جست‌وجوی استاد و پژوهشگر

Yunjie Yangمشاهده پروفایل

دانشیار

• AI-powered sensing and imaging
• Machine learning
• Soft sensors & electronics for robotics
• +۵ مورد دیگر

Dr. Yunjie Yang is an Associate Professor at the University of Edinburgh's School of Engineering, with affiliations at the Edinburgh Futures Institute (EFI), the Edinburgh Generative AI Laboratory (GAIL), and the Edinburgh Centre for Robotics. He previously held the Chancellor's Fellow in Data Driven Innovation (2018-2023) and Bayes Innovation Fellow (2023-2024) positions. His research focuses on AI-powered sensing and imaging, machine learning, and soft sensors & electronics for robotics. Yang received his PhD in Engineering Electronics from the University of Edinburgh, MSc in Control Science & Engineering from Tsinghua University, and BEng in Measurement & Control Engineering from Anhui University. After his PhD, he worked as a Postdoctoral Research Associate in Chemical Species Tomography before securing his lectureship. His research interests center on developing intelligent sensing systems that replicate human perception capabilities for robotics and intelligent systems. He pioneers flexible sensing and imaging technologies across various scales through innovative multi-modal sensors, soft electronics, and their modeling using machine learning approaches. His work aims to enable autonomous physical artificial intelligence by bridging the gap between robotic systems and human-like perception. Analysis of his recent publications reveals a strong focus on soft robotics perception, particularly through electrical impedance tomography (EIT) and transformer-based architectures. His research spans medical imaging applications, digital twin modeling for industrial processes, and machine learning approaches for sensor data interpretation. The trend shows increasing integration of physics-informed deep learning with traditional tomographic techniques to achieve higher accuracy and efficiency. European Research Council (ERC) Starting Grant (2024) IEEE J. Barry Oakes Advancement Award (2024) IEEE I&M Society Graduate Fellowship Award (2015) Multiple Best Paper Awards Senior Member of IEEE Fellow of the International Society for Industrial Process Tomography Fellow of the Higher Education Academy ESI highly cited papers Dr. Yang serves as Associate Editor for IEEE Transactions on Instrumentation and Measurement and holds editorial positions with Scientific Reports and IEEE Sensors Journal. His research has been licensed to overseas research institutes and industry partners and received wide media coverage including BBC, EFE, USA Today, and STV. He has secured significant grant funding including the prestigious ERC Starting Grant. He leads the Edinburgh SMART Lab (Sensing/imaging + Machine Learning + Robotics), which aims to replicate human perception capabilities for robotics and advance flexible sensing technologies through innovative multi-modal sensors and machine learning approaches. The lab focuses on enabling autonomous physical artificial intelligence with applications spanning medical diagnostics, industrial monitoring, and advanced robotics systems.

Ian Horrocksمشاهده پروفایل

استاد

• Knowledge Representation
• Description Logics
• Automated Reasoning
• +۴ مورد دیگر

Ian Horrocks is a Professor of Computer Science at the University of Oxford and a Fellow of Oriel College. His research focuses on knowledge representation, description logics, automated reasoning, and semantic web technologies. He has held academic positions at the University of Manchester (2003–2007) and served as Chief Scientist at Cerebra Inc. (2001–2006). Horrocks earned his BSc (1st class), MSc, and PhD in Computer Science from the University of Manchester (1981–1997). His work includes foundational contributions to ontology languages (e.g., OWL) and reasoning systems such as HermiT and ELK. He has supervised over twenty doctoral students and postdoctoral researchers. His honors include Fellowships from the Royal Society (2011), ECCAI (2009), and the British Computer Society (2005). He serves as Editor-in-Chief of the Transactions on Graph Data and Knowledge and leads initiatives in semantic web standards and knowledge graph applications. Key Roles: Editor-in-Chief (Journal of Web Semantics), Co-Chair (W3C OWL Working Group) Grants: EPSRC Senior Research Fellowship (2005), numerous international collaborations Labs: Oxford Semantic Technologies, involvement in projects like RDFox and PAGOdA

Christian Bargetzمشاهده پروفایل

استاد

• Nonlinear Functional Analysis
• Banach Spaces
• Locally Convex Spaces
• +۴ مورد دیگر

