the Applied Statistics Research Unit (ASTAT) within the Institute of Statistics and Mathematical Methods in Economics at TU Wien's Faculty of Mathematics and Geoinformation. Her research focuses on dimension reduction techniques in regression and classification, high-dimensional statistics, and their applications in biostatistics, econometrics, and legal statistics. She leads projects like ProbInG (WWTF-funded) and the SecInt Doctoral College on statistical verification of cyber-physical systems. Her work integrates advanced statistical methodologies with interdisciplinary applications, emphasizing practical solutions for complex data challenges. Current projects explore probabilistic program analysis, security properties in cyber-physical systems, and dynamic econometric modeling. She collaborates internationally, with notable contributions to statistical theory and applications in law, healthcare, and telecommunications. Key research themes include time-varying regression models, sufficient dimension reduction for mixed predictors, and fusion of statistical methods with machine learning. Her publications bridge theoretical advancements and real-world problem-solving, reflecting her role as a leading academic in modern applied statistics. Her team includes postdocs and assistants working on WWTF and SecInt grants, focusing on probabilistic systems and statistical verification. While no formal student advisees are listed, her collaborative projects engage junior researchers in cutting-edge statistical research.

• Optimization
• Convex Analysis
• Nonlinear Analysis
• +3 more

Radu Ioan Bot is a Professor and Dean of the Faculty of Mathematics at the University of Vienna, where he also serves as Head of the Department of Mathematics. His primary affiliations include the Department of Mathematics (Oskar-Morgenstern-Platz 1, 1090 Wien) and the Research Network Data Science (Währinger Straße 29, 1090 Wien). Bot's research centers on optimization theory with emphasis on convex/nonconvex optimization, monotone operators, and dynamical systems. He develops fast algorithms for variational inequalities and monotone inclusions by bridging continuous-time dynamics with discrete optimization methods. His work frequently addresses bilevel optimization, Tikhonov regularization, and second-order dynamics, yielding accelerated convergence rates for complex problems. Analysis of his 15 most recent publications (2023-2025) reveals dominant trends in time-scaling techniques, vanishing damping dynamics, and structured splitting methods. Key contributions include unifying Nesterov acceleration with Heavy Ball dynamics, developing reflected forward-backward algorithms for constrained optimization, and establishing strong convergence guarantees for monotone operator flows. These advances demonstrate consistent innovation in accelerating optimization while maintaining theoretical rigor. No scientific awards were mentioned in the provided source material. Details regarding student advising and research grants were not specified in the available information, though his leadership roles as Dean and Department Head indicate significant administrative responsibilities alongside active research. Bot participates in the University of Vienna's Research Network Data Science, suggesting interdisciplinary engagement in data-driven methodologies with potential applications in machine learning and computational mathematics.

• Scientific Workflow Management
• Distributed Computing
• Numerical Linear Algebra
• +3 more

Wilfried Gansterer is a Professor at the Faculty of Computer Science, University of Vienna, leading the Theory and Applications of Algorithms research group. His work focuses on numerical algorithms, distributed computing, and machine learning, with notable contributions to graph neural networks and fault-tolerant systems. Active in projects such as Algorithmic Data Science for Computational Drug Discovery (2020–2028) and REPEAL (Resilience vs. Performance in Numerical Linear Algebra, 2016–2020). Research Interests: Dr. Gansterer’s expertise spans graph neural networks, matrix compression, adversarial defense mechanisms, and high-performance computing. His work addresses challenges in efficient computation, resilience against node failures, and optimizing distributed systems. Projects : Algorithmic Data Science for Computational Drug Discovery (2020–2028) REPEAL: Resilience vs. Performance in Numerical Linear Algebra (2016–2020) Verteiltes Rechnen (Distributed Computing, 2007–2014) Awards : 2023 Best Paper Award for work on Crossfire: An Elastic Defense Framework for Graph Neural Networks. Labs/Teams : Directs the Theory and Applications of Algorithms group, focusing on algorithmic innovation in distributed and high-performance computing environments.

