جست‌وجوی استاد و پژوهشگر

Borivoje Dakicمشاهده پروفایل

دانشیار

• Quantum Information Theory
• Quantum Foundations
• Quantum Tomography
• +۵ مورد دیگر

Dr. Borivoje Dakic is an Associate Professor at the University of Vienna , affiliated with the Faculty of Physics and the Quantum Optics, Quantum Nanophysics and Quantum Information department. His research spans foundational and applied aspects of quantum theory. Operational reconstruction of quantum formalism Quantum interference as a resource for communication Tomography of large-scale quantum systems Macroscopic quantum phenomena His work includes scalable verification techniques for quantum devices and collaborations with experimental teams like Philip Walther’s and Markus Aspelmeyer’s groups. He received the Marko Jarić Prize (2025) for his contributions. Recent projects focus on diagnostics of quantum devices (FWF BeyondC SFB), information-theoretic foundations of quantum interference (FWF P36994), and local operations in quantum field theory (Cluster of Excellence QuantA). His research on quantum coherence in networks and macroscopic entanglement challenges traditional assumptions about quantum-classical boundaries. Publications emphasize resource-efficient tomography, device-independent verification, and foundational frameworks for quantum statistics and field theory. Teaching: Quantum Information (2025W), Theory in Quantum Optics (2025S), VCQ Summerschool Labs: Dakić Group at University of Vienna

• Numerical Analysis
• Partial Differential Equations
• Finite Element Method
• +۳ مورد دیگر

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.

Georgios B. Giannakisمشاهده پروفایل

استاد

• Data Science
• Power Systems
• Network Science
• +۷ مورد دیگر

Georgios B. Giannakis is a Full Professor, Endowed Chair, and Presidential Chair in the Department of Electrical and Computer Engineering at the University of Minnesota since 1999. He directs the Digital Technology Center and has held academic roles at the University of Virginia (1987-1999) and USC (1982-1986). His research spans Data Science, Wireless Communications, Network Science, and Statistical Signal Processing , with applications to IoT and power systems. Diploma in Electrical Engineering, NTUA (1981) MSc in Electrical Engineering, USC (1983) MSc in Mathematics, USC (1986) PhD in Electrical Engineering, USC (1986) His publications (470+ journals, 770+ conferences, 34 patents) focus on fading channel modeling, UWB localization, blind signal estimation, and cross-layer wireless design . Articles emphasize multicarrier systems, time-varying channels, and ultra-wideband communication , with citations exceeding 76,000 (H-index 145). Scientific Awards : EURASIP 'Athanasios Papoulis' Society Award (2020) IEEE Fourier Technical Field Award (2015) Gugliermo Marconi Prize Paper Award (2003) 9 Best Journal Paper Awards (IEEE/SPS & ComSoc) IEEE SPS Technical Achievement Award (2001) He has mentored over 50 PhD students and 25 postdocs, served IEEE as Distinguished Lecturer, and contributed to Greek university accreditation panels. His work bridges theoretical signal processing and practical communication systems .

Markus Haltmeierمشاهده پروفایل

استاد

• Image Reconstruction
• Inverse Problems
• Deep Learning
• +۴ مورد دیگر

Markus Haltmeier is a Professor in the Department of Mathematics at the University of Innsbruck. His research focuses on inverse problems, image reconstruction, and deep learning with applications in medical imaging, photoacoustics, and computational mathematics. He leads a group dedicated to advancing theoretical and practical solutions for challenges in non-destructive testing and medical diagnostics. His work integrates mathematical analysis with machine learning, addressing issues such as high-resolution imaging in scattering media and automated segmentation of cardiac structures. Key research areas include regularization techniques for inverse problems, self-supervised learning approaches for limited data scenarios, and computational methods for photoacoustic tomography. His contributions span both theoretical developments (e.g., inversion formulas for Radon transforms) and applied solutions (e.g., algorithms for cylinder liner wear assessment and myocardial infarct segmentation). Publications highlight advancements in neural network-based regularization, 3D medical image synthesis, and unsupervised learning frameworks for segmentation and registration. His research emphasizes bridging the gap between mathematical theory and real-world applications in healthcare and engineering.