Christian Bargetz is a Professor of Functional Analysis at the University of Innsbruck, Austria, affiliated with the Faculty of Mathematics, Computer Science, and Physics (MIP). His primary research focuses on nonlinear functional analysis, Banach space theory, and distribution theory. He teaches advanced courses such as Optimization, Distribution Theory, and Functional Analysis, demonstrating his expertise in both theoretical and applied aspects of his field. Education: Completed his PhD in 2012 at the University of Innsbruck under the supervision of Norbert Ortner. His diploma thesis (2008) explored differential behaviors with Ulrich Oberst. Bargetz has held continuous academic positions since 2008, including roles as a lecturer and researcher. Research Interests: Specializes in iterative projection methods, generic properties of nonexpansive mappings, Fréchet spaces, and vector-valued distributions. His work bridges functional analysis with geometric measure theory and optimization, with applications in metric geometry and topological tensor products. Publications: Over 30 peer-reviewed articles in prestigious journals such as Canadian Journal of Mathematics , Journal of Mathematical Analysis and Applications , and Proceedings of the American Mathematical Society . Recent work includes studies on extremal nonexpansive mappings and Lipschitz function spaces. Grants & Projects: Principal investigator in FWF-funded projects on nonexpansive mappings and Banach spaces. Collaborates internationally, including with institutions in Israel, Poland, and Serbia. Teaching: Leads advanced courses in functional analysis, optimization, and distribution theory. Supervises bachelor's theses and master's projects on topics like extension operators for Lipschitz functions. Affiliations: Active member of the Functional Analysis working group and regularly participates in international conferences such as the Banach Afternoon, Winter School in Abstract Analysis, and DMV-ÖMG Annual Conferences.

Vladimir Kazeevمشاهده پروفایل

استادیار

• Numerical Analysis
• Tensor Methods
• Partial Differential Equ to the data-driven solution of PDEs
• +۳ مورد دیگر

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

Xu Jinchaoمشاهده پروفایل

استاد

• Multigrid methods
• Machine learning
• Finite element methods
• +۵ مورد دیگر

Xu Jinchao is a Professor of Applied Mathematics and Computational Sciences at King Abdullah University of Science and Technology (KAUST) and the Verne M. Willaman Professor of Mathematics at Penn State University. He has held distinguished roles, including Director of the Center for Computational Mathematics and Applications at Penn State since 1997 and is an Affiliated Faculty member of the College of Information Sciences and Technology at Penn State. His research focuses on numerical partial differential equations (PDEs), multigrid methods, machine learning, finite element methods, and domain decomposition methods. He is renowned for pioneering contributions such as the Bramble-Pasciak-Xu (BPX) preconditioner, Hiptmair-Xu (HX) preconditioner, Xu-Zikatanov (XZ) identity, and Morley-Wang-Xu (MWX) element. His work bridges computational mathematics and machine learning, including the development of MgNet, which unifies multigrid methods with convolutional neural networks. Xu has been recognized with numerous awards, including Fellowships from SIAM, AMS, AAAS, and the European Academy of Sciences. Notable accolades include the 2008 DOE Top 10 Breakthroughs for his HX preconditioner and the 1995 Feng Kang Prize for Scientific Computing. He has organized over 100 conferences and serves on editorial boards of top journals such as Mathematics of Computations and Numerische Mathematik . His leadership includes directing research centers and advancing computational science through collaborative efforts.

Eitan Tadmorمشاهده پروفایل

استاد

• Nonlinear Conservation Laws
• Spectral Methods
• Kinetic Theory
• +۵ مورد دیگر

Eitan Tadmor is a Distinguished University Professor at the Department of Mathematics and Institute for Physical Science & Technology at the University of Maryland. He holds the 2024 Chaire d'excellence at Sorbonne University's Fondation Sciences Mathématiques de Paris, and has served as Director of multiple research centers including the Center for Scientific Computation and Mathematical Modeling (2002-2016) and The Sackler Institute of Scientific Computation (1993-1996). Current: University of Maryland (2005-present) Previous: UCLA (1995-2002), Tel-Aviv University (1989-1995), CalTech (1980-1982) His research spans nonlinear conservation laws , entropy-stable schemes , collective dynamics , spectral methods , and multiscale modeling . He pioneered the spectral viscosity method and developed stability criteria for numerical schemes. Recent publications focus on swarm-based optimization , Euler-Poisson equations , and hydrodynamic alignment with over 15000 citations. His work on kinetic formulations and regularizing effects in PDEs has become foundational in computational mathematics. 2022 Norbert Wiener Prize (AMS-SIAM) 2022 Gibbs Lecturer (AMS) 2015 Peter Henrici Prize (SIAM-ETH) 2013-2021 Fellow of AMS/SIAM NSF grants (1999, 2008-2012, 2012-2020) He developed CentPack software for hyperbolic conservation laws and co-authored influential review papers on numerical methods and mathematical modeling. His collaborative work with institutions like IPAM, KI-Net, and ETH-ITS demonstrates international scientific leadership.

