جست‌وجوی استاد و پژوهشگر

• Plant Root Systems
• Image Analysis
• Rhizosphere Studies
• +۳ مورد دیگر

Dr. Pavel Baykalov MSc is a researcher at the Institute of Forest Ecology within the Department of Ecosystem Management, Climate and Biodiversity at the University of Natural Resources and Life Sciences (BOKU) in Vienna, Austria. His work focuses on the intersection of plant science, image analysis, and environmental monitoring. Baykalov's research interests center around plant root systems , rhizosphere studies , and the application of computer vision techniques to agricultural and ecological challenges. His work combines expertise in plant-soil interactions with advanced image processing methodologies to develop innovative solutions for root system analysis. His recent publications demonstrate a strong focus on applying convolutional neural networks to root length measurement challenges and automating image acquisition processes in rhizosphere research. These works reflect a trend toward integrating artificial intelligence with traditional ecological monitoring techniques to overcome challenges in noisy field conditions. Baykalov has presented his research at major international conferences including the European Geosciences Union General Assembly, indicating active participation in the global scientific community. His research appears to be connected with interdisciplinary teams working at the intersection of forest ecology, soil science, and computational methods, though specific lab affiliations or major grant funding are not detailed in the available information.

• Biomedical Engineering
• Ophthalmic Imaging
• Optical Coherence Tomography
• +۵ مورد دیگر

Christoph Hitzenberger serves as Associate Professor at the Center for Medical Physics and Biomedical Engineering within the Faculty of Medicine at Medical University of Vienna. His work focuses on advanced optical imaging techniques for ophthalmic applications, particularly polarization-sensitive optical coherence tomography (PS-OCT). His research spans corneal biomechanics in keratoconus, retinal nerve fiber layer analysis in glaucoma, and fibrosis tracking in age-related macular degeneration. Key methodologies include ultrahigh-resolution PS-OCT, machine learning for image classification, and large field-of-view depolarization mapping. His recent work demonstrates significant innovation in quantifying structural changes post-corneal crosslinking and establishing structure-function correlations in early glaucoma. Analysis of his 15 most recent publications reveals dominant trends in PS-OCT applications for corneal and retinal pathologies, with increasing integration of deep learning for image registration and disease classification. His work consistently bridges engineering innovation with clinical ophthalmology needs. No scientific awards were explicitly mentioned in the source materials. His technical leadership drives advancements in PS-OCT instrumentation and clinical translation, with collaborations spanning biomedical engineering and ophthalmology departments. Current projects emphasize longitudinal monitoring of fibrotic processes and machine learning-enhanced diagnostic frameworks.

Ulrich Bodenhoferمشاهده پروفایل

استاد

• Artificial Intelligence
• Machine Learning
• Fuzzy Logic
• +۴ مورد دیگر

Ulrich Bodenhofer serves as a full-time Professor for Artificial Intelligence at Upper Austria University of Applied Sciences (Hagenberg campus) since September 2020, while maintaining a part-time role as Chief Artificial Intelligence Officer at QUOMATIC.AI since June 2018. His institutional affiliations include Research Center Hagenberg AIST and multiple Centers of Excellence: Automotive/Mobility, Medical Technology/TIMed, Smart Production, Computed Tomography, and Digital Transformation within the Strength area of ICT - Information & Communication Technology. His academic credentials include a Habilitation (2003), Dr. techn. (1998), and Dipl.-Ing. (1996), all in Technical Mathematics from Johannes Kepler University Linz. His research focuses on applying fuzzy logic systems to practical problems across healthcare, industry, and finance. Key areas include: Machine Learning & Artificial Intelligence in Sales Analytics Healthcare and Bioinformatics applications Nondestructive Testing methodologies Financial Condition Monitoring systems His publication record shows consistent output since 1996 with 74 publications, demonstrating evolving expertise from foundational fuzzy logic research to current AI applications in medical imaging and industrial processes. Recent work emphasizes human-centered AI approaches, explainable systems, and practical implementations addressing real-world challenges in critical infrastructure and healthcare diagnostics. Bodenhofer actively leads and participates in significant research projects including HCAI (2022-2027), FLARE (2025-2027), and iReduce (2025-2026), securing funding from diverse sources including FWF - doc.funds.connect and KIRAS cooperative research programs. His collaborative approach spans multiple disciplines and institutions, reflecting the interdisciplinary nature of modern AI research. His professional activities include 54 scientific engagements through 2025, featuring numerous invited lectures on AI applications across finance, healthcare, and industrial contexts. He has supervised 9 academic works according to institutional records, mentoring the next generation of AI practitioners through hands-on research projects focused on practical implementation challenges.

