Find Academic Supervisors

• Biomechanics
• Soft Tissue Mechanics
• Cardiovascular Biomechanics
• +5 more

Gerhard Holzapfel is a Professor at TU Graz's Department of Biomechanics. His research focuses on biomechanics of soft biological tissues, cardiovascular systems, and advanced material modeling. He leads the Institute of Biomechanics, conducting experimental and computational studies on tissue mechanics, vascular diseases, and medical device design. Education : Details not explicitly provided in source text. Research Interests : Dr. Holzapfel's work spans constitutive modeling of soft tissues, computational fluid dynamics (CFD) in vascular systems, mechanobiology of cells and tissues, and biomaterials. He emphasizes translating biomechanical insights into clinical applications such as endovascular devices and surgical simulations. His studies often integrate microstructural analysis with macroscopic mechanical behavior to understand pathologies like atherosclerosis and aortic dissection. Publications : Recent work highlights advancements in fiber dispersion models for skin mechanics, Bayesian frameworks for material calibration, and CFD-driven evaluations of TEVAR (thoracic endovascular aortic repair). His articles emphasize predictive modeling of tissue behavior under various loading conditions, with applications in cardiology and regenerative medicine. Awards : No specific honors or fellowships mentioned in the provided text. Advising & Grants : No student/advisor relationships or grant details explicitly listed. The Institute of Biomechanics likely supports collaborative projects in biomechanical engineering and medical research. Labs/Teams : Director of TU Graz's Institute of Biomechanics, leading interdisciplinary teams in biomechanics, material science, and computational modeling. Collaborates with clinical partners on vascular biomechanics and surgical device development.

• Medical Imaging
• MRI Reconstruction
• Real-Time MRI
• +4 more

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.

Aad van der VaartView profile

Professor

• High-dimensional statistics
• Nonparametric Bayesian statistics
• Statistical genetics
• +5 more

Aad van der Vaart is a distinguished Professor of Statistics at Delft University of Technology (since 2021). Previously, he held Full Professorships at Leiden University (2012–2021) and Vrije Universiteit Amsterdam (1996–2012). His research focuses on foundational statistical theory and applications, including high-dimensional statistics, Bayesian methods, inverse problems, and genomics. He has made seminal contributions to nonparametric Bayesian inference, empirical processes, and semiparametric theory. Van der Vaart has authored influential textbooks such as Asymptotic Statistics (1998) and Fundamentals of Nonparametric Bayesian Inference (2017, with S. Ghosal). His work bridges theoretical rigor and practical applications, with over 34,830 citations (Google Scholar, 2023) and an H-index of 60. Key honors include the Spinoza Prize (2015, Netherlands’ highest science award), DeGroot Prize (2020), and membership in the Royal Netherlands Academy of Sciences. His academic journey includes roles such as Miller Fellow at UC Berkeley (2000), visiting positions at leading universities, and leadership in statistical societies. His research group actively explores modern challenges in statistical theory and methodology, including causal inference, adaptive estimation, and large-scale data analysis. Notable grants include an ERC Advanced Grant (2012) for Bayesian inverse problems. Collaborations span academia and industry, emphasizing interdisciplinary impact. While specific lab affiliations are not explicitly stated, his work is rooted in foundational mathematical statistics with broad applicability.

Andrea M. TonelloView profile

Professor

• Power Line Communications
• Wireless Communications
• Embedded Communication Systems
• +12 more

Andrea M. Tonello is a Full Professor at the Institute of Networked and Embedded Systems, University of Klagenfurt, Austria, where he chairs the Embedded Communication Systems Lab. He previously held positions at the University of Udine, Italy, where he was an Associate Professor and founded the Wireless and Power Line Communication Lab (WiPLi Lab). His research spans power line communications, wireless systems, embedded communications, smart grids, and machine learning applications in signal processing. Doctor of Engineering, University of Padova (1996) Doctor of Research, Telecommunications, University of Padova (2003) His research interests focus on next-generation communication systems, including power line and wireless networks, signal processing, machine learning for communications, UAV systems, and smart grid technologies. He has made significant contributions to PLC channel modeling, full-duplex communications, and information-theoretic learning for communication systems. His work integrates theoretical innovation with practical implementation in real-world networks. The most recent publications highlight a strong trend toward integrating machine learning and information theory into communication systems, particularly in power line and wireless networks. Themes include f-divergence based classification, mutual information estimation, neural decoding (MIND), noise-robust receivers, and topology-aware machine learning for PLC quality prediction. There is also a notable focus on UAV control, full-duplex PLC, and digital pre-distortion techniques for high-speed converters. IET 2016 Premium Award Best Paper Award, ISPLC 2016 Best Student Paper Award, ISPLC 2016 Aerospace Best Paper Award, 2018 Best Paper Award, ISPLC 2021 Best PhD Dissertation Award, 2019 IEEE ComSoc Distinguished Lecturer (2018) Two Awards from IEEE ComSoc TC-PLC (2019) University of Klagenfurt Technology Scholarships (2019) Dr. Tonello has supervised numerous PhD and Master’s students, including notable advisees such as Nunzio A. Letizia, Davide Righini, and Babak Salamat. He has led over 10 institutional and multiple industrial research projects with a total funding exceeding 20 million euros. He played a key role in promoting international academic collaboration, including Erasmus agreements, joint PhD programs with INSA Rennes and Ecole Polytechnique de Grenoble, and a joint master’s program with the University of Klagenfurt. He founded and led the WiPLi Lab at the University of Udine, which received around 3 million euros in funding and involved over 60 researchers and students. He also founded WiTiKee s.r.l., a spin-off company specializing in PLC for smart grids. Currently, he chairs the Embedded Communication Systems Lab at the University of Klagenfurt, focusing on next-generation networked and embedded communication technologies.

Sebastian KnauerView profile

Researcher

• Magnonics
• Quantum Physics
• Materials Science
• +4 more

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.

• Computational Electromagnetics
• Inverse Problems in Electromagnetism
• Bioimpedance and Medical Diagnostics
• +3 more

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.

• Machine Learning
• Nanotechnology Sensors
• Bayesian Inversion
• +3 more

Amirreza Khodadadian is a researcher affiliated with TU Wien's E194-06 Machine Learning Research Group. His work focuses on computational methods for nanosensors, numerical solutions to stochastic partial differential equations, and Bayesian inversion techniques. He collaborates extensively with Prof. Clemens Heitzinger and other researchers in fields like nanotechnology and applied mathematics. Key research areas include meshless numerical methods (e.g., interpolating element free Galerkin), optimal multi-level Monte Carlo algorithms, and sensor modeling using machine learning and Bayesian approaches. His publications address challenges in nanoelectronics, groundwater contamination modeling, and phase-field fracture simulations. Khodadadian's contributions span theoretical developments and practical applications, including work on carbon nanotube field-effect transistors and hyper-parameter optimization for autoencoder-based systems. He actively participates in projects funded by the Austrian Science Fund (FWF) and collaborates across disciplines to advance computational sensor technology.

• Computer Vision
• Optimization Methods
• Medical Imaging
• +5 more

Thomas Pock is a Professor at the Institute of Visual Computing at TU Graz. His research focuses on computer vision, optimization methods, and mathematical models in computer vision, with significant contributions to medical imaging and inverse problems. He leads projects integrating deep learning with traditional variational methods for applications in MRI reconstruction, microscopy, and cardiac signal analysis. His work emphasizes efficient sampling techniques, uncertainty quantification, and algorithmic optimization in computational imaging. Education and academic background are not explicitly detailed in the provided texts, but his extensive publication record indicates expertise in interdisciplinary areas such as biomedical engineering, signal processing, and machine learning. Key research themes include variational networks, total variation methods, and generative adversarial networks (GANs) for image reconstruction and segmentation. His research spans collaborations in both academic and clinical settings, addressing challenges in 3D reconstruction, particle flow estimation, and cardiac electrophysiology modeling. Notable projects include Total Deep Variation and inverse Eikonal methods for medical data analysis. He actively contributes to open-source tools and frameworks for variational optimization and deep learning integration.

Clemens HeitzingerView profile

Associate Professor

• Machine Learning
• Reinforcement Learning
• Nanotechnology
• +7 more

Prof. Clemens Heitzinger is an Associate Professor at TU Wien, affiliated with the Forschungsbereich Machine Learning (E194-06) . His research focuses on interdisciplinary applications of machine learning, computational methods, and nanotechnology. Key areas include reinforcement learning for healthcare optimization, stochastic modeling of PDE systems, and Bayesian inversion in sensor design. He leads the project PDE models for nanotechnology under the Scientific Computing and Modelling department (E101-03). His work spans medical imaging (e.g., electrical impedance tomography), nanopore sequencing, and nanoscale sensor development. Notable contributions include algorithms for corticosteroid therapy optimization in sepsis and superhuman performance in sepsis prediction via distributional reinforcement learning. He has advised over 13 PhD and Master's students, including Tobias Kietreiber, Sebastian Bittner, and Leila Taghizadeh. His research methodologies integrate machine learning with numerical analysis, emphasizing uncertainty quantification and PDE modeling. Recent trends in his publications highlight applications in personalized healthcare, industrial anomaly detection, and computational fluid dynamics in nanoscale systems. While no scientific awards are explicitly mentioned, his work has been published in high-impact journals and conference proceedings, reflecting his contributions to computational science and engineering.

