جست‌وجوی استاد و پژوهشگر

Can Liمشاهده پروفایل

استاد

• Physical Chemistry
• Environmental Catalysis
• Chiral Catalysis
• +۴ مورد دیگر

Can Li is a Full Professor at the Dalian Institute of Chemical Physics, affiliated with the Chinese Academy of Sciences (elected in 2003) and holds memberships in prestigious institutions such as the Academy of Sciences for Developing World (TWAS, 2005) and Academia Europaea (2008). His academic career spans decades, including leadership roles as Director of the State Key Laboratory of Catalysis (1998–2015), President of the International Association of Catalysis Societies (2008–2012), and Chairman of the Catalysis Society of China (2005–2012). Dr. Li’s research focuses on broad areas of catalysis and physical chemistry, including Environmental Catalysis , Chiral Catalysis , Photo(electro)catalysis , and Biocatalysis . His work also involves advanced analytical techniques like In-situ Characterization of Catalytic Reactions and Time-resolved Spectroscopy . While recent Google Scholar publications appear to focus on computer science topics like AI fairness and safety, his core research remains rooted in chemical sciences. His scholarly contributions have been recognized with multiple awards, including the National Catalysis Achievement Award of China (2014) , Natural Science Award of China (2011) , and the International Catalysis Award (2004) . He has held visiting professorships at institutions in Belgium, the USA, the UK, Japan, and France. 2014 National Catalysis Achievement Award of China 2011 Natural Science Award of China 2005 Outstanding Science and Technology Achievement Prize of the CAS 2005 HoLeungHoLee Prize 2004 International Catalysis Award 1998 National Award for Outstanding Young Scientists in China 1997 Hong Kong Qiu-Shi Outstanding Young Scientist Award

• Geotechnical Engineering
• Artificial Intelligence
• Computational Engineering
• +۳ مورد دیگر

Enrico Soranzo is a Senior Lecturer at the Institute of Geotechnical Engineering, part of the Department of Landscape, Water and Infrastructure at the University of Natural Resources and Life Sciences, Vienna (BOKU). His research focuses on the application of Artificial Intelligence in geotechnical engineering , particularly in tunnelling , slope stability , and soil mechanics . He has contributed extensively to understanding stress redistribution in tunnels, levee cross-sections, and probabilistic analysis using surrogate models. Research Highlights: Soranzo's work bridges computational engineering with physical modeling , utilizing geotechnical centrifuge testing and machine learning to address challenges in infrastructure stability. Recent studies involve predicting soil particle size distribution via convolutional neural networks and analyzing landslide transitions from slow movement to catastrophic failure. Scientific Awards: Brenner Award 2011 Advising and Grants: Though no explicit student names are listed, Soranzo has supervised theses on topics like root-reinforced slopes and tunnel face stability . His work often involves collaborations with institutions like Delft University of Technology and the Verband Beratender Ingenieure.

• Cloud Computing
• Business Process Management
• Workflow Systems
• +۴ مورد دیگر

Amirreza Tahamtan is affiliated with the Institute of Software Technology and Interactive Systems at TU Wien. His research focuses on cloud computing, business process management, workflow systems, temporal reasoning, and IT infrastructure optimization. He has contributed to developing frameworks for cloud service discovery, BPEL extensions for time management, and privacy-preserved interorganizational workflows. Key projects include the Network Lab and collaborative efforts with institutions like Rechenzentren. His work bridges theoretical advancements in workflow systems with practical applications in public sector IT integration and low-carbon supply chain management. Publications span from 2005 to 2012, emphasizing interdisciplinary approaches combining machine learning (e.g., SOM-based content analysis) with cybersecurity and neuroimaging data analysis.

• Machine Learning
• Nanotechnology Sensors
• Bayesian Inversion
• +۳ مورد دیگر

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.

