جست‌وجوی استاد و پژوهشگر

• Plant Breeding
• Genomic Selection
• Wheat Genetics
• +۳ مورد دیگر

Dr. Sebastian Michel is a researcher at the Institute of Biotechnology in Plant Production , part of the Department of Agricultural Sciences at the University of Natural Resources and Life Sciences Vienna (BOKU) . His work focuses on applying genomic and phenomic selection to improve disease resistance and climate adaptability in wheat species. Research interests include: Genomic selection for complex traits Plant disease resistance (Fusarium, Septoria, common bunt) Climate-resilient crop breeding Wheat genomics and QTL mapping Scientific contributions span numerous publications on Fusarium resistance mechanisms, deoxynivalenol detoxification, and genomic prediction models. He has supervised multiple MSc theses on transgenerational defense induction, allele prediction, and stripe rust resistance. Projects include EU-funded initiatives for: Climate Resilient Orphan Crops PhenoMix: Deep Learning in Wheat Breeding Sustainable Resistance Breeding in Ethiopia/Kenya/Zimbabwe Community engagement features keynote lectures on modern breeding methods and peer-review roles for journals like Frontiers in Plant Science and Theoretical and Applied Genetics .

Ashutosh Modiمشاهده پروفایل

دانشیار

• Natural Language Processing
• Machine Learning
• Artificial Intelligence
• +۳ مورد دیگر

Ashutosh Modi is an Associate Professor in the Computer Science and Engineering department at Indian Institute of Technology Kanpur. Previously, he conducted research at Disney Research in Pittsburgh, Los Angeles, and Zurich. His research interests span across Natural Language Processing , Machine Learning , and Artificial Intelligence , with specific focus on multi-modal Affective computing, Conversational Systems, and Natural Language Understanding. His work takes a statistical approach to developing natural language understanding models from statistical regularities in large corpora of text while leveraging linguistic theories. Dr. Modi has taught multiple courses at IIT Kanpur including: Statistical Natural Language Processing (CS779) - Fall 2020, Spring 2021, Spring 2023 Special Topics in Natural Language Processing (CS698O) - Winter 2020 Deep Reinforcement Learning (CS698R) - Fall 2021 Fundamentals of Computing (ESC101) - Spring 2022 His courses cover a comprehensive curriculum from basic linguistics fundamentals and language models to advanced topics like neural networks, word vectors, and transformer architectures including BERT. The teaching approach emphasizes both theoretical foundations and practical applications through research projects. Dr. Modi completed his PhD at Saarland University in Germany under the supervision of Prof. Dr. Manfred Pinkal and Dr. Ivan Titov. During his doctoral studies, he was associated with the Department of Computational Linguistics, MultiModal Cluster Initiative (MMCI), and Department of Computer Science.

• Cognitive Neuroscience
• Electroencephalography (EEG)
• Biosignal Processing
• +۶ مورد دیگر

Clemens Brunner is a Researcher at the Institute of Psychology within the Faculty of Natural Sciences at the University of Graz. His work bridges electrical/biomedical engineering and cognitive neuroscience, specializing in the neural mechanisms of arithmetic processing and numerical cognition. He actively develops open-source neuroimaging tools used globally in EEG research and sleep analysis. His research focuses on EEG oscillations, biosignal processing, and machine learning applications in cognitive neuroscience. Key interests include arithmetic fact learning, neural correlates of numerical order processing, and non-invasive brain stimulation effects on mathematical cognition. His expertise spans Python programming, statistical analysis, and contributions to major scientific libraries like scikit-learn and SciPy. Analysis of his recent publications reveals consistent emphasis on electrophysiological signatures of arithmetic processing, with growing integration of computational methods and neuromodulation techniques. His work demonstrates strong methodological innovation through open-source software development for EEG analysis and sleep staging. Brunner contributes to the University of Graz's "Brain and behavior" research network, developing tools like MNELAB, SleepECG, and XDF.jl that enhance reproducibility in neuroscience. His collaborations extend to major projects including MNE-Python and BNCI Horizon 2020, advancing brain-computer interface methodologies and neuroimaging standards.

Nadiya Gandalialikhaniمشاهده پروفایل

پژوهشگر

• Applied time series econometrics
• Behavioral macroeconomic theory
• Financial economics
• +۳ مورد دیگر

Nadiya Gandalialikhani is a researcher at the Institute of Economics within the Kurt Rothschild School of Economics and Statistics (RoSES) at Johannes Kepler University Linz. She holds an MSc degree and contributes to both research and teaching activities at the university. Her research interests focus on applied time series econometrics , behavioral macroeconomic theory , and financial economics , with particular expertise in energy markets and oil price forecasting. Dr. Gandalialikhani's work bridges traditional econometric methods with machine learning approaches to analyze complex financial and energy market dynamics. Dr. Gandalialikhani teaches Bachelor thesis seminars in Economics and Economic Psychology for the 2025 Winter semester, guiding students through research methodology and thesis development. Her office is located in the Kepler Building on the 1st floor (room K154/2C), and she is available for consultations by appointment. Her publication record demonstrates consistent contributions to energy economics, with four peer-reviewed articles published between 2013-2014 focusing on oil price volatility, neural network applications in forecasting, and energy commodity market relationships. Research focuses on advanced time series analysis in financial and energy markets Specializes in hybrid forecasting models combining econometrics with machine learning Active contributor to energy economics literature, particularly regarding oil markets Dr. Gandalialikhani collaborates with researchers across multiple institutions, as evidenced by her co-authored publications with scholars from various academic backgrounds. Her work has appeared in reputable journals including OPEC Energy Review, International Journal of Economics and Financial Issues, and International Journal of Energy Economics and Policy.

