Find Academic Supervisors

• Neuropsychology
• Neurofeedback
• Neural Plasticity
• +7 more

Guilherme Maia de Oliveira Wood is a University Professor and head of the Neuropsychology/Neuroimaging department at the University of Graz, where he has been employed since 2011. His research focuses on developing high-tech tools for neuropsychological rehabilitation, clinically testing their efficacy, and understanding the mechanisms influencing neural plasticity. He is actively involved in the interdisciplinary Master's program 'Computational Social Systems' offered jointly by the University of Graz and Graz University of Technology. His educational background includes: Psychology studies at the Federal University of Minas Gerais (1994-1999), Brazil Doctorate in Psychology at RWTH Aachen (2001-2005), Germany Habilitation in Psychology at the University of Salzburg (2005-2011) Wood's research interests center on combining technological innovation with cognitive science, particularly in neuropsychology and neurofeedback applications. He investigates neural plasticity as the brain's ability to organize itself as a dynamic system, with special attention to psychosocial influences on treatments and rehabilitation. His work bridges technical neuroimaging approaches with ethical considerations regarding neurotechnologies. His recent publications demonstrate a strong interdisciplinary approach spanning neuroscience, psychology, data science, and ethics. The articles reveal consistent focus on methodological rