جستجوی استاد راهنمای دانشگاهی

• Computer Science
• Software Engineering
• Formal Methods
• +۶ مورد دیگر

Thomas Henzinger is a Professor at the Institute of Science and Technology Austria (IST Austria) since 2009, serving as its President from 2009 to 2022. He holds honorary doctorates from Fourier University and Masaryk University. His research focuses on formal methods for improving software reliability in concurrent and embedded systems, with contributions to quantitative verification, quantum computing, and algorithmic fairness. He leads the Henzinger Group , which addresses challenges in software correctness, safety-critical systems, and AI ethics. Education: PhD from Stanford University (1991), followed by postdoctoral work at Université Joseph Fourier (1991). Research Interests: Concurrent and embedded systems, formal methods, quantum algorithms, fairness in AI, and runtime verification. His team develops mathematical foundations and tools to ensure software robustness in complex systems. Recent Trends in Publications: Focus on advancing formal verification techniques for neural networks, quantum computing, and ethical AI. Key contributions include fairness shields for decision systems and middleware for third-party monitoring (VAMOS). Awards: Royal Society Fellow (2024), US National Academy of Sciences Member (2020), Wittgenstein Award (2012), and numerous test-of-time awards. Advising & Grants: Advised over 20 PhD students. Secured ERC Advanced Grants (VAMOS, QUAREM) and FWF grants (Wittgenstein Award). Labs/Teams: Leads the Henzinger Group , collaborating on projects like SPyCoDe (semantic security) and VAMOS (third-party monitoring middleware).

Yun Fuمشاهده پروفایل

استاد

• Artificial Intelligence
• Computer Vision
• Pattern Recognition
• +۲ مورد دیگر

Yun Fu is a tenured Professor in the Department of Electrical and Computer Engineering at Northeastern University, with a joint appointment in the Khoury College of Computer Science. He has established himself as a leading researcher in Artificial Intelligence, with over 500 publications in top-tier venues including IEEE/ACM transactions and major AI conferences. His work spans both theoretical foundations and practical applications, with significant impact in computer vision and machine learning. Professor Fu earned his Ph.D. in Electrical and Computer Engineering from the University of Illinois at Urbana-Champaign. His academic career progressed from Assistant Professor at SUNY Buffalo to his current position as tenured Professor at Northeastern University, where he has held appointments since 2012. His educational background includes a Beckman Graduate Fellowship at UIUC (2007-2008). His research focuses on advancing Artificial Intelligence with particular emphasis on Computer Vision, Pattern Recognition, and Machine Learning. His seminal work includes the "Residual Dense Network for Image Super-Resolution" presented at CVPR 2018, which was ranked among the Top 10 Most Influential CVPR papers. His research interests span image processing, anomaly detection, multimodal learning, and trajectory prediction, with applications ranging from healthcare to consumer technology. Analysis of his recent publications reveals a strong trend toward developing efficient and robust AI systems that bridge computer vision with language understanding. His work increasingly focuses on multimodal learning, trajectory prediction for multi-agent systems, anomaly detection in complex environments, and model validation techniques for black-box systems, while maintaining practical applications in real-world scenarios. Professor Fu's extensive recognition includes: Fellow of IEEE (2018), OSA (2019), SPIE (2018), IAPR (2016), AAIA (2021), and AAAI (2025) Member of Academia Europaea (2022) and European Academy of Sciences and Arts (2023) Fellow of National Academy of Inventors (2023) Multiple Young Investigator Awards from NAE, ONR, ARO, IEEE, ACM, and INNS 12 Best Paper Awards from major conferences Industrial Research Awards from Google, Amazon, Samsung, JPMorgan, and others Professor Fu has successfully mentored numerous Ph.D. students who now hold prominent positions in academia and industry at institutions including Amazon, Microsoft, Meta, Adobe, and major universities. His entrepreneurial ventures include founding Giaran (acquired by Shiseido in 2017) and co-founding TVision Insights, demonstrating his commitment to translating research into real-world impact. He has secured significant research funding from both government agencies and industry partners. As the PI and Founding Director of the SmiLe Lab at Northeastern University, Professor Fu leads a dynamic research group focused on advancing the state-of-the-art in AI and Computer Vision. The lab fosters interdisciplinary collaboration across computer science, electrical engineering, and applied mathematics, with ongoing projects in efficient deep learning, multimodal understanding, and practical AI applications.