• Quantum Information
• Quantum Computing
• Quantum Many-Body Physics
• +9 more

Univ.-Prof. Dr. Norbert Schuch is a Professor of Physics and Mathematics at the University of Vienna, leading the Quantum Information and Quantum Many-Body Physics group. His research bridges Quantum Information Theory, Quantum Computing, and the study of complex quantum many-body systems, with a focus on Tensor Networks, Topological Order, and Symmetry Breaking. He has offices at both the Faculty of Physics (Boltzmanngasse 9) and Faculty of Mathematics (Oskar-Morgenstern-Platz 1). Research Interests : Quantum Information at the interface of Many-Body Physics, including Entanglement Theory, Topological Quantum Computation, Tensor Network algorithms, and Symmetry-Protected Topological (SPT) phases. His work develops numerical and analytical frameworks to study entanglement order parameters and prepare/experiment with topological states in quantum simulators. Teaching : Courses on Quantum Information, Quantum Computing, Theoretical Physics, and seminars on quantum many-body topics. Prior to Vienna, he was a tenured group leader at Max-Planck-Institute of Quantum Optics and a lecturer at Technical University Munich. Scientific Awards : ERC Consolidator Grant SEQUAM (2020–2025) FWF ESPRIT Programme ESP 306 FWF SFB BeyondC FWF Entanglement Order Parameters Research Trends : His recent publications explore Quantum Algorithms, Tensor Networks for Topological Phase Transitions, Entanglement Spectra, and Symmetry-Protected Phases. Key subfields include Non-Abelian Anyons, Chiral Spin Liquids, and Computational Complexity in Many-Body Systems. Group Members : Current team includes postdocs like Dr. Ilya Kull and Dr. András Molnár, with historical alumni spanning PhDs, Masters, and BSc students now at institutions like Xanadu, MIT, and Quantinuum.

• Bayesian Statistics
• Cluster Analysis
• Data Science
• +5 more

Bettina Grün is an Associate Professor and Deputy Head of the Institute for Statistics and Mathematics at Vienna University of Economics and Business (WU). Her research focuses on Bayesian mixture models, cluster analysis, and applying statistical methods to sustainability, tourism, and environmental studies. She has led multiple research projects, including studies on environmental behavior in tourism and advanced text modeling in economics. Grün holds a PhD in Technical Mathematics from TU Wien (2006) and a Habilitation in Statistics from Johannes Kepler University Linz (2012). She has authored over 150 publications in top journals like Journal of Environmental Management and Expert Systems with Applications . Her work emphasizes practical applications, such as reducing hotel waste through behavioral interventions and developing R packages like movMF and circlus for statistical clustering. Grün has received awards including the AIEST Best Contribution Award (2019) and the MRS Silver Medal (2016). Grün teaches courses on statistical modeling and leads projects like Analysis of Central Bank Communication (2022–2026) and Environmentally Friendly Behavior in Tourism (2019–2024).

• hp-Finite Element Method (hp-FEM)
• Fractional Diffusion Equations
• Boundary Element Method (BEM)
• +3 more

Prof. Jens Markus Melenk is a Professor at TU Wien's Faculty of Mathematics and Geoinformation, leading the Computational Mathematics Research Group (E101-02-1). His research focuses on advanced numerical methods for partial differential equations, with a strong emphasis on hp-FEM (hp-Finite Element Method), fractional diffusion equations, and wave propagation problems. He has contributed significantly to the development of robust and efficient algorithms for complex domains and heterogeneous media. Key research interests include the application of hp-FEM to fractional operators (e.g., integral fractional Laplacian), boundary element methods (BEM), and the analysis of wavenumber-explicit convergence for Maxwell's equations and elastic wave equations. His work bridges theoretical analysis and computational implementation, addressing challenges in multiscale problems and singular perturbations. Notable contributions: Exponential convergence of hp-FEM for fractional Laplacian problems, wavenumber-explicit error estimates for wave equations in heterogeneous media. Collaborations: Active involvement with researchers like Markus Faustmann, Christoph Schwab, and Dirk Praetorius. Supervised theses: Includes works on hp-FEM for fractional operators, RBF interpolation, and error estimators for elliptic PDEs. His research group develops and analyzes numerical schemes for challenging PDE scenarios, with applications in electromagnetism, acoustics, and computational mechanics.