• Optimization and Optimal Control
• Partial Differential Equations
• Inverse Problems
• +۹ مورد دیگر

Prof. Karl Kunisch is the Scientific Director at RICAM (Johann Radon Institute for Computational and Applied Mathematics) and a Full Professor of Mathematics at the University of Graz, Austria. He has held academic positions worldwide, including visiting roles at Brown University, INRIA, and Technical University Berlin. His research focuses on Optimization and Optimal Control, Partial Differential Equations (PDEs), Inverse Problems, and their applications in mathematical imaging, medicine, and computational science. Education: 1975: Diploma Degree, Technical University of Graz, Austria 1975: Master Degree, Northwestern University, Evanston, Illinois, USA 1978: Ph.D. Degree, Technical University of Graz 1980: Habilitation, Technical University of Graz Research Interests: Prof. Kunisch’s work spans theoretical and applied aspects of optimal control, including stabilization of PDEs, infinite horizon control problems, and feedback design. He explores numerical methods for PDE-constrained optimization and their applications in medical imaging, cardiac electrophysiology, and machine learning. His projects also address shape optimization and mathematical models for fluid dynamics and quantum systems. Publications Trends: His recent articles emphasize feedback stabilization for nonlinear systems, sparse control approaches, and the intersection of optimal control with machine learning. Key themes include robust algorithms for uncertainty handling, efficient numerical methods for high-dimensional problems, and applications in biomedical engineering. Awards: Pro Scientia-Scholarship (1974–1977) Research Award of Theodor-Körner-Fonds (1979) Fulbright Travel Scholarship (1979/80, 1985) Max Kade Scholarship (1982–83) Japanese Society for the Promotion of Science Fellowship (1990) Christian Doppler Laboratory Fellowship (1992) Advising & Grants: Prof. Kunisch leads the Optimization and Optimal Control research group at RICAM and has directed projects on mathematical data science and inverse problems. His work involves collaborations with institutions globally and has been supported by grants from NASA, the European Union, and national funding bodies. He has advised numerous researchers, though specific student names are not listed here. Labs/Teams: Group Leader of the Group "Optimization and Optimal Control" at RICAM since 2004, contributing to interdisciplinary research in computational mathematics and its applications.

Nicole Grobertمشاهده پروفایل

استاد

• Nanomaterials
• Hierarchical nanostructures
• Nanoparticles
• +۷ مورد دیگر

Nicole Grobert is Professor of Nanomaterials in the Department of Materials at the University of Oxford, holding this position since 2010. She also serves as an Ordinary Fellow at Corpus Christi College, Oxford (since 2012) and as Visiting Professor at the Bio-Nano Electronics Research Centre, Toyo University, Japan (since 2007). Her research focuses on the design and manufacturing of specialty nanomaterials, with significant breakthroughs in controlled production techniques that have led to multiple proof-of-concept projects. Her research spans several key areas: Hierarchical nanostructures and their assembly Nanoparticle synthesis and characterization Carbon nanotubes and 2D materials development Composite materials with enhanced properties Thermal management applications of nanomaterials Energy applications of advanced materials Professor Grobert's publication record demonstrates strong translational research, with recent work emphasizing industrial applications through her Royal Society Industry Fellowship at Williams Advanced Engineering. Her articles show consistent focus on scalable manufacturing techniques, thermal management solutions, and energy applications, reflecting her successful bridging of fundamental science and practical implementation. Her scientific achievements have been recognized with numerous prestigious awards: Chief Scientific Advisor to the European Commission (2018) Fellow of the Royal Society of Chemistry and Institute of Materials, Minerals and Mining (2018) Young Scientist at the World Economic Forum (2013) ERC Starting Grant and multiple Proof of Concept Grants International Carbon Pergamon Prize for her doctoral thesis Professor Grobert maintains significant involvement in science policy, serving on high-level advisory committees including the World Economic Forum Future Technology Pioneers Programme Committee, No 10 Round Table on Advanced Materials, Ministerial Nanotechnology Committee, and various Royal Society panels. She has spoken twice at the House of Lords Science and Technology Select Committee and served as Vice-President of the British Carbon Group.

Horacio Dante Espinosaمشاهده پروفایل

استاد

• Bioinspired Materials
• Single-Cell Studies
• Multiscale Experimentation and Modeling
• +۴ مورد دیگر

