جست‌وجوی استاد و پژوهشگر

Philipp Grohsمشاهده پروفایل

استاد

• Mathematical Data Science
• Deep Learning Algorithms
• High-Dimensional Partial Differential Equations
• +۷ مورد دیگر

Prof. Dr. Philipp Grohs is a full professor of Mathematical Data Science at the University of Vienna and head of the Mathematical Data Science group at RICAM (Austrian Academy of Sciences). He holds a MSc from TU Vienna (2006) and a PhD from the same institution (2007). After postdoc positions at TU Graz, KAUST, and ETH Zurich, he became an assistant professor at ETH Zurich in 2011 before moving to the University of Vienna in 2016. His research focuses on designing efficient algorithms for signal processing, computational sciences, and finance, with recent contributions to understanding deep learning algorithms and solving high-dimensional PDEs using machine learning. He has received the ETH Latsis Prize (2014) and was selected for an Alexander von Humboldt Professorship (2019). Research Interests: Mathematical foundations of deep learning High-dimensional PDEs and their numerical solutions Signal and image processing (phase retrieval, Gabor systems) Approximation theory and function spaces Computational finance and mathematical modeling Recent Research Trends: His work explores theoretical guarantees for neural network performance, particularly in overcoming dimensionality challenges. Notable contributions include phase retrieval algorithms, analysis of DNN expressivity, and applications of deep learning in quantum chemistry and epidemiology modeling. Awards: ETH Latsis Prize (2014) Alexander von Humboldt Professorship (2019) Advising & Projects: Leads research initiatives such as the 'Data Science' hub at Vienna, and has coordinated projects on hybrid computational sciences and explainable AI models. Active in supervising interdisciplinary research teams across mathematics and computer science. Labs/Teams: Directs the Mathematical Data Science group at RICAM and oversees computational research collaborations with institutions like KAUST and ETH Zurich. Engages in applied projects like group testing strategies for SARS-CoV-2 and neural network-based electronic structure calculations.

Mourad Siniمشاهده پروفایل

عضو هیئت علمی

• Inverse Problems
• Mathematical Imaging
• Wave Propagation
• +۳ مورد دیگر

Mourad Sini is a Senior Fellow at the Radon Institute for Computational and Applied Mathematics (RICAM) within the Austrian Academy of Sciences. His primary affiliation is with the Department of Inverse Problems and Mathematical Imaging. He holds a PhD from the University of Provence (2002) and a Habilitation from the Johannes Kepler University Linz (2009). His research focuses on mathematical analysis of partial differential equations, inverse problems, and applications in wave propagation, material sciences, and medical imaging. Key areas include acoustic and electromagnetic scattering, metamaterials, and nanoparticle-based imaging techniques. Current projects include the Austrian Science Fund (FWF)-funded studies on 'Electromagnetism with Extreme Materials' (2020–2024) and 'Mathematical Analysis of Imaging Modalities using Nanoparticles' (2018–2022). Selected grants and collaborations involve King Abdullah University of Science and Technology (KAUST) and the Nanjing Center for Applied Mathematics. His work bridges theoretical analysis with practical applications in engineering and biomedical sciences.

• Electronic Circuit Design
• RF Power Amplifiers
• Digital Signal Processing
• +۵ مورد دیگر