• Analytical Chemistry
• Elemental and Isotopic Analysis
• Inductively Coupled Plasma Mass Spectrometry (ICP-MS)
• +7 more

Anastassiya Tchaikovsky is a postdoctoral researcher at the Institute of Analytical Chemistry, Department of Natural Sciences and Sustainable Resources, University of Natural Resources and Life Sciences, Vienna (BOKU). She holds a PhD in analytical chemistry and has an extensive research background in elemental and isotopic analysis, particularly using ICP-MS techniques. Her work spans food authentication, environmental forensics, and trace metal speciation. Her research interests include: Inductively Coupled Plasma Mass Spectrometry (ICP-MS) Chemometrics and multivariate data analysis Isotopic and elemental fingerprinting for provenance determination Metrology and method validation Separation techniques coupled to mass spectrometry Applications in food science, ecology, and human health Her recent publications demonstrate a strong trend toward interdisciplinary applications of analytical chemistry, particularly in verifying the geographical origin of food products (e.g., carrots, caviar, fish) using combined isotopic, elemental, and metabolomic fingerprints. She frequently employs chemometric modeling and data fusion techniques to enhance discrimination power. Her work also extends to biomedical applications, such as iron metabolism in neurodegenerative diseases and lead detoxification. She has received multiple scientific awards, including: Scholarship for a postdoc mentoring program (2019) Best talk award at DocDay, Tulln (2014) ESF-Studienabschluss scholarship (2013) Merkur scholarship from TU Vienna (2013) Best student lecture award at ICPMS Anwendertreffen (2012) Science scholarship from TU Vienna (2012) She has been involved in several research projects funded by the European Commission, Austrian federal and local governments, and private institutions. Her collaborative work includes advising on fish migration, caviar traceability, and citizen science initiatives like IsoPROTECT. She actively presents her research at international conferences and contributes to knowledge transfer in food safety and analytical methodology. She is affiliated with the Institute of Analytical Chemistry at BOKU and has no listed advisees, though she collaborates extensively with senior researchers and interdisciplinary teams.

Bart De MoorView profile

Professor

• Numerical Linear Algebra
• Optimization
• Algebraic Geometry
• +11 more

Bart De Moor is a Full Professor at the Department of Electrical Engineering, KU Leuven, Belgium, and a guest professor at the University of Siena. He leads the STADIUS research group and has supervised 85 PhD students. His roles include chairman of Health House (2016–present), member of the Board of VIB (Biotech Institute), and former Vice-Rector for International Policy (2009–2013). Education: Master Degree in Electrical Engineering (1983), KU Leuven PhD in Engineering (1988), KU Leuven Research Interests: His work spans numerical linear algebra, optimization, algebraic geometry, systems and control theory, data-driven AI, machine learning, and applications in process industry and biomedical big data. He has contributed to subspace identification, tensor decomposition, bioinformatics, and quantum computing. Publications Trends: His publications highlight subspace identification methods, tensor decomposition, bioinformatics, and biomedical data analysis. These reflect interdisciplinary advancements in control theory, quantum physics, and mathematical engineering, with applications in industrial and healthcare domains. Scientific Awards and Honors: Leslie Fox Prize (1989) Laureate of the Belgian Royal Academy of Sciences (1992) Bi-annual Siemens Award (1994) Fellow of IEEE (since 2004) Member of the Royal Academy of Belgium for Science and Arts (since 2000) Fellow of IFAC (since 2022) Commander in the Order of King Leopold I (2020) Fellow of SIAM (since 2017) FWO Excellence Award (2010) Advising and Grants: He has led a research group of 20 PhD students and postdocs, co-founded 8 spinoff companies, and secured the ERC Advanced Grant ‘Back to the roots’ (2020–2025). He also co-holds the KU Leuven Chair on healthcare systems (2018–present). Labs and Organizations: Active in the STADIUS research group (KU Leuven), he has served on boards of the Flemish Interuniversity Institute for Biotechnology (VIB), the Alamire Foundation, and the Health Tech Experience Center Health House. His spinoffs include Trendminer, Cartagenia, and Ugentec.