• Machine Learning
• Image Processing
• Medical Imaging
• +۳ مورد دیگر

Gerald Adam Zwettler is a researcher at the University of Applied Sciences Upper Austria (FH Hagenberg) with a focus on digital transformation in Information and Communication Technology (ICT). His work spans machine learning applications in non-destructive testing, human-robot interaction, and sensor data classification. Active in projects like FLARE (Human-Centered AI for NDT), MARIE (Mobile Robotic Assistance), and MOVE (Orthosis Modeling) Collaborates internationally on railway infrastructure analysis and medical imaging Research interests include deep learning , medical image processing , IoT sensor analysis , and AI-driven customization of orthopedic devices . Recent work explores edge computing for low-energy IoT systems and multimodal interaction frameworks for office robots. Publications emphasize applied AI in technical domains, with methodological contributions to presegmentation techniques , Levenshtein distance optimization , and human-centered robotics . Project roles include principal investigator in knowledge databases for industrial plastic manufacturing.

Hannes Leebمشاهده پروفایل

استاد

• Inference Post Model Selection
• Model Selection in Statistics and Econometrics
• Asymptotic Statistics
• +۴ مورد دیگر

Hannes Leeb is a full-time Professor at the University of Vienna , leading the Department of Statistics and Operations Research within the Faculty of Business, Economics, and Statistics . He is also affiliated with the Research Network Data Science and co-organizes the ISOR Colloquium . Research Interests : Inference Post Model Selection, Shrinkage-Type Estimators, High-Dimensional Data Analysis, Machine Learning, Random Matrices, Asymptotic Statistics, Linear/Non-Linear Time Series. Recent Research Trends : His 15 most recent articles focus on valid statistical inference in high-dimensional settings, including F-statistics , conformal prediction , and adaptive methods for deep learning. Key subfields include post-model selection uncertainty , conditional coverage , and shrinkage estimator properties . Teaching & Leadership : He teaches core courses like 'Basic Principles of Statistics' and 'Probability Theory', and supervises PhD research privatissimum in econometrics/statistics. As department head, he emphasizes data science integration and methodological rigor .

• Neuroinformatics
• Brain-Computer Interfaces
• Causal Modeling
• +۳ مورد دیگر

Moritz Grosse-Wentrup is a Professor at the Faculty of Computer Science, University of Vienna , where he leads the Neuroinformatics research group. His work spans neuroscience, machine learning, and brain-computer interfaces , focusing on causal modeling and neurotechnology. Location: Kolingasse 14-16, Room 02.49, 1090 Wien Email: moritz.grosse-wentrup@univie.ac.at Phone: +43-1-4277-79610 Research interests include: Developing causal frameworks to bridge neuronal activity and cognition Designing interpretable machine learning algorithms for BCI applications Neurorehabilitation using virtual reality and EMG-BCI systems Advancing causal inference and feature importance in AI models Open-source tools like PyTES for neurostimulation research Recent publications highlight trends in auditory BCI, causal recourse, and Riemannian geometry-based classification . His work integrates neuroscience, computer science, and clinical applications to improve communication for impaired patients and motor rehabilitation. Students mentored include Alex Markham (2021), Gunnar König (2023), and Jiachen Xu (2023). He teaches foundational courses in data analysis, signal processing, and neuroinformatics at the University of Vienna.