Zhang Yanمشاهده پروفایل

استاد

• Internet of Vehicles
• Smart grid
• Mobile edge computing
• +۴ مورد دیگر

Zhang Yan is a Full Professor at the Department of Informatics, University of Oslo, Norway. He previously served as Head of Department and Chief Scientist at Simula Research Laboratory (2014–2016). His research focuses on advanced communication technologies including Internet of Things (IoT), 5G/6G networks, mobile edge computing, and blockchain applications. He has held significant roles such as IEEE VTS Distinguished Lecturer (2016–2020) and Chair of IEEE TCGCC (2019–2021). His honors include IEEE Fellow (2020), election to Academia Europaea (2020), and recognition as a Web of Science Highly Cited Researcher (2018–2019). Research interests span interdisciplinary areas like network dynamics, socio-economic systems, and algorithmic design. His work bridges theoretical foundations with practical applications in smart grids, vehicular networks, and global trade systems. Recent publications emphasize network science methodologies applied to economic complexity and information diffusion. Professional contributions include editorial roles for top journals and leadership in EU-funded projects. His awards reflect impactful contributions to both technical innovation and scientific leadership in informatics and communications.

• Mathematical Finance
• Stochastic Processes
• Affine Processes
• +۵ مورد دیگر

Christa Cuchiero is a Professor at the Department of Statistics and Operations Research, Faculty of Business, Economics and Statistics, University of Vienna. Her research spans Mathematical Finance, Stochastic Processes, and Machine Learning applications in finance. She has over 48 publications, including recent work on signature-based models for SPX/VIX options, polynomial McKean-Vlasov SDEs, and infinite-dimensional Wishart processes. Her projects include 'Dynamic Uncertainty Modeling in Finance' and a long-term study on 'Universelle Strukturen in Finanzmathematik' (2020–2028). Research Interests: Signature methods for financial modeling Affine and polynomial processes Machine learning in finance Measure-valued stochastic differential equations Volterra equations and rough path theory Portfolio optimization and risk management Scientific Awards: Bruti-Liberati Visiting Fellowship (2018) Fellow at the Center for Advanced Study (CAS), Norwegian Academy of Science and Letters (2024) ETH Medal for Ph.D. thesis (2012) Recent Publications (2023–2025) focus on signature methods, stochastic portfolio theory, energy markets, and robust calibration techniques. Her work integrates advanced mathematical theory with practical financial applications, emphasizing nonlinear SPDEs, polynomial models, and machine learning frameworks.

Thomas Gärtnerمشاهده پروفایل

استاد

• Machine Learning
• Graph Neural Networks
• Geometric Deep Learning
• +۳ مورد دیگر

Thomas Gärtner is a Professor at the Institute of Logic and Computation within the Faculty of Informatics at Vienna University of Technology, leading the Machine Learning research group (E194-06). His work bridges theoretical machine learning with practical applications in chemistry, biology, and network analysis. His primary research focuses on graph neural networks (GNNs) and geometric deep learning, with significant contributions to GNN expressivity, graph transformations, and kernel methods for structured data. He explores fundamental questions about the limitations of message-passing architectures while developing practical enhancements like path-based extensions and expectation-complete representations. His chemical informatics work applies these techniques to binding affinity prediction, reaction classification, and solvent selection, demonstrating real-world impact in computational chemistry. Analysis of his 15 most recent publications (2023-2025) reveals three dominant research thrusts: theoretical GNN advancements (35% of articles), chemical informatics applications (40%), and novel learning frameworks (25%). The theoretical work increasingly addresses expressivity limitations through graph transformations and path-based approaches, while chemical applications show growing sophistication in molecular representation. Recent publications also indicate expanding interest in foundation models for graphs and robustness verification. He actively supervises master's students including Fabian Traxler (binding affinity prediction), Maximilian Plattner (SGD optimization), Fabian Jogl (graph transformations), and Thomas Schmied (reinforcement learning). His research is conducted through the Network Lab at TU Wien, where he serves as Principal Investigator for the Structured Data Learning with Generalized Similarities project.

• Biomechanics
• Soft Tissue Mechanics
• Cardiovascular Biomechanics
• +۵ مورد دیگر

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.

Ramin Hasaniمشاهده پروفایل

پژوهشگر

• Machine Learning
• Robotics
• Neural Networks
• +۳ مورد دیگر

Ramin Hasani is a researcher at TU Wien's Cyber-Physical Systems department. He holds a Dr.techn. (Doctor of Engineering) and specializes in machine learning applications for robotics, control systems, and biologically-inspired neural networks. His work focuses on developing interpretable neural architectures like Liquid Time-Constant Networks and Neural Circuit Policies, emphasizing safety and stability in autonomous systems. Hasani's research bridges neural network theory with practical robotics challenges, including autonomous racing, medical data analysis, and adversarial robustness. Key research areas include continuous-time neural networks, formal verification of neural ODEs, and bio-inspired control mechanisms derived from biological neural circuits (e.g., Caenorhabditis elegans). He collaborates extensively with institutions like MIT and ETH Zurich, contributing to projects in health-monitoring systems and end-to-end robot learning frameworks. His publications consistently address real-world challenges such as sepsis prediction via reinforcement learning and robust CNN architectures for image classification. Recent work highlights include developing stable recurrent networks through Gershgorin loss functions and advancing zero-shot transfer learning for autonomous systems. Hasani's interdisciplinary approach integrates principles from neuroscience, control theory, and machine learning to create auditable, high-performance AI solutions for cyber-physical environments.