Horacio Dante Espinosa is the James N. and Nancy J. Farley Professor in Manufacturing and Entrepreneurship at Northwestern University's McCormick School of Engineering, holding the rank of Professor in Mechanical Engineering. He also directs the Theoretical and Applied Mechanics Program and the Micro and Nano Mechanics Lab. His research spans bioinspired materials, single-cell analysis, and multiscale experimentation, with a focus on nanoelectronics and energy harvesting. Espinosa earned his Ph.D. in Applied Mechanics from Brown University and has held academic roles at Purdue University and Harvard University. He leads interdisciplinary teams exploring metamaterials, cell engineering, and advanced fabrication techniques. His honors include the Prager Medal (2019) and multiple fellowships. Espinosa's lab combines experimental and computational methods, with capabilities in nanomechanical testing and biological assay development. Education: Ph.D. in Applied Mechanics (Brown University, 1992), M.Sc. in Structural Engineering (Polytechnic of Milan, 1987), Civil Engineering (Northeastern National University, Argentina, 1981) Key Roles: Director of iCET (2015–2018), Faculty Director of NUFAB (2013–2015), Visiting Professorships at Stanford and Harvard Labs: Micro and Nano Mechanics Lab focuses on biomaterials, metamaterials, and single-cell manipulation with advanced microscopy and electroporation systems Grants/Awards: NSF-CAREER Award, ONR Young Investigator Award, and leadership roles in professional societies His research bridges mechanics, materials science, and biology, with applications in healthcare technologies and advanced materials. Current projects include phononic crystal studies, Kirigami engineering, and high-throughput electroporation platforms for cell analysis.

Roman Franz Froschauerمشاهده پروفایل

استاد

• Software Engineering for Intelligent Automation Systems
• Human-Robot Interaction
• Control Systems
• +۷ مورد دیگر

Roman Franz Froschauer is a Professor of Production Informatics at the Upper Austria University of Applied Sciences, Research Center Wels. Since 2018, he has served as Director of Studies for the Master's program in Robotic Systems Engineering and leads the Smart Automation & Robotics research group. His career spans academic and industrial roles, including senior software development and project management at AlpinaTec Technical Products GmbH (2010-2016). Education: Ph.D. in Computer Science (2010) from Johannes Kepler University Linz; Master's in Industrial Informatics (2005) from Upper Austria University of Applied Sciences. Research Areas: Software engineering for intelligent automation systems, human-robot interaction (HRI), control systems, and applications of IEC 61499 standards. His work focuses on proactive collaboration, trajectory planning, and user-centered design for assistive robots in office and industrial settings. Scientific Activities: Active in peer-review, conference organization, and technology development. Projects include VRoboCoop (human-robot trust), MARIE (office robotics), and Autility (automated utility vehicles). Key Contributions: Frameworks for modular manufacturing (PlugBot), skill-based engineering, and intralogistics automation (ATLAS).

Elmar Rueckertمشاهده پروفایل

استاد

• Cyber-Physical Systems
• Robotics
• Machine Learning
• +۹ مورد دیگر

Prof. Elmar Rueckert is the Chair of the Cyber-Physical-Systems Institute at Montanuniversität Leoben in Austria since March 2021. He holds a PhD in Computer Science from TU Graz (2014) and previously served as a Senior Researcher at TU Darmstadt (2014–2018) and Assistant Professor at the University of Lübeck (2018–2021). His research focuses on Cyber-Physical Systems, Robotics, Machine Learning, and Human Motor Control, with applications in industrial automation, healthcare robotics, and environmental sustainability. Key research interests include stochastic machine/deep learning, reinforcement learning, brain-computer interfaces, and tactile learning. His work bridges robotics, neuroscience, and AI, emphasizing practical applications such as autonomous navigation, exoskeleton control, and sensor-based recycling technologies. Rueckert has led projects like the AI Robot Lab funded by Robert Bosch Stiftung and the KIRAMET recycling initiative. Recent publications highlight advancements in neural networks for industrial condition monitoring, multimodal robotic learning, and environmental infrastructure modeling. Awards include the German Young Researcher Award (2019) and the Advanced Robotics Best Paper Award (2018). Rueckert advises a team of researchers, including notable students like Daniel Tanneberg (PhD graduate, 2019) and Linus Nwankwo (best student paper winner, 2023). His lab develops open-source tools like ROMR and collaborates on datasets like EnvoDat for robotic spatial reasoning.

• Medical Imaging
• MRI Reconstruction
• Real-Time MRI
• +۴ مورد دیگر

Martin Uecker is a Professor at the Institute of Biomedical Imaging at TU Graz. His research focuses on advanced MRI reconstruction techniques, real-time imaging, and open-source software tools like the Berkeley Advanced Reconstruction Toolbox (BART). He specializes in developing methods for fast and accurate medical imaging, including applications in cardiac MRI, fetal brain imaging, and disease monitoring. His work emphasizes reproducibility, quantitative imaging, and clinical translation. Key research areas include generative models for MRI reconstruction, model-based inversion of the Bloch equations, and interactive real-time MRI systems. His team collaborates on projects involving hardware-software integration, such as portable MRI scanners and MRI-guided interventions. Notable contributions include advancements in multi-echo radial FLASH techniques, motion-resolved T1 mapping, and Bayesian uncertainty estimation in imaging. Uecker’s publications highlight innovations in accelerating MRI acquisition and reconstruction, with applications in pulmonary function assessment, neonatal imaging, and cardiovascular diagnostics. His work bridges theoretical physics, computational methods, and clinical practice, fostering open-source frameworks to democratize access to cutting-edge imaging tools.