• Antenna Design
• Microwave Engineering
• Dielectric Materials
• +۲ مورد دیگر

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

• Model-Based Testing
• Automata Theory
• Machine Learning
• +۵ مورد دیگر

Bernhard Aichernig is an Associate Professor at the Institute of Software Engineering and Artificial Intelligence. His work bridges formal methods, model-based testing, and artificial intelligence, with a focus on automata learning, digital twins, and AI-assisted programming. Institution: Institute of Software Engineering and Artificial Intelligence Key Research Areas: Model-Based Testing, Automata Learning, AI-Driven Verification His research explores the integration of machine learning into formal verification, enabling scalable testing of complex systems like IoT devices and reinforcement learning agents. Recent projects include AI-Augmented DevOps frameworks (AIDOaRT) and digital twin validation (LearnTwins). Notable scientific awards include multiple best paper recognitions at SEFM (2020, 2021) and the TAYSIR Competition first place (2023). His publications emphasize hybrid approaches combining genetic programming, SMT solving, and neural networks for system modeling. 2025 : AI-assisted programming, timed automata via domain knowledge 2024 : Stochastic environment modeling, Git system learning 2023 : Reinforcement learning under partial observability, digital twins for VPN servers He actively contributes to testing frameworks like AALpy and investigates explainable AI for fault diagnosis in cyber-physical systems.

Harald Pichlerمشاهده پروفایل

دانشیار

• Membrane Biology
• Industrial Biotechnology
• Enzyme Engineering
• +۴ مورد دیگر

Harald Pichler is an Associate Professor and Team Leader at the Institute of Biotechnology (IMBT) at Graz University of Technology. His research focuses on membrane biology, enzyme engineering, and industrial biotechnology using yeast systems like Pichia pastoris . Key projects include engineering microbial membranes for protein secretion, optimizing biocatalysts for terpenoid synthesis, and studying lipid interactions with membrane proteins. Collaborations with companies like DSM and LONZA highlight industrial applications of his work. Research interests span microbial membrane modifications, sterol biosynthesis, and metabolic engineering for producing valuable metabolites. Major contributions include developing yeast strains for high-throughput screening of terpenoids and engineering hydratases for biocatalytic applications. His team also explores protein secretion pathways and membrane protein expression in yeasts, with patent filings for key genetic targets. Current projects involve lipid raft dynamics, SARS-CoV-2 spike protein interactions with membranes, and low-cost cell culture media alternatives. Collaborations with academic partners like the University of Geneva and Wageningen University emphasize interdisciplinary approaches. Publications frequently address membrane biophysics, enzyme mechanisms, and yeast strain engineering, reflecting a blend of fundamental and applied research.

• Optimization
• Convex Analysis
• Nonlinear Analysis
• +۳ مورد دیگر

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

Torsten Hoeflerمشاهده پروفایل

استاد

• Deep learning Systems
• Parallel programming models
• High performance and datacenter networking
• +۸ مورد دیگر

Torsten Hoefler is a Full Professor of Computer Science at ETH Zurich, Switzerland, with an adjunct appointment in Electrical Engineering. He previously held roles at the National Center for Supercomputing Applications (University of Illinois at Urbana-Champaign) and Indiana University. Full Professor of Computer Science, ETH Zurich (2020–present) Adjunct Professor of Electrical Engineering, ETH Zurich (2020–present) Member at Large, ACM SIGHPC Executive Committee (2013–present) Leadership roles in the MPI Forum and Blue Waters project His research focuses on performance-centric system design , with emphasis on scalable networking, parallel programming models, and performance modeling. Key contributions include the Slim Fly network topology, Data-Centric Python framework, and innovations in parallel graph computations and RDMA-based systems. Recent publications span topics like LLM training networks , quantization geometry , chiplet interconnects , and AI-driven climate modeling , reflecting his interdisciplinary approach combining HPC, AI, and hardware-software co-design. ACM Gordon Bell Prize (2019) ERC Consolidator Grant (2020) IEEE TCSC Award for Excellence (2019) SIAM SIAG/SC Junior Scientist Prize (2012) Latsis Prize of ETH Zurich (2015) He has received multiple best paper awards at top conferences (SC10, SC13, SC14, SC19, IPDPS'15, HPDC'15, OOPSLA'16) and contributed to MPI-3 standardization.

• Plant Biotechnology
• Molecular Biology
• Biochemistry
• +۴ مورد دیگر

Dr. Alexandra Machado Ferreira Castilho is a leading researcher at the Institute of Plant Biotechnology and Cell Biology within the University of Natural Resources and Life Sciences, Vienna (BOKU) . She has served as Principal Investigator since 2018 and previously as Scientific Coordinator of the Laura Bassi Center PlantBioP (2010-2017). Her work focuses on Plant Molecular Farming and Glycoengineering , aiming to optimize plant-based production of therapeutically relevant glycoproteins. Specializes in tailoring N-glycan profiles for biopharmaceuticals Develops plant cell factories for antibody and decoy receptor production Key projects include SARS-CoV-2 neutralization strategies and cancer immunotherapy agents Her recent publications analyze glycoengineering techniques in Nicotiana benthamiana , covering topics like ACE2-Fc decoys for viral neutralization, PD1/PDL1 blockade for oncology, and protease suppression for protein stability. She has received funding from the Austrian Science Fund (FWF) for projects on immune checkpoint inhibitors and glycosylation pathway modulation.