Hans-Georg Brachtendorf is a Professor at the Research Center Hagenberg Embedded Systems within the School of Informatics, Communications and Media at the University of Applied Sciences Upper Austria - Hagenberg Campus. His work focuses on advanced circuit simulation techniques, RF engineering, and digital signal processing with significant contributions to harmonic balance methods and nonlinear circuit analysis. His research interests span electronic circuit design, RF power amplifiers, digital signal processing, and nonlinear dynamics. Specializing in harmonic balance methods and oscillator circuits, he has developed innovative techniques for steady-state circuit simulation, nonlinear adaptive filtering, and multiplier-less digital filter design. His work bridges theoretical mathematics with practical electronic engineering applications, particularly in THz-wave detection and wireless communication systems. Analysis of his recent publications (2022-2025) reveals a strong focus on nonlinear dynamics identification using symbolic regression, optimization of digital filters through multiplier-less architectures, and governing equation reconstruction from measurement data. His work demonstrates consistent advancement in computational efficiency for circuit simulation while addressing modern challenges in RF and THz technologies. While no specific scientific awards are listed in the available data, his research has generated significant academic impact with 43 publications since 1994 and an h-index reflecting substantial citation influence. Professor Brachtendorf has supervised at least two academic works according to institutional records, though specific student names are not provided. His research activities include collaborations evidenced by joint publications with researchers such as Steiger, Dalpiaz, and Kronberger across multiple institutions. The Research Center Hagenberg Embedded Systems serves as his primary laboratory environment, focusing on digital transformation within ICT strength areas with particular emphasis on circuit simulation and signal processing applications.

Wilfried Kauselمشاهده پروفایل

استاد

• Musical Acoustics
• Physics of Brass Instruments
• Structural Vibrations
• +۳ مورد دیگر

Wilfried Kausel is a faculty member at the University of Music and Performing Arts Vienna, associated with the Department of Music. He serves as Dean of Scientific Studies (Studiendekanat für wissenschaftliche Studien) and leads the Graduate Center. His work bridges musical acoustics and computational physics, with a focus on brass instruments and structural vibrations. Academic Rank: Professor Key Institutions: University of Music and Performing Arts Vienna Research Labs: Acoustic Laboratory, Performance Science Lab Research interests center on the physics of musical instruments, particularly: Computational modeling of brass instruments Wall vibrations and their acoustical influence Directivity patterns and power efficiency Time-resolved interferometry for vibration analysis Bore reconstruction and impedance measurements Stick-slip simulations for lip oscillators The 15 most recent publications (2002-2021) reveal expertise in: Brass instrument mechanics (walls, bells, bores) Wave propagation and coupling Electronic speckle pattern interferometry Computational simulations for acoustics Material properties in instrument design Wind instrument intonation and sensory evaluation Labs and infrastructure include: Acoustic Laboratory with anechoic chamber Performance Science Lab Slow Motion Lab Computer Wing for simulations Motion Capture and Eye Tracking networks

• General Sociology
• Sociology of Work
• Environmental Sociology
• +۸ مورد دیگر

Josef Langer is a Professor at the University of Klagenfurt's Institute for Society, Knowledge and Politics. His research focuses on sociology, social theory, information society dynamics, and globalization. He contributes to understanding spatial development, EU integration, and cross-cultural cooperation. Current research includes media literacy promotion in youth and interdisciplinary migration methodologies. Key research areas span sociological theory, modernization, and the interplay between technology and society. His work bridges theoretical frameworks with empirical analysis, addressing contemporary issues like digital media impact and sustainable business models. Recent publications explore online political microtargeting avoidance, swarmalator systems, and entrepreneurial ecosystems for sustainability. He collaborates on EU integration projects and educational initiatives. No awards are listed here, though his extensive publication record reflects scholarly impact.

• Literary history
• German studies
• Cultural studies
• +۸ مورد دیگر

Sabine Seelbach is a Professor at the Department of Linguistics and Literature, University of Klagenfurt. Her research focuses on medieval German literature, including discourse interferences in vernacular texts, knowledge literature, courtly narratives, and early German humanism. She also engages with cultural studies, hagiography, and narrative theory. Her work explores interdisciplinary connections between medieval literature and modern academic practices, such as editorial studies and the application of narrative theory. Recent publications address topics like swarm dynamics, political microtargeting avoidance, and university third mission initiatives, reflecting her broad intellectual curiosity across humanities and social sciences. Seelbach has contributed to over 35,000 publications, emphasizing methodological innovation in literary analysis. She actively participates in academic governance and peer review, contributing to conferences and editorial boards. Her research bridges historical textual analysis with contemporary societal challenges, maintaining a strong presence in both traditional and emerging scholarly discourses.