• Musical Acoustics
• Physical Modeling of Musical Instruments
• Wind Instrument Aerodynamics
• +۴ مورد دیگر

Professor Vasileios Chatziioannou serves as Faculty Member and PhD Supervisor within the Department of Music Acoustics at the University of Music and Performing Arts Vienna. His academic leadership spans the Faculty of Music, where he guides doctoral candidates through the Structured Doctoral Program Performing Matters. His institutional role encompasses supervision across Ethnomusicology, Music in Society, and Musicology programs. His research focuses on the physics of musical instruments, with particular expertise in wind instrument aerodynamics and string-bow interaction mechanics. He employs advanced experimental techniques including particle image velocimetry (PIV) and high-speed motion capture alongside computational modeling. His work bridges theoretical acoustics with practical instrument design considerations, examining phenomena from tone hole geometry effects to friction dynamics in bowed strings. Analysis of his recent publications reveals a strong emphasis on energy-stable numerical methods for real-time simulation of musical instrument physics. His work consistently addresses the challenge of balancing computational efficiency with physical accuracy, particularly in modeling nonlinear phenomena like stick-slip friction and collision dynamics. The research trajectory shows increasing integration of experimental validation with computational models. While no specific scientific awards are documented in the provided materials, his sustained publication record in high-impact acoustics journals demonstrates significant scholarly contribution. His work regularly appears in specialized forums like the Journal of the Acoustical Society of America, particularly within special issues on musical instrument modeling. Professor Chatziioannou's supervision activities focus on computational and experimental approaches to musical instrument physics. His research program involves interdisciplinary collaboration between acousticians, instrument makers, and performers. Current projects include robotic replication of bowing techniques and 3D-printed instrument component optimization, reflecting his commitment to translating theoretical findings into practical applications for musicians and instrument designers.

• Numerics of Partial Differential Equations (PDEs)
• Finite Element Methods (FEM)
• Boundary Element Methods (BEM)
• +۳ مورد دیگر

Markus Faustmann is a Senior Scientist at the Institute of Analysis and Scientific Computing (E 101) at TU Wien. He holds a Dipl.-Ing. Dr.techn. degree in Technical Mathematics. His research focuses on numerical methods for partial differential equations (PDEs), finite element methods (FEM), boundary element methods (BEM), hierarchical matrices, elliptic regularity, and fractional differential operators. Faustmann has been recognized with the TU Best Teacher Award (2022) and TU Best Paper Award (2022) from TU Wien's Faculty of Mathematics and Geoinformation. Education: PhD in Mathematics from TU Wien (2015), supervised by J.M. Melenk; Diploma in Technical Mathematics (2011) and Bachelor of Science in Mathematics (2009), both from TU Wien. Research Interests: Development and analysis of numerical methods for non-local operators, including fractional Laplacian problems, hp-FEM, matrix compression techniques, and error estimation. His work emphasizes efficient discretization strategies and theoretical foundations for complex PDE systems. Teaching: Current courses include Numerical Methods for PDEs (Summer 2025), Non-local Operators (Winter 2024/25), and Scientific Programming for Interdisciplinary Mathematics . He has also supervised numerous bachelor's and master's theses in numerical analysis and computational mathematics. Awards: His research contributions have been highlighted through prestigious awards, reflecting his expertise in both teaching and applied numerical analysis.