• Bioarchaeology
• Proteomics
• Zooarchaeology by Mass Spectrometry (ZooMS)
• +۵ مورد دیگر

Manasij Pal Chowdhury is a Research Fellow in the Department of Evolutionary Anthropology at the Faculty of Life Sciences. His work contributes to the United Nations Sustainable Development Goals (SDGs) through interdisciplinary research in bioarchaeology, proteomics, and ancient human diet analysis. Research Focus: Bioarchaeology, ZooMS, collagen analysis, deep eutectic solvents, and prehistoric health. Collaborations: Active in international projects spanning France, Iraq, Kyrgyzstan, and Scotland. Publications: Recent studies include tobacco use detection in ancient dentine, robotic ZooMS integration, and wool production economies in Neolithic contexts. Methodologies: Expertise in protein extraction, ATR-FTIR spectroscopy, and machine learning for archaeological material screening.

Milad Hadidiمشاهده پروفایل

پژوهشگر ارشد

• Food Chemistry
• Food Science
• Plant-based Proteins
• +۴ مورد دیگر

Milad Hadidi is a post-doctoral researcher and visiting scientist with expertise in Food Chemistry and Plant-based Protein Extraction . Currently affiliated with the Institute of Physiological Chemistry at the University of Vienna , he has also held postdoctoral positions at the University of Castilla-La Mancha and University of Lleida in Spain, as well as a visiting researcher role at Aarhus University in Denmark. PhD in Food Technology (2016-2019, University of Lleida) His research focuses on sustainable extraction methods for plant-based proteins and bioactive compounds from agricultural byproducts, including deep eutectic solvents , microwave-assisted techniques , and ultrasound processing . He explores applications in food packaging , functional foods , and nanoencapsulation of bioactives for enhanced stability and delivery. Analysis of his recent publications (2024-2025) reveals a strong emphasis on waste valorization , green chemistry , and food safety through advanced analytical methods like sp-ICP-MS . His work bridges food technology with nanotechnology to address sustainability challenges. Scientific Recognition : 2024: Ranked among the top 2% of scientists worldwide by Stanford University and Elsevier

Martin Ehlerمشاهده پروفایل

دانشیار

• Computational Harmonic Analysis
• Mathematical Data Analysis
• Image Processing
• +۳ مورد دیگر

Martin Ehler serves as Associate Professor and Vice-Dean for Teaching at the University of Vienna's Faculty of Mathematics, where he concurrently holds the position of Deputy Head of the Institute of Mathematics. His academic activities are centered at Oskar-Morgenstern-Platz 1, Room 10.128, Vienna, with primary responsibilities spanning research leadership, teaching undergraduate/graduate mathematics courses, and administrative oversight of academic programs. His research program in Computational Harmonic Analysis drives innovations in Mathematical Data Analysis and Medical Image Processing , evidenced by his leadership in the Applied Harmonic Analysis Cluster (AHA) and co-organization of the bi-annual Strobl conference. Core methodologies integrate frame theory, manifold approximation, and spherical design principles to address challenges in neural network stability, medical imaging reconstruction, and signal processing robustness. Analysis of his 2023-2025 publications reveals dominant themes in harmonic analysis applications to deep learning architectures (particularly ReLU-layer injectivity and filterbank design), geometric approximation on manifolds, and medical imaging algorithms. These works demonstrate consistent interdisciplinary collaboration across mathematics, signal processing, and biomedical engineering domains. No scientific awards were documented in the provided materials. While teaching responsibilities for courses like Introduction to Analysis and Mathematics of Data Science are explicitly listed, details regarding graduate student supervision or research grant funding were absent from the source texts. His research ecosystem is anchored in the Applied Harmonic Analysis Cluster (AHA), facilitating cross-institutional collaboration on harmonic analysis applications, with documented partnerships through co-authored publications in venues like IEEE Signal Processing Letters and Mathematics of Computation.