Souvik Royمشاهده پروفایل

استاد

• Game Theory
• Mechanism Design
• Algebraic Graph Theory
• +۷ مورد دیگر

Souvik Roy is a Professor at the Applied Statistics Unit of the Indian Statistical Institute, Kolkata . His research spans multiple disciplines including Game Theory, Mechanism Design, Social Choice Theory, and Probability Theory with applications in diffusion processes, percolation, and random matrices. Education: PhD (2010) from the University of Maastricht; M.Stat (2006) from the Indian Statistical Institute, Delhi. Research Interests: Game Theory (Bayesian learning, supermodular games), Mechanism Design (randomized rules, constrained domains), Social Choice (ordinal Bayesian IC, hybrid domains), Probability Theory (diffusion, percolation). His recent work focuses on Bayesian best response dynamics , probabilistic cellular automata in percolation games , and incentive-compatible voting systems . Articles appear in journals like Games and Economic Behavior , SIAM Journal on Control and Optimization , and Social Choice and Welfare , reflecting interdisciplinary applications in economics, computer science, and statistical physics. Scientific Awards: Mahalanobis Memorial Medal (2024), Social Choice and Welfare Award (2024), Professor M.J. Manohar Rao Award (2016). Students: Mentored over 15 Master’s students (M.Stat, M.Math, MSQE) and PhD advisees, including Madhuparna Karmokar and Ujjwal Kumar, with projects on strategy-proofness, mechanism design, and graph-based voting.

• Artificial Intelligence
• Knowledge Representation and Reasoning
• Logic Programming
• +۵ مورد دیگر

Thomas Eiter is a Full Professor at the Vienna University of Technology (TU Wien) in the Department of Knowledge-Based Systems, Faculty of Informatics. His research focuses on artificial intelligence, knowledge representation and reasoning, logic programming, computational logic, and neurosymbolic AI integration. He leads projects in declarative problem-solving, intelligent agent systems, and stream reasoning frameworks like LARS. Eiter has contributed to foundational work in answer set programming (ASP), algebraic reasoning, and their applications in scheduling, robotics, and real-time data processing. He has extensive international collaborations, including EU-funded projects like HumanE-AI-Net and the Austrian Science Fund (FWF) initiatives. His work emphasizes bridging symbolic AI with modern machine learning techniques, particularly in visual question answering and neural-symbolic systems. Eiter has supervised numerous PhD students and maintains active roles in academic leadership, including editorial boards of journals like Theory and Practice of Logic Programming . Key contributions include development of the DLVHEX system for hybrid knowledge representation, optimization frameworks for ASP, and methodologies for stream reasoning in dynamic environments. His research also addresses ethical AI through projects like the TAIGER initiative, focusing on training AI agents with ethical rules.

• Computer Vision
• Optimization Methods
• Medical Imaging
• +۵ مورد دیگر

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.

• Social networks
• Algorithms
• Network science
• +۵ مورد دیگر

Aravind Srinivasan is a Professor in the Department of Computer Science at the University of Maryland, USA, since 2006. He was previously an Associate Professor with Tenure (2001-2006) and has held roles at Bell Laboratories and the National University of Singapore. B. Tech. , Indian Institute of Technology Madras Ph.D. , Cornell University His research focuses on randomized algorithms, social networks, combinatorial optimization, and probabilistic methods, with applications in public health, machine learning, energy, and networking. He explores the intersection of algorithms and networks, applying these techniques to domains like the social Web, biology, and energy systems. Elected Fellow of the ACM (2015), AAAS (2012), IEEE (2010), and EATCS (2017) Distinguished Faculty Award (University of Maryland, 2016) Distinguished Alumnus Award (IIT Madras, 2016) IBM Graduate Fellowship (1992) and Cornell Research Fellowship (1992) Best Paper/Best Student Paper Awards at conferences Dr. Srinivasan has mentored PhD students who now hold tenure-track positions, work in research labs (AT&T, IBM), major corporations, and U.S. Government agencies, while some have founded startups. He has served as Editor-in-Chief of the ACM Transactions on Algorithms and delivered keynote lectures globally.