• Wireless Networks
• Telecommunications
• Stochastic Analysis
• +۳ مورد دیگر

Udo Schilcher is a researcher affiliated with the University of Klagenfurt’s Institute for Networked and Embedded Systems, part of the Faculty of Technical Sciences. His primary research focuses on wireless networks, telecommunications, and stochastic analysis, with particular emphasis on swarmalator systems, pulse-coupled oscillators, and network performance optimization. He contributes to the Information Technology Curricular Commission, shaping academic standards in his field. His work explores topics like LoRa network energy efficiency, interference dynamics in wireless systems, and self-organizing synchronization mechanisms. Schilcher’s publications span over a decade, addressing theoretical and applied challenges in IoT, network reliability, and adaptive systems. His research often bridges mathematical modeling with real-world applications in communication systems and smart infrastructure. Recent projects involve collaborative efforts on IoT applications, including LoRa network enhancements and cooperative relaying strategies. His interdisciplinary approach combines stochastic processes with engineering principles to address complex network challenges. Schilcher’s contributions to academic curricula and technical research highlight his role as both an educator and innovator in networked systems.

Michael Reiterمشاهده پروفایل

مدرس ارشد

• Machine Learning
• Pattern Recognition
• Computer Vision
• +۴ مورد دیگر

Michael Reiter is a Senior Lecturer at the Institute of Visual Computing & Human-Centered Technology, part of the Faculty of Informatics at TU Wien. His research focuses on Machine Learning, Pattern Recognition, and Computer Vision, particularly in medical applications such as flow cytometry data analysis for leukemia diagnosis and minimal residual disease (MRD) assessment. He leads the MyeFLOW project (2020–2025), which develops automated methods for immunophenotyping and MRD detection in pediatric leukemia. His work bridges machine learning and clinical diagnostics, emphasizing explainable AI in healthcare. Reiter collaborates with medical institutions to translate algorithms into clinical tools, such as the FlowMe decision support system. His research interests include transformer networks, UMAP visualization, and Gaussian mixture models for biomedical data. He has supervised diploma theses on topics like meta learning for blast cell classification and U-Net-based flow cytometry analysis. Reiter’s publications span over two decades, with recent emphasis on medical AI applications. His lab, the Computer Vision Lab, focuses on visual computing for healthcare, robotics, and cultural heritage digitization. Current projects address automated analysis of museum herbarium collections and empathic robotic systems for autism therapy.

Astola Jaakko Tapioمشاهده پروفایل

استاد

• Signal Processing
• Image Processing
• Nonlinear Signal Processing
• +۹ مورد دیگر

Jaakko Tapio Astola is a Professor of Signal Processing at Tampere University of Technology since 1993. He has held prominent roles including Academy Professor (2001–2006) and Associate Professor of Applied Mathematics (1988–1993). His academic journey includes positions at Lappeenranta and Eindhoven Universities of Technology. Astola is a member of the Finnish Academies of Science and Technology, and has received prestigious awards like the EURASIP Fellowship (2012), SPIE Fellowship (2007), and IEEE Fellowship (2000). His research spans signal processing, nonlinear filtering, bioinformatics, and algebraic coding theory. Notable contributions include foundational work on vector median filters and influential textbooks on nonlinear digital filtering. His interdisciplinary work bridges signal processing with systems biology and genetic network modeling. Education Highlights: 1984–1985: Research at Eindhoven University of Technology with Prof. J.H. van Lint's Discrete Mathematics Group 1976–1977: Studies at Kyoto University's Research Institute for Mathematical Sciences Research Interests: Astola's work emphasizes robust filtering techniques, spectral methods, and applications in biomedical signal analysis. His contributions to algebraic signal processing and bioinformatics include advancing Boolean network models for genetic systems and developing systematic bioinformatic analysis frameworks for large-scale genomic data. Awards: EURASIP Fellow (2012) Recipient of 2010 Nokia Award IEEE Fellow (2000) Grants & Advising: While specific grants are not detailed, his extensive academic roles and publications indicate sustained research funding. No student advisees are listed in available texts.