Michael Kerberمشاهده پروفایل

استاد

• Computational Topology
• Algebraic Topology
• Computational Geometry
• +۴ مورد دیگر

Michael Kerber is a Professor at Graz University of Technology, Institute of Geometry, specializing in computational topology and geometry. His research bridges mathematical theory with applications in data analysis, focusing on persistent homology and geometric algorithms. PhD from Max Planck Institute for Informatics (2009) Postdoc positions: Max Planck Institute, Stanford University, IST Austria His work centers on designing efficient algorithms for topological data analysis, particularly: Persistent Homology 2-Parameter Persistence Geometric Filtrations Algebraic Curve Analysis High-Dimensional Sphere Packing Recent publications emphasize: Improved Delaunay bifiltration methods NP-hardness of interleaving distance computation Sparse Čech filtrations for big data Integration with graph neural networks He has co-developed key software tools: PHAT DIPHA HERA SOPHIA

• Network Biology
• Computational Systems Medicine
• Proteomics
• +۴ مورد دیگر

Jörg Menche holds a full Professor position (Univ.-Prof.) at the University of Vienna, with dual appointments in the Faculty of Mathematics, Department of Mathematics, and Max Perutz Labs, Department of Structural and Computational Biology. His work bridges mathematical modeling and biomedical research through network-based approaches to complex disease mechanisms. Primary research interests include Network Biology, Computational Systems Medicine, and Immunometabolism, with specific focus on proteomic signatures in neuropathic pain, metabolic reprogramming in obesity, and autoimmunity networks. He develops innovative computational tools like VRNetzer for virtual reality network analysis and BioProfiling.jl for high-content imaging data integration, targeting intersections between metabolism, immunity, and environmental exposures. Recent publications (2019-2024) reveal a dominant trend in multi-omics network medicine, particularly in autoimmunity (AutoCore framework), cardiometabolic diseases, and rare genetic disorders. His work demonstrates exceptional translational impact, with multiple papers highlighted by news outlets and patents (e.g., 3 patents referencing 2021 research). While the text indicates involvement in one project (2016) and 13 activities, grant specifics and student mentoring details remain undisclosed. His collaborative network spans immunology, neuroscience, and toxicology, evidenced by co-authorships with clinical and computational teams. Menche operates within Max Perutz Labs, a premier molecular biology research center in Vienna. His team integrates computational biologists, cell biologists, and clinicians to develop network cartography methods for disease module identification, with strong emphasis on virtual reality interfaces and multi-omics validation.

• Data Science
• Econometrics
• Economics
• +۵ مورد دیگر

Univ.-Prof. Martin Wagner is a Professor at the Department of Economics, Alpen-Adria-Universität Klagenfurt, where he serves as Vice-Dean of the Faculty of Management, Economics and Law and Head of the Quantitative Economics Division. His research bridges theoretical and applied econometrics with environmental and macroeconomic challenges. Affiliation: Department of Economics, Faculty of Management, Economics and Law, Alpen-Adria-Universität Klagenfurt Wagner’s work focuses on econometric theory and its applications, particularly in modeling nonlinear cointegrating relationships, environmental economics, and macroeconomic forecasting. His expertise includes: Cointegrating polynomial regressions Unit root processes and stationarity High-dimensional time series analysis Environmental Kuznets curves Panel data econometrics Macroeconomic modeling Recent publications highlight his contributions to robust estimation techniques and the intersection of climate science with economic growth dynamics. Contact: martin.wagner@aau.at

• Formal Methods
• Cyber-Physical Systems
• Probabilistic Programming
• +۴ مورد دیگر