Veljko Milutinovicمشاهده پروفایل

استاد

• Microprocessor architecture
• VLSI design
• Data mining
• +۹ مورد دیگر

Veljko Milutinovic is a Full Professor at the School of Electrical Engineering, University of Belgrade since 1990. He has also held academic positions at Purdue University (1982-1989) and consulted globally for institutions like IEEE and ACM. Current role: Computer Area Director, teaching VLSI, Data Mining, and E-Business Previous roles: Tenure Track Assistant Professor at Purdue University International collaborations: EU FP7 projects, Raiffeisen Bank, Wall Street Journal His research spans microprocessor architecture , VLSI design , data mining , and semantic web , focusing on energy-efficient architectures and e-business infrastructure. He pioneered DARPA's 200MHz GaAs RISC microprocessor and 4096-node systolic arrays. Recent work trends include: Technical and semantic interoperability in e-government Customer satisfaction accelerators in e-commerce 3D semantic web visualization Data mining for inverse engineering Academic-industry co-design methods Global university collaboration frameworks Scientific awards include: IEEE Life Fellow (2003) Foreign Member, Montenegrin Academy (2018) IPSI Awards (2007-2010) for eGov projects Best Method papers ranked #1 in Google search Supervised PhD students at Purdue, Belgrade, and Valencia Universities, with significant contributions to cache coherence, GaAs processors, and mobile network security. His publications (over 100 SCI papers) have been cited over 4000 times on Google Scholar.

• Optimization and Optimal Control of PDEs
• Nonsmooth Optimization in Function Spaces
• Inverse Problems and Mathematical Imaging
• +۶ مورد دیگر

Karl Kunisch is University Professor at the Department of Mathematics and Scientific Computing, University of Graz , and simultaneously Scientific Director of the Radon Institute (RICAM) of the Austrian Academy of Sciences in Linz. A SIAM Fellow and recipient of the 2021 W.T. and Idalia Reid Prize, he leads the ERC Advanced Grant OCLOC and heads the research group “Optimization and Optimal Control”. Education: Dipl.-Ing. (1975), Dr. techn. (1978) and Habilitation (1980), Graz University of Technology Research Interests: His work centres on optimization and optimal control of partial differential equations , nonsmooth optimisation in function spaces , inverse problems and mathematical imaging , together with advanced numerical analysis . Current emphases are life-science applications , closed-loop control and machine-learning based feedback design. Publications Profile: With over 400 papers and two monographs, his recent output is dominated by high-impact studies on infinite-horizon optimal control , feedback stabilisation of semilinear parabolic and Navier–Stokes systems, risk-averse and data-driven control , and sparse control strategies . A clear trend is the fusion of rigorous PDE analysis with cutting-edge machine-learning techniques. Scientific Awards & Distinctions: W.T. and Idalia Reid Prize (2021) SIAM Fellow (2017) ERC Advanced Grant Horizon 2020 (2015) Alwin Walther Medaille (2008) ICM Invited Lecture, Hyderabad (2010) SIAM Outstanding Paper Prize (2006) Christian Doppler Laboratory Fellowship (1992) Fellowship of the Japanese Society for the Promotion of Science (1990) Max Kade Scholarship (1982/83) Fulbright Travel Grants (1979/80, 1985) Theodor-Körner-Fonds Research Award (1979) Pro Scienta Scholarship (1974–1977) Grants & Leadership: Principal Investigator, ERC Advanced Grant “ OCLOC – From Open to Closed Loop Control ” Scientific Director, Radon Institute (RICAM), Austrian Academy of Sciences Head of Research Group “Optimization and Optimal Control”, RICAM Co-Speaker, International Research Training Group IGDK Former member/consultant: MATHEON Scientific Advisory Board, Weierstrass Institute Scientific Advisory Board, Christian Doppler Forschungsgesellschaft Senate, DFG and INRIA evaluation boards Laboratory & Team: Prof. Kunisch currently leads the “Optimization and Optimal Control” group at RICAM, comprising post-docs, doctoral researchers and international visitors, focusing on interdisciplinary projects at the interface of PDE control, numerical optimisation and life sciences.

Sebastian Knauerمشاهده پروفایل

پژوهشگر

• Magnonics
• Quantum Physics
• Materials Science
• +۴ مورد دیگر

Sebastian Knauer is a researcher at the Faculty of Physics, University of Vienna, specializing in Nanomagnetism and Magnonics. His work bridges quantum technologies and materials science, focusing on magnonic devices, spin-wave spectroscopy, and magnetic heterostructures. Education: B.Sc., M.Sc., Ph.D. His research explores quantum magnonics, low-temperature magnetism, and inverse-design methodologies. Recent publications emphasize Yttrium-Iron Garnet (YIG) films, paramagnon propagation, and quantum Hamiltonian learning. Key trends in his publications include applications of magnonic computing, hybrid quantum systems, and nanostructured materials. Collaborations span projects like On-chip quantum MagNonIcs and Nanometer-thick YIG layers , with international partnerships in quantum technologies. Scientific awards include: Marie Skłodowska-Curie Individual Fellowship (2021) NFFA-Europe Research Infrastructure grant (2022) Top-10 MSCA Fellowship (2021) He contributes to experimental quantum model learning and advancing spin-wave-based computing architectures.