Ali Ozkesمشاهده پروفایل

استاد پژوهشی

• Collective Decision-Making
• AI Ethics
• Public Administration
• +۷ مورد دیگر

Dr. Ali Ozkes is a Research Professor at the Institute for Public Management & Governance and an affiliated researcher at the Competence Center for Applied AI & Scientific Computing at Vienna University of Economics and Business. He also holds a part-time professorship at SKEMA Business School. His academic journey includes a habilitation from University Paris 1 Panthéon-Sorbonne, a doctorate from École Polytechnique in Paris, a master's from Istanbul Bilgi University, and bachelor's degrees from the University of London and LSE. Dr. Ozkes' research spans several interconnected fields: Collective decision-making processes and consensus building Ethics of artificial intelligence and human-AI interaction Public administration and organizational behavior Behavioral social sciences and social choice theory Strategic decision-making in public and nonprofit sectors His recent publications (2024-2025) show a strong focus on the intersection of AI ethics, public administration, and organizational decision-making. Key themes include consensus building in leadership teams, ethical frameworks for AI in public sector applications, and cognitive models of decision-making. His work frequently appears in top journals across management, public administration, and AI ethics domains. Scientific recognition includes: WU Visiting Fellowship for Japan (2019) Dr. Ozkes actively supervises graduate students and has served on numerous doctoral committees. His research is supported by multiple interdisciplinary projects, including 'Morality, social choice and artificial intelligence' (2019-2021) and 'Establishment of an international research center for experimental financial markets' (2020-2025). He has collaborated with institutions worldwide including Aix-Marseille University, Caltech, Lund University, and Waseda University. Dr. Ozkes contributes significantly to academic service as co-editor of code and data for Management Science and co-editor of a special issue of Theory and Decision. He is also a member of several prestigious academic societies including the Academy of Management, Association for Computing Machinery, and Society for Social Choice and Welfare. His expertise was instrumental in developing the Code of Conduct for General Purpose AI Models under the EU AI Regulation.

Jozsef Aratoمشاهده پروفایل

پژوهشگر ارشد

• Cognitive Science
• Data Science
• Eye-tracking
• +۴ مورد دیگر

Jozsef Arato is a Research Fellow at the Vienna Cognitive Science Hub, University of Vienna, where he has worked as a senior scientist for data science since 2019. His research spans multiple interdisciplinary domains within cognitive science, with particular expertise in computational modeling of eye-movement patterns and visual statistical learning. His research interests focus on understanding the principles that guide learning and decision-making through computational modeling approaches. He investigates how eye-movement patterns can reveal learning processes, comparing gaze patterns across different observers. His work spans diverse application areas including art perception, environmental psychology, and animal communication systems. His publication record from 2020-2024 shows a strong focus on eye-tracking methodologies applied to art perception, with significant contributions to understanding how gaze patterns relate to aesthetic experiences. His research also extends to environmental conservation psychology and comparative studies of vocal learning in birds, demonstrating remarkable interdisciplinary breadth. Dr. Arato has been actively involved in academic service, having served as Program Chair of the Pattern Recognition in Neuroimaging Summer School in 2020. He organizes the CogData reading group and contributes to the academic community through collaborative research projects. As an educator, he teaches Scientific Computing in Python (TEWA 1) for the Psychology master's program at the University of Vienna. Previously, he taught Cognitive Modelling at both the University of Vienna and Eötvös University in Budapest, demonstrating his commitment to training the next generation of cognitive scientists and data analysts.

• Landslide Analysis
• Spatial Pattern Analysis
• Statistical Learning Models
• +۷ مورد دیگر

Ugur Öztürk is an Assistant Professor in the Department of Geography and Regional Research within the Faculty of Earth Sciences, Geography and Astronomy at the University of Vienna, specializing in landslide risk assessment and environmental modeling. His work directly contributes to UN Sustainable Development Goals through disaster risk reduction research. His academic credentials include: PhD in Earth Science from Universität Potsdam (awarded February 21, 2019) MSc in Civil Engineering for Risk Mitigation from Politecnico di Milano (awarded October 5, 2012) BSc in Civil Engineering (awarded June 30, 2010) Öztürk's research employs statistical learning models and physics-based simulations to analyze landslide triggers like earthquakes and heavy rainfall, with recent expansion into urban landslide impacts. His fingerprint reveals dominant expertise in Earthquake Trigger (100%), Spatial Analysis (100%), Urban Development (100%), and Heavy Rainfall (100%) within Earth and Planetary Sciences. He has received significant recognition: Postdoc Award in Natural and Engineering Sciences of the State Brandenburg (2023) Univerlecco Master's Scholarship (2010) Öztürk currently leads the €2.3M research project Getting Ahead of Landslides: Understanding Social and Environmental Interactions to Anticipate Future Urban Landslide Risk (UrbanSlide) (2025-2030), securing major grant funding. He maintains active public engagement through 7 press/media contributions, including interviews on landslide disasters and climate challenges covered by 16+ news outlets and 92+ social media accounts.