Wenwu ZhuView profile

Professor

• Big data analysis
• Multimedia communication and networking
• Multimedia computing
• +5 more

Wenwu Zhu is a Professor and Vice Chair of the Department of Computer Science and Technology at Tsinghua University. He has held prominent positions at Microsoft Research Asia, Intel Research China, and Bell Labs, establishing himself as a leading figure in multimedia computing and networking with international recognition as a FOREIGN member of the Academy of Europe (elected 2018). His educational background includes: Ph.D. in Electrical and Computer Engineering from New York University (1996) Professor Zhu's research focuses on the intersection of multimedia systems, networking, and big data. His work has pioneered advancements in internet video streaming, multimedia cloud computing, and social-aware content distribution. He has made significant contributions to understanding how multimedia content can be efficiently delivered across diverse network environments, from traditional wired networks to modern mobile and social platforms. His research bridges theoretical computer science with practical applications, with his work on social-aware video content distribution being transferred to Tencent company. His publication record shows a clear evolution from foundational work on internet video streaming in the early 2000s, through multimedia cloud computing in the early 2010s, to more recent work on social-aware multimedia and network embedding using deep learning approaches. This progression reflects the changing landscape of multimedia computing from infrastructure-focused to socially-aware and AI-driven systems. Professor Zhu has received numerous prestigious honors: AAAS Fellow (2016) SPIE Fellow (2013) IEEE Fellow (2010) Minister of Education's Natural Science Award, 1st prize (2017) Chinese Institution of Electronics's Natural Science Award, 1st prize (2015, 2012) National Natural Science Award, 2nd prize (2012) Chief Scientist for NSFC Major Project (2016) Chief Scientist for Ministry of Science and Technology's 973 Project (2014) Multiple Best Paper Awards including ACM Multimedia 2012 As Editor-in-Chief of IEEE Transactions on Multimedia since 2017 and through leadership roles as General Co-Chair for ACM CIKM 2019 and ACM Multimedia 2018, Professor Zhu has significantly shaped the multimedia research community. His research has been supported by major grants including NSFC Major Projects and Ministry of Science and Technology's 973 Projects, demonstrating both academic and national strategic importance. He has published over 300 referred papers with an H-Index of 55, including 6 Best Paper Awards and 7 books or book chapters. Professor Zhu leads a research group at Tsinghua University focused on multimedia big data computing, with strong industry connections. His team has made pioneering contributions to structural network embedding using deep learning and social contextual recommendation systems, bridging theoretical advances with practical applications in social media platforms.

Anders LindquistView profile

Professor

• Mathematical systems theory
• Control theory
• Stochastic realization theory
• +3 more

Anders Lindquist is Zhiyuan Chair Professor at Shanghai Jiao Tong University and Emeritus Professor at KTH Royal Institute of Technology. He earned his PhD from KTH in 1972 and began his career as a postdoctoral fellow at the University of Florida under R.E. Kalman. His academic journey includes positions as Assistant Professor (University of Florida), Associate Professor (University of Kentucky and Brown University), and Full Professor (University of Kentucky and KTH). At KTH, he served as Head of Mathematics Department (2000-2009) and Director of the Center for Industrial and Applied Mathematics (2006-2016). His research focuses on: Mathematical systems theory and control theory Stochastic realization and estimation Spectral estimation methods Moment problems with complexity constraints Applications of operator theory His publications demonstrate consistent focus on mathematical foundations of control systems, stochastic processes, and optimization techniques, with recent work expanding into multidimensional applications and image processing. Major scientific honors include: IEEE Control Systems Award (2020) Reid Prize in Mathematics (2009) Axelby Outstanding Paper Award (2003) Fellowships: IEEE, SIAM, IFAC Memberships: Royal Swedish Academy of Engineering Sciences, Chinese Academy of Sciences He holds four U.S. patents and has served on editorial boards of leading journals including Philosophical Transactions of the Royal Society and SIAM Review.