• Music Information Retrieval
• Machine Learning
• Evaluation of Computer Experiments
• +۴ مورد دیگر

Arthur Flexer is a Post-Doctoral Researcher at the Institute of Computational Perception, Johannes Kepler University Linz (JKU). His work bridges music informatics, machine learning, and computational humanities. He leads projects like 'Pop Music Culture' and 'Spirits in Complexity' exploring AI in cultural contexts. Flexer holds a PhD and has advised multiple students, including Katharina Hoedt (PhD) and Selene Ximena Andrade Lopez (Master's). Research focuses on music similarity, adversarial attacks in recommendation systems, and ethical AI applications in art curation. He has organized major conferences like ISMIR 2019 and serves on editorial boards for Transactions of the International Society for Music Information Retrieval. His collaborations span museums like the Belvedere and institutions in Stockholm, Vienna, and Barcelona. Key projects include 'Dust and Data' (AI-driven museum curation), protein sequence analysis with the University of Vienna, and EU-funded initiatives on music perception. Flexer's work emphasizes validity in experiments, hubness reduction in high-dimensional data, and interdisciplinary approaches to music technology.

• Artificial Intelligence
• Machine Learning
• Explainable Artificial Intelligence
• +۳ مورد دیگر

Sebastian Tschiatschek is an Associate Professor at the Faculty of Computer Science, affiliated with the Research Group Data Mining and Machine Learning. His work contributes to UN Sustainable Development Goals through research in artificial intelligence, machine learning, and deep generative models. Key themes include explainable AI, forest ecology applications, and data clustering optimization. His recent publications focus on: (1) information needs for algorithmic decision-making (2025), (2) centroid-based deep clustering challenges (2024), and (3) drone-assisted forest ecology management (2024). Research spans interdisciplinary applications in environmental monitoring, stakeholder engagement, and democratic AI systems. Active projects include Interpretability and Explainability as Drivers to Democracy (2021-2025). Notable collaborations involve Austrian Computer Science Day organization (2024) and contributions to web survey methodologies (2024). His academic fingerprint includes 100% expertise in artificial intelligence, with secondary concentrations in training data, deep generative models, and forest management.

• Population Genetics
• Evolutionary Anthropology
• Ancient DNA Analysis
• +۳ مورد دیگر

Dr. Martin Kuhlwilm is an Assistant Professor in the Department of Evolutionary Anthropology at the Faculty of Life Sciences. His research focuses on population genetics, ancient DNA analysis, and primate genomics. He explores topics such as genetic diversity in great apes, ancient pathogen evolution, and the application of deep learning to population genetic inference. His work contributes to understanding human ancestry, infectious disease history, and conservation genetics. Key research interests include genomic substructure in bonobos, introgression detection in ancient populations, and viral genome analysis from museum specimens. He has published on methodologies like the SAI Python package for adaptive introgression studies and co-authored reviews on deep learning applications in population genetics. Collaborations span institutions globally, with activities including symposia on ancient genomics and presentations on ghost admixture detection. His recent work emphasizes enhancing sampling strategies for ancient pathogen DNA (e.g., Plasmodium studies) and curating genomic resources for great apes. While no specific awards are listed, his publications highlight impactful contributions to evolutionary biology and genetics.

• Autonomous Systems
• Computer Vision
• Sensor Technology
• +۵ مورد دیگر

Prof. Daniel Watzenig is a Full Professor at Graz University of Technology's Faculty of Electrical Engineering and Information Technology, affiliated with the Institute of Visual Computing and the Institute of Electrical Measurement and Sensor Technology. He serves as Dean of Studies for Digital Engineering (DE), overseeing academic programs in this field. His research focuses on autonomous systems, sensor fusion, and advanced control strategies for vehicles, with a strong emphasis on LiDAR technology, path planning algorithms, and real-time environmental perception systems. He holds academic qualifications including Dipl.-Ing. (FH) and Dr.techn. degrees. Research interests span robotics, computer vision, and machine learning applications in transportation systems. His work addresses challenges in autonomous vehicle navigation, sensor reliability under adverse conditions, and safety-critical system validation. Recent publications highlight innovations in thermal-LiDAR fusion, trajectory optimization, and radar-based occupancy grids. He actively contributes to interdisciplinary projects involving co-simulation frameworks and virtual validation methodologies for automated driving functions. Professional roles include leading academic programs in digital engineering and managing research collaborations at TU Graz. His technical expertise is reflected in over 100 publications (2020-2025) covering sensor technology, path planning algorithms, and autonomous system validation. Key labs/teams associated include the Institute of Visual Computing's autonomous systems group and the Electrical Measurement Institute's sensor innovation team.