Ezio Bartocci is a Full Professor in Formal Methods for Cyber-Physical Systems Engineering at TU Wien's Faculty of Computer Science. He leads the Trustworthy Cyber-Physical Systems (TrustCPS) Group within the Cyber-Physical System Research Unit. His research focuses on formal verification, probabilistic systems, and runtime monitoring, with applications in autonomous systems, safety-critical software, and embedded systems. Roles & Affiliations: Full Professor, TU Wien (100% research focus) Principal Investigator in projects funded by EU, WWTF, FFG, and industry partners Chair of the Curriculum Commission for Computer Engineering Editor-in-Chief of the Formal Methods in Outer Space series Research Interests: Formal methods for CPS: verification, synthesis, and runtime monitoring Probabilistic programming and loop analysis Temporal logic specifications and mining Automated tools for safety-critical systems (e.g., Polar, MoonLight) Applications in healthcare, robotics, and autonomous vehicles Key Projects: ProbInG (2020–2025): Analyzing probabilistic loops ARTIST (2021–2026): AI and robotics safety EdgeAI (2022–2025): Optimizing embedded processing TAIGER (2023–2027): Trustworthy AI and CPS Grants & Funding: €10M+ secured from EU Horizon 2020, WWTF, FFG, and industry partners like TTTech Auto AG. Academic Leadership: Teaches courses on logical methods, CPS engineering, and scientific research at TU Wien. Supervises PhD students in formal methods and CPS domains. Tools Developed: Polar (probabilistic loop analyzer), MoonLight (spatio-temporal monitoring), and FIM (fault injection tool).

Roman Słowińskiمشاهده پروفایل

استاد

• Decision Support
• Multiple Criteria Decision Aiding
• Ordinal Data Mining
• +۱۱ مورد دیگر

Roman Słowiński is a full Professor at the Institute of Computing Science , Poznań University of Technology, Poland, and holds a professorial position at the Systems Research Institute of the Polish Academy of Sciences in Warsaw. He is a Full Member of the Polish Academy of Sciences and served as President of its Poznań Branch (2011–2014). His career spans decades of contributions to decision support systems and computational intelligence. His research focuses on Multiple criteria decision aiding Rough set theory and fuzzy set theory Multiobjective optimization Ordinal data mining Robust decision analysis with applications in medicine, economics, and environmental studies. He is renowned for pioneering the use of rough sets in decision analysis, collaborating with Zdzisław Pawlak and others. His recent work emphasizes robust ordinal regression, dominance-based methods, and monotonicity constraints in classification. Scientific awards include EURO Gold Medal (1991) Edgeworth-Pareto Award (1997) Annual Prize of the Foundation for Polish Science (2005) Doctor Honoris Causa from three international universities He has advised 26 Ph.D. theses and served as Coordinating Editor for European Journal of Operational Research , alongside leadership roles in the International Rough Set Society and the EURO Working Group on Multiple Criteria Decision Aiding. He founded the Laboratory of Intelligent Decision Support Systems at PUT.

• Visual Interfaces
• Interactive Data Exploration
• Human-Computer Interaction
• +۳ مورد دیگر

Manuela Waldner is an Assistant Professor (tenure track) at TU Wien's Department of Computer Graphics and Visualization. She leads research in visual interfaces for exploring large-scale data, focusing on human-computer interaction and attention guidance in complex visualizations. Her work spans academic roles including Visualization Group membership and PhD Program Visual Heritage involvement. Research interests include: Interactive data exploration Guided visual attention mechanisms Emerging display environments (VR, multi-display systems) Publications emphasize practical applications of visualization techniques in domains like geographic data, medical imaging, and collaborative analytics. Notable projects include weBIGeo (geographic visualization) and Deskollage (visual information foraging). Awards include the Best Paper Award at EuroVA 2024 . Teaching roles include courses on Information Visualization , Computer Graphics , and Visual Computing . Active in academic service through program committees and editorial roles.

Andrea M. Tonelloمشاهده پروفایل

استاد

• Power Line Communications
• Wireless Communications
• Embedded Communication Systems
• +۱۲ مورد دیگر

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.

Chao Zhangمشاهده پروفایل

دانشیار

• Dielectrics
• Electrochemistry
• Electrolyte materials
• +۳ مورد دیگر

Chao Zhang is an Associate Professor at the Department of Chemistry-Ångström Laboratory, Uppsala University, specializing in computational electrochemistry and multi-scale modeling of electrolyte materials. His research bridges atomistic simulations with machine learning approaches to address challenges in energy storage and conversion systems. Education: Dr. rer. nat. from RWTH Aachen University (2013); Docent from Uppsala University (2020) Appointments: Postdoctoral researcher at the University of Cambridge (prior to joining Uppsala in 2017) His group develops finite-field methods for computational electrochemistry and investigates electrified solid-liquid interfaces. Recent research trends include neural rendering for underwater SLAM systems (2025), robust path-following control in marine robotics, and event-based localization in LiDAR-integrated environments. Scientific Awards: ERC Starting Grant (2020) Junior Research Fellowship, Wolfson College (2015) Jülich Excellence Prize for Young Scientists (2013)