Alexandru Nicolauمشاهده پروفایل

استاد

• Parallelizing compilers
• Supercomputing
• High Performance Computing (HPC)
• +۵ مورد دیگر

Alexandru Nicolau is a Distinguished Professor and Chair of the Department of Computer Science at the University of California, Irvine (USA), where he has worked since 1992. He previously held positions as Associate Professor (1988–1992) and Assistant Professor (1984–1988) at UC Irvine and Cornell University, respectively. Education : B.A., Brandeis University (1980) MS (1981), Ph.D. (1984), Yale University Research Interests : A leading expert in parallelizing compilers , high-performance computing , and software-hardware co-design , Nicolau pioneered foundational techniques like Percolation Scheduling and Optimal Loop Parallelization . His work enables efficient exploitation of instruction-level parallelism in general-purpose programs, with applications in embedded systems , matrix algorithms , and GPU-based neural networks . He has also contributed to Electronic Design Automation (EDA) and lightweight synchronization protocols . Scientific Awards : IEEE Fellow (2014) ACM SIGPLAN Most Influential Paper (PLDI 20 years) EDAA/IEEE/ACM DATE Most Influential Paper (10 years) ACM ICS Most Influential Paper (25 years) 4 Best Paper awards (VLSI design 2003, ISHPC 2005, CASES 2008, IJCNN 2009) Advising & Grants : He has mentored notable scholars now at Stanford, McGill, and Google, and secured over $20M in funding from NSF , DARPA , and industry leaders like IBM and Intel . His professional service includes chairing ACM ICS’09 and PPOPP’13, and serving on steering committees for LCPC and ICS. Labs & Collaborations : His techniques have been adopted by IBM Watson, Siemens Munich, Fujitsu Labs Japan, and the open-source GCC compiler.

Michael Kunzingerمشاهده پروفایل

استاد

• Generalized Functions
• Differential Geometry
• Partial Differential Equations
• +۳ مورد دیگر

Michael Kunzinger is a Professor at the Department of Mathematics, Faculty of Mathematics, University of Vienna. His research spans generalized functions, differential geometry, and mathematical physics, with a focus on Colombeau algebras, Lorentzian length spaces, and non-smooth geometric structures. His recent work includes stochastic PDEs on manifolds, synthetic curvature bounds, and causality in Lorentzian geometry. Articles highlight applications to conservation laws, sectional curvature analysis, and rigidity theorems. Notably, he explores connections between generalized functions and smooth manifold theory, advancing geometric regularization techniques. 2024: 9 publications on singular limits, Ricci curvature, and synthetic geometry. 2022: Studies on null distance and singularity theorems in low-regularity spacetimes.

Sebastian Forsterمشاهده پروفایل

استاد

• Algorithms
• Graph Theory
• Network Science
• +۴ مورد دیگر

Sebastian Forster is a Professor at the University of Salzburg , affiliated with the Faculty of Digital & Analytical Sciences and the Department of Computer Science . He leads the Big Data Algorithms Group and holds administrative roles as Vice Dean, Local Coordinator of CIVIS Hub 5, and coordinator of the supplementary study programme "Informatikkompetenz für alle." His research focuses on algorithms for big and fast data, particularly distributed and dynamic algorithms applied to graphs, networks, and complexity theory. Education: Ph.D. in Computer Science (University of Vienna, 2011–2015) M.Sc. in Computational Intelligence (Vienna University of Technology, 2008–2011) B.Sc. in Computer Science (University of Passau, 2005–2008) Research Agenda: Forster's work addresses the theoretical and mathematical challenges of prior-free algorithms in the post-Moore's Law era. His key contributions include advancements in graph algorithms , fine-grained complexity , and network science . Recent publications span distributed Laplacian methods , dynamic graph sparsifiers , and approximate shortest path solutions , reflecting a blend of algorithmic design and hardness analysis. Scientific Awards: Heinz Zemanek Award (OCG) Award of Excellence (BMWFW) Academic Service: He has served on program committees for ICALP 2025, STACS 2024, FOCS 2023 , and other major conferences. He actively reviews for journals like Journal of the ACM and funding bodies such as BSF and ISF . Co-organized the Dagstuhl Seminar 22461 on Dynamic Graph Algorithms.