• Computational Biology
• Bioinformatics
• Machine Learning
• +۱۵ مورد دیگر

Peter Sykacek is a researcher at the Institute of Computational Biology, Department of Biotechnology and Food Science, University of Natural Resources and Life Sciences, Vienna (BOKU). He holds the academic title of Privatdozent and has served as Head of Analytical Method Development at the Science Chair of Bioinformatics since 2006. His research integrates computational biology, bioinformatics, and machine learning to address complex biological questions in molecular biology, medical biotechnology, and systems biology. His research interests are centered on computational biology , bioinformatics , and machine learning , with a strong emphasis on statistical and probabilistic modeling . He applies these methods to diverse domains including genomics (RNA-seq, microarrays), proteomics, plant phenotyping, and medical biotechnology. His work often involves the development of novel computational frameworks for data analysis, such as for protein complex prediction, kinase activity assays, and root system modeling. The analysis of his recent publications reveals a consistent trend in applying advanced machine learning and statistical methods to biological data. His work spans from fundamental methodological developments in probabilistic modeling and force field validation to applied research in agricultural science (e.g., root phenotyping in Vicia faba) and medical biotechnology (e.g., EGFR mutation analysis, extracellular vesicle profiling). A key theme is the use of computational tools to extract meaningful biological insights from high-dimensional datasets. Dr. Sykacek has supervised numerous academic theses, including master’s and doctoral projects, indicating an active role in mentoring the next generation of scientists. He has been involved in organizing scientific workshops and has served as a reviewer for prominent journals such as Bioinformatics , Nature Biotechnology , and Scientific Reports , reflecting his standing in the academic community. He has contributed to several research projects, including the TD0801 project on statistical challenges in plant genome sequences, and has been a key participant in organizing the ECCB Satellite Meetings on Probabilistic Modelling in Computational Biology. His collaborative work spans multiple institutions and research groups, particularly within the BOKU ecosystem.

Georg Mayrمشاهده پروفایل

دانشیار

• Atmospheric Dynamics
• Lightning Prediction
• Climate Modeling
• +۳ مورد دیگر

Dr. Georg Mayr is an Associate Professor in the Department of Atmospheric and Cryospheric Sciences (ACINN) at the University of Innsbruck. His research focuses on atmospheric dynamics, lightning prediction, foehn wind patterns, and climate modeling. He leads projects such as LightningPredict and Profcast , addressing environmental risks like upward lightning at wind turbines and atmospheric deserts' impact on extreme weather events. Mayr's work integrates machine learning and statistical methods for probabilistic forecasting, including lightning processes and thunderstorm environments. He has co-authored over 50 peer-reviewed articles since 2016, emphasizing interdisciplinary approaches to climate and meteorological challenges. His team collaborates on tools like foehnix for scalable diagnostics and Cholesky-based regression models for multivariate data analysis. Key contributions include long-term foehn wind reconstructions, spatial lightning climatologies, and risk assessments for tall structures. Mayr's research supports practical applications in renewable energy safety and airport low-visibility forecasts. His lab, ACINN, fosters collaboration across atmospheric science and cryospheric studies.

Wei Wuمشاهده پروفایل

استاد

• Geomechanics
• Granular Materials
• Constitutive Modeling
• +۷ مورد دیگر

Wei Wu is a Full Professor and Head of the Institute of Geotechnical Engineering at BOKU University, within the Department of Structural Engineering and Natural Hazards. He leads research in geomechanics, numerical modeling, and AI applications in geotechnical engineering. Doctor of Engineering (Dr.-Ing.), Karlsruhe University, Germany (1986–1993) MSc in Geotechnical Engineering, Xian University of Technology, China (1982–1985) Bachelor in Civil Engineering, Wuhan University, China (1978–1982) Professor Wu's research focuses on geomechanics, granular materials, constitutive modeling, and numerical simulations for landslides, debris flows, and tunneling. His work integrates SPH, DEM, and hypoplastic models to simulate complex soil behavior. He also explores soil bioengineering and AI applications in geotechnics . His recent publications emphasize failure mechanisms, uncertainty analysis, and large deformation modeling using advanced computational methods. His recent articles highlight a strong trend in developing and applying hypoplastic constitutive models combined with meshfree methods (SPH, DEM) for simulating granular flows, landslides, and soil-structure interaction. Research spans from micro-scale particle behavior to macro-scale geohazard modeling , with increasing integration of machine learning and probabilistic frameworks . Advanced Grant, European Research Council, 2024 Gold medal, Technical University of Brno, Czech Republic, 2024 Professor Wu has supervised over 30 PhD and 100 MSc theses . He leads major research projects funded by the European Research Council (ERC) , European Commission , and Austrian Science Foundation , including the €2.5M ERC Advanced Grant 'Modeling transient granular flow' (2024–2029). He serves as founding Editor-in-Chief of Acta Geotechnica and leads the Otto Pregl Foundation for Fundamental Geotechnical Research. He heads the Institute of Geotechnical Engineering at BOKU and co-leads research on AI in Geotechnical Engineering . His team conducts advanced numerical simulations and experimental validations, particularly in landslide dynamics, tunnel stability, and root-reinforced slopes .