• Artificial Intelligence
• Robotics
• Systems Engineering
• +۳ مورد دیگر

Peter Pavel Arthur Petráš is a Tutor at the Institute for Intelligent System Technologies within the Faculty of Technical Sciences at the University of Klagenfurt. His research focuses on interdisciplinary applications of artificial intelligence, robotics, and systems engineering, with contributions to fields like swarmalator dynamics, automated systems design, and media literacy in digital environments. He is actively engaged in collaborative projects involving industry partners such as Infineon Technologies Austria AG and the Austrian Research Promotion Agency (FFG), emphasizing practical innovations in technology and education. While not listed as a project head, his role supports cutting-edge research in areas like radar simulation frameworks, garment sorting automation, and multi-agent localization systems. His work bridges theoretical advancements with real-world implementation, particularly in edge-cloud computing and interdisciplinary methodological innovations. Recent publications highlight his expertise in AI-driven solutions, robotics, and the societal implications of digital media. Notable themes include analyzing online political microtargeting avoidance, improving multi-robot exploration algorithms, and exploring poetry workshops in migration research. His contributions align with the University of Klagenfurt’s focus on fostering innovation through its Third Mission initiatives, emphasizing knowledge transfer to regional industries and public sectors. Petráš collaborates on projects addressing societal challenges, such as promoting media literacy through the #NoFakeFacts! Citizen Science initiative and enhancing wind turbine blade inspection via multimedia drones. His work reflects a commitment to both technical innovation and addressing ethical and educational dimensions of emerging technologies.

• Speech technology
• Phonology
• Language processing
• +۴ مورد دیگر

Janet Pierrehumbert is a Professor of Language Modelling at the Oxford e-Research Centre (part of the Department of Engineering Science, University of Oxford). She holds a courtesy appointment in the Faculty of Linguistics, Philology and Phonetics and is an Associate Member of the Oxford-Man Institute of Quantitative Finance. Previously, she served as a Professor at Northwestern University (1993–2015), where she held leadership roles including department chair and director of graduate studies. Her interdisciplinary research spans experimental and computational linguistics, focusing on phonology, morphology, and computational linguistics. She is a Fellow of the American Academy of Arts and Sciences, the Cognitive Science Society, and the Linguistic Society of America, and a member of the National Academy of Sciences (USA). Education: BA in Linguistics and Mathematics (Harvard University); PhD in Linguistics (MIT). Research Interests: Pierrehumbert’s work integrates computational methods with linguistic theory to explore sound change, morphological processing, and language modeling. Her studies often address how statistical patterns in speech and lexicons influence phonological cognition. Recent projects include improving NLP models (e.g., BERT) through linguistic insights and developing benchmarks for hate-speech detection algorithms. Publications: Her work spans decades, with notable contributions to computational linguistics, phonology, and sociolinguistics. Recent articles highlight advancements in NLP ethics, morphological theory, and sound change mechanisms. Earlier foundational studies include pioneering work on Japanese tone structure and intonational pragmatics. Awards & Memberships: Her honors include the ISCA Medal for Scientific Achievement (2020) and Guggenheim Fellowship (1996). She co-founded the Association for Laboratory Phonology and is active in interdisciplinary initiatives like the Northwestern Institute on Complex Systems. Labs & Teams: Her research group at Oxford focuses on robust language technology and theoretical linguistics. She collaborates across disciplines, bridging computational and experimental approaches to language science.