• Digital signal processing and simulation
• Dynamical systems
• Nonlinear vibrations
• +۹ مورد دیگر

Pol D. Spanos is the Lewis B. Ryon Professor of Mechanical and Civil Engineering and Professor of Materials Science and NanoEngineering at Rice University, USA. He is an elected member of the National Academy of Engineering (USA) and foreign member of a dozen prestigious academies worldwide. Education Diploma in Mechanical Engineering and Engineering Science, National Technical University of Athens, Greece M.S. in Civil Engineering, California Institute of Technology (Caltech), USA Ph.D. in Applied Mechanics (with minors in Applied Mathematics and Business Economics & Management), Caltech, USA Research Interests Professor Spanos’ research integrates digital signal processing , nonlinear and stochastic dynamics , and advanced computational mechanics . His group develops analytical, numerical, and Monte-Carlo methods to study: Nonlinear vibrations of structures and mechanical systems under random loads Seismic risk assessment and spectrum estimation Fatigue and fracture of advanced nanocomposite materials Signal processing of biomedical data (ECG, EEG, bone mechanics) Flow-induced vibrations of offshore platforms, risers and pipelines Directional drilling dynamics and certification of space-station payloads The mathematical frameworks involve deterministic and stochastic differential equations (integer or fractional order), finite-element formulations, wavelet/chirplet transforms, and high-performance computing. Scientific Awards & Honors 2024 Blaise-Pascal Medal in Engineering 2021 ASME Gold Medal (highest ASME award) 2020 T.K. Caughey ASME Medal for Lifetime Contributions to Nonlinear Dynamics 2017 International Scientific and Technological Cooperation Award of the People’s Republic of China Member or Foreign Member of 11 national academies and fellow of multiple professional societies ASCE Theodore von Kármán Medal (2003), Newmark Medal, Freudenthal Medal, Huber Prize, etc. Advising & Funding He has supervised over 75 M.S. theses and 60 Ph.D. dissertations , hosted numerous visiting scholars, and secured funding from NSF, NASA, DOE and a broad range of industrial partners. Professional Service & Outreach Editor-in-Chief, International Journal of Non-Linear Mechanics Editor, Journal of Probabilistic Engineering Mechanics Former Chair, ASCE Engineering Mechanics Division and ASME Applied Mechanics Division ASME Distinguished Lecturer (1997-2003) Registered Professional Engineer (Texas) and Licensed Engineer in Greece Active in diversity-enhancing initiatives and pro-bono engineering consulting

Mitra Sanjitمشاهده پروفایل

عضو هیئت علمی

• Digital Signal Processing
• Analog Signal Processing
• Computer-Aided Design
• +۳ مورد دیگر

Sanjit K. Mitra is a Distinguished Professor Emeritus at the University of California, Santa Barbara (UCSB), and a Life Fellow of the IEEE. He holds memberships in prestigious academies including the U.S. National Academy of Engineering, European Academy of Arts & Sciences, and Bavarian Academy of Sciences and Humanities. His career includes roles as Distinguished Professor and Professor at UCSB, UC Davis, and Cornell University, alongside contributions at Bell Labs. Key Positions: Distinguished Professor Emeritus (UCSB), Member of Technical Staff (Bell Labs), 1962–2006. Education: Not explicitly detailed in texts, but his academic lineage spans decades of mentorship. Research focuses on digital and analog signal processing, filter design, and image/video compression. He authored 14 books and over 700 publications, cited 31k+ times (h-index 73). His work has been translated into multiple languages and influenced global engineering curricula. Awards include the IEEE Education Medal (2006), Golden Jubilee Medal (1999), and recognition as a 'Pioneer in Signal Processing' (1998 & 2017). His 48 Ph.D. students include 9 IEEE Fellows and a U.S. NAE member.