• Machine Learning
• Neuroscience
• Computer Science
• +۴ مورد دیگر

Christian Toth is a researcher active in fields including Causal Inference, Machine Learning, and Community Detection. His work involves interdisciplinary approaches at the intersection of computer science and neuroscience. Education: BSc Dipl.-Ing. Research focuses on causal modeling, probabilistic graphical models, and signal processing applications. Recent publications address Bayesian causal inference and random walk-based community detection. Collaborations include institutions like Graz University of Technology (inferred from Institute of Signal Processing and Speech Communication affiliation) and researchers in Austria.

• Evolutionary Anthropology
• Genetics
• Population Genomics
• +۴ مورد دیگر

Xin Huang is a researcher at the Faculty of Life Sciences , affiliated with the Department of Evolutionary Anthropology . Their work focuses on evolutionary genetics, particularly introgression, population genomics, and human evolution. Education : PhD in Life Sciences Research Interests: Xin Huang’s research spans evolutionary anthropology, genetics, and population genomics. Key areas include introgression , natural selection , and genomic landscapes , with applications in computational biology, human evolution, and bioinformatics. Recent publications highlight trends in adaptive introgression , machine learning for genomic analysis , and social-genetic diversity in early European farmers . These works emphasize collaborative, data-driven approaches to understanding human evolutionary history. Activities: Xin Huang has participated in academic events like the 13th International Conference on Learning Representations (2025), 12th International Conference on Learning Representations (2024), and Probabilistic Modeling in Genomics 2024 .

Carlos Eduardo Cancino-Chacónمشاهده پروفایل

استادیار

• Computational Models of Music Performance
• Human-Computer Interaction in Music
• Music Information Retrieval
• +۳ مورد دیگر

Carlos Eduardo Cancino-Chacón is an Assistant Professor at the Institute of Computational Perception at Johannes Kepler University Linz , Austria. His research focuses on computational models of music performance, human-computer interaction in music, and machine learning applications in music analysis. He holds a PhD from JKU Linz and has conducted postdoctoral research at the Austrian Research Institute for Artificial Intelligence and the University of Oslo. Education: PhD in Technical Sciences (2018), JKU Linz Master's in Electrical Engineering & Audio Engineering (2014), TU Graz Undergraduate Degrees in Physics (2011) and Piano Performance (2011) from the National Autonomous University of Mexico and National Conservatory of Music. Research Interests: Expressive music performance modeling Symbolic music processing Generative models for music Human-AI collaboration in music Notable Awards: Breakthrough of the Year (2021) for the ACCompanion project AccompaniX Competition Award (2017) Grants: Austrian Science Fund (FWF) grant (€457k) for the Rach3 dataset project (2024-2026) Teaching: Courses in musical informatics, machine learning, and artificial intelligence at JKU Linz.

Martin Weißmannمشاهده پروفایل

استاد

• Numerical weather prediction
• Atmospheric data assimilation
• Ensemble forecasting
• +۴ مورد دیگر

Martin Weißmann is a Professor for Theoretical Meteorology at the University of Vienna's Faculty of Earth Sciences, Geography and Astronomy, where he has served since 2020. Previously, he headed the Department of Meteorology and Geophysics (2020-2022) and the Data Assimilation Branch at the Hans-Ertel Centre for Weather Research. His research focuses on numerical weather prediction, atmospheric data assimilation, ensemble forecasting, error modeling, and applications of wind lidar and satellite data. Education includes an MSc in Meteorology from the University of Innsbruck (1996-2003) and a PhD from DLR Oberpfaffenhofen in cooperation with University of Innsbruck (2003-2006). His scientific commitments include membership in the ESA Aeolus Science and data quality Advisory Group and coordination of the project Experimental Validation and Assimilation of Aeolus Observations. Research interests center on: Advanced data assimilation techniques for numerical weather prediction Ensemble methods for probabilistic forecasting and error quantification Integration of novel observation systems like Doppler wind lidars Validation and application of Aeolus satellite wind data Convective-scale modeling and high-resolution forecasting His publication record demonstrates consistent focus on improving weather prediction systems through innovative assimilation methods, with recent work emphasizing machine learning applications, convective-scale data assimilation, and satellite data validation. Teaching responsibilities include courses in dynamic meteorology, numerical weather prediction, and scientific communication.