Thomas Forstnerمشاهده پروفایل

مدرس ارشد

• Medical Statistics
• Biostatistics
• Dental Implant Research
• +۳ مورد دیگر

Thomas Forstner is a Senior Lecturer and Deputy Head of the Department of Applied Systems Research and Statistics at Johannes Kepler University Linz. With over 15 years of academic experience, he has established himself as a prominent researcher in medical statistics with a particular focus on dental implant studies and clinical research methodology. His educational background includes: Bachelor's degree in Statistics from Johannes Kepler University Linz (2005), with thesis on "Imputation methods in Austrian economic statistics" and "Selected multiple test procedures" Master's degree in Statistics from Johannes Kepler University Linz (2007), graduated with distinction, with thesis on "ROC curves as a means of evaluating classification schemes" Dr. Forstner's research primarily focuses on medical statistics, particularly in dental implantology and clinical research. His work examines classification systems, covariate adjustment in medical studies, and statistical methodology for clinical trials. He has made significant contributions to understanding factors affecting dental implant success, sinus augmentation procedures, and bone regeneration techniques. His research bridges statistical methodology with practical clinical applications, providing valuable insights for both statisticians and medical professionals. His recent publications demonstrate a strong trajectory in dental implant research, with particular emphasis on sinus augmentation techniques, bone grafting materials, and long-term outcomes of implant procedures. He has also maintained an active interest in statistical methodology, particularly in multiple testing procedures and educational applications of statistics. Dr. Forstner is actively involved in teaching statistics across multiple disciplines including Business Informatics, Computer Science, and AI. He also teaches specialized courses in Biostatistics and Clinical Research at both JKU and external institutions including the University of Applied Sciences for Health Professions in Linz and Steyr.

• Computer Vision
• Explainable Machine Learning
• Statistical Models in AI
• +۳ مورد دیگر

Siavash Arjomand Bigdeli serves as an Associate Professor of Computer Vision at the Technical University of Denmark, following prior employment as a scientist at the Swiss Center for Electronics and Microtechnologies (CSEM). His research focuses on: Ante-/Post-Hoc explainability of machine learning models Integration of statistical models in learning/inference processes Philosophical methodologies in artificial intelligence development Advanced computer vision techniques for visual understanding Recent publications reveal consistent specialization in image restoration and stereo vision, employing deep learning architectures and probabilistic graphical models to solve core challenges in visual data reconstruction and temporal coherence. His work demonstrates strong interdisciplinary connections between theoretical machine learning, practical computer vision applications, and epistemological considerations in AI systems.

• Cybersecurity
• Energy Informatics
• Smart Grids
• +۳ مورد دیگر

Maximilian Jakob Schirl serves as a Junior Researcher at the Centre for Secure Energy Informatics within the Department of Computer Science, Faculty of Natural and Mathematical Sciences at Paris Lodron University of Salzburg. His work focuses on cybersecurity, energy informatics, and data-driven solutions for smart energy systems, contributing to multiple funded research projects including FTZ CyberSec and ECOSINT. His research spans cybersecurity in energy infrastructure, smart meter data analytics, industry 4.0 adoption, and digital readiness assessment. He employs advanced techniques like reinforcement learning and privacy-preserving algorithms to address challenges in local energy communities, supply chain resilience, and sociodemographic profiling from energy consumption patterns. Key methodologies include microaggregation for data anonymization, load profile analysis, and interoperability frameworks for Austrian energy systems. Recent publications demonstrate a strong trend toward machine learning applications in energy informatics, particularly reinforcement learning for production systems and privacy risk assessment in smart grids. His work bridges theoretical algorithms with practical industrial implementations, emphasizing data-driven optimization of low-voltage networks and secure energy community integration. Dr. Schirl actively participates in major funded projects including FTZ CyberSec (2025-2028) for cybersecurity evaluation, DAWN (2025-2026) for data-driven network optimization, ECOSINT (2021-2024) for energy community integration, and DIH West (2019-2023) supporting SME digitalization. These initiatives involve cross-institutional collaborations with researchers like G. Eibl and D. Radovanovic across Austria. As a core member of the Centre for Secure Energy Informatics, he contributes to developing secure, interoperable energy community frameworks through the ECOSINT project and advances privacy-preserving techniques for smart meter data within the FTZ CyberSec initiative.