Guodong Wangمشاهده پروفایل

پژوهشگر

• Machine Learning
• Industrial Data Processing
• Cyber-Physical Systems
• +۵ مورد دیگر

Dr. Guodong Wang is a researcher affiliated with the Forschungsbereich Cyber-Physical Systems at TU Wien. His work focuses on machine learning applications in industrial data processing, fault diagnosis, and health monitoring systems. He has collaborated extensively with Prof. Radu Grosu and other researchers on projects involving neural networks, Bayesian methods, and sensor data analysis. Education: PhD in Technical Sciences (Dr.techn.) MSc in Engineering His research interests bridge theoretical machine learning with practical industrial challenges, including: Development of generative neural network models for quality prediction in manufacturing Fault prognostics for semiconductor and marine machinery systems Integration of Bayesian networks and evidential reasoning for multi-sensor data fusion Key contributions include: Proposing the ZIZO algorithm for Echo-State Networks optimization Advancing wear condition prediction in milling tools Creating machine learning frameworks for cyber-physical systems diagnostics His work often addresses real-world applications in manufacturing, marine engineering, and medical diagnostics.

Richard Kowarمشاهده پروفایل

پژوهشگر

• Inverse Problems
• Wave Propagation
• Medical Imaging
• +۳ مورد دیگر

Priv.-Doz. Dr. Richard Kowar is a Postdoctoral Researcher at the Department of Mathematics, University of Innsbruck. His research focuses on inverse problems, wave propagation, and mathematical modeling in medical imaging and physics. He specializes in photoacoustic tomography, regularization methods for ill-posed equations, and the analysis of causal wave equations. His work bridges theoretical mathematics with practical applications in biomedical imaging and signal processing. Key research areas include the analysis of dissipative systems, fractional calculus in wave equations, and iterative algorithms for image reconstruction. His studies on causality in wave equations and diffusion models have advanced the understanding of nonlocal operators and their applications. Kowar has contributed to projects like VASCAGE, integrating mathematical techniques with real-world engineering and medical challenges. Publications highlight advancements in photoacoustic imaging with attenuating media, numerical inversion techniques, and theoretical frameworks for causal dynamics. Despite no listed scientific awards, his extensive bibliography demonstrates impactful contributions to applied mathematics and engineering.

• Computational Electromagnetics
• Inverse Problems in Electromagnetism
• Bioimpedance and Medical Diagnostics
• +۳ مورد دیگر

Alice Reinbacher-Köstinger is an Associate Professor at the Institute for Fundamentals and Theory of Electrical Engineering, Graz University of Technology, Faculty of Electrical Engineering. Her research focuses on computational electromagnetics, inverse problems, and bioimpedance analysis. Role: Research and Teaching Research Areas: Transformer modeling, NFC systems, aortic dissection detection, and surrogate-based optimization Email: alice.koestinger@tugraz.at Her work combines advanced modeling techniques with machine learning, particularly in magnetic material characterization and bioimpedance signal analysis. Recent publications emphasize physics-informed neural networks, harmonic balance methods, and Bayesian experimental design. Current projects include optimizing sensor placement for magnetic property measurements and developing simulation tools for medical diagnostics. Her research bridges electrical engineering with biomedical applications through multiphysics simulations and data-driven methods.

• Mathematics
• Applied Mathematics
• Harmonic Analysis
• +۳ مورد دیگر

Norbert Kaiblinger is an Associate Professor at the Institute of Mathematics, BOKU University, Vienna, Austria. His research focuses on applied mathematics, harmonic analysis, and numerical methods, with applications in fluid mechanics, statistics, and chemical engineering. He holds a habilitation in Mathematics from the University of Vienna, granting him teaching authorization in the field. His work bridges theoretical mathematics with practical computational problems, particularly in adsorption modeling and fluid dynamics. Key research interests include multicomponent batch adsorption models, dynamics of subaqueous systems, analysis of variance (ANOVA) methodologies, and special functions. Recent publications (2023–2025) highlight advancements in equilibrium composition calculations and statistical experimental design. His earlier work (2007–2019) explores Fourier analysis, operator theory, and algebraic structures like cyclotomic rings and circulant matrices. While no formal advising relationships or grants are listed, his collaborative research involves co-authors from diverse fields such as chemical engineering and fluid mechanics. Kaiblinger’s work is disseminated through journals like Adsorption, Journal of Fluid Mechanics, and Transactions of the American Mathematical Society.