Leila TaghizadehView profile

Research Fellow

• Uncertainty Quantification
• Numerical Methods
• Bayesian Inverse Problems
• +3 more

Leila Taghizadeh is a Research Fellow and Group Leader for Uncertainty Quantification at the Institute for Analysis and Scientific Computing, Vienna University of Technology (TU Wien). She holds a Master of Science degree and is currently pursuing a Doctorate in Engineering (Dr. techn.). Her primary role involves leading research projects in computational uncertainty quantification and related areas. Her research focuses on Numerical Methods for Uncertainty Quantification , PDE-based Bayesian Inverse Problems , and Optimal Experimental Design . She leads the FWF Elise Richter Grant (V1000) project titled "Computational Uncertainty Quantification in Nanotechnology" as the Principal Investigator (PI). Leila actively contributes to academic organization and outreach, including co-chairing the 5th Austrian Day of Women in Mathematics (ADWiM2025) at TU Wien and co-organizing the Minisymposium on Uncertainty Quantification, Machine Learning, and Inverse Problems at the 2024 SIAM Conference on Uncertainty Quantification (SIAM UQ24). She teaches advanced courses such as the 2025 Summer Semester seminar on Uncertainty Quantification and Machine Learning . Her work integrates computational methodologies with applied mathematics, emphasizing interdisciplinary applications in semiconductor device modeling and nanotechnology. She is currently recruiting student assistants for research and administrative tasks starting September 2025.

• Virtual Screening
• Cytochrome P450
• Small Molecule
• +4 more

Johannes Kirchmair is a Professor in the Department of Pharmaceutical Sciences at the Faculty of Life Sciences. His research focuses on computational methods in drug discovery, particularly virtual screening, machine learning applications, and understanding drug metabolism. He is actively involved in projects such as the Anti-Infectives Drug Discovery initiative and the AIDD: Advanced Machine Learning for Innovative Drug Discovery program. His work addresses challenges in toxicity prediction, enzyme inhibition, and assay interference detection. Key areas include cytochrome P450 metabolism, small molecule drug design, and natural product analysis. Research interests span virtual screening methodologies, metabolite identification, and computational toxicology. He has contributed to the development of tools like E-GuARD for detecting assay interference and aweSOM for predicting metabolic sites. His collaborations emphasize cross-disciplinary approaches to advance drug discovery and chemical safety. He has delivered talks on computational methods for early drug discovery and assay interference prediction, reflecting his commitment to both academic research and practical applications. Current projects aim to leverage machine learning to tackle antimicrobial resistance and enhance drug development efficiency.

Thomas GrafView profile

Research Professor

• Macrophage
• Cancer biology
• Reprogramming
• +16 more

Thomas Graf is a Research Professor at the Centre for Genomic Regulation (CRG) in Barcelona, Spain, and an ICREA Research Professor since 2006. His academic career spans multiple prestigious institutions including Albert Einstein College of Medicine, European Molecular Biology Laboratory, and Max Planck Institute. Research focus: Cancer biology, Reprogramming, Genetic engineering, Molecular biology Key contributions: Hematopoietic stem cells, Transdifferentiation, Computational biology His recent publications demonstrate expertise in inverse imaging problems, diffusion models, and biomedical applications spanning cardiology and protein structure analysis. Scientific accolades include the Paul Ehrlich Prize and membership in Academia Europaea. 1989: Paul Ehrlich and Ludwig Darmstaedter Prize 1983: Wilhelm Warner Foundation Prize Other: Leukemia Research Award, German Society for Microbiology and Hygiene Prize, Academia Europaea membership

• Nonlinear Partial Differential Equations
• Cross-Diffusion Systems
• Mathematical Modeling in Semiconductor Devices
• +5 more

Ansgar Jüngel is a Full Professor for Analysis of Nonlinear Partial Differential Equations at TU Wien, Austria. He leads the Institute of Analysis and Scientific Computing within the Faculty of Mathematics and Geoinformation. His research focuses on mathematical modeling, analysis, and numerical simulation of complex systems, particularly in semiconductor physics, quantum hydrodynamics, and biological transport phenomena. Jüngel holds a PhD from TU Berlin (1994) and habilitation (1997), with prior academic roles at University of Konstanz and University of Mainz. His work spans nonlinear PDEs, cross-diffusion systems, entropy methods, and applications in semiconductor modeling (e.g., quantum drift-diffusion, Maxwell-Stefan systems). Notable contributions include rigorous derivation of energy-transport models, analysis of quantum fluid models, and development of entropy-dissipative numerical schemes. Awards include the ERC Advanced Grant (2021) and Tsungming-Tu Award (2011). Teaching emphasizes advanced mathematical topics: cross-diffusion systems, partial differential equations, and computational finance. Active in PhD supervision and collaborative projects, he co-authored books on transport equations, entropy methods, and semiconductor modeling. His team includes postdocs and PhD students working on topics like multi-species populations, tumor growth modeling, and stochastic cross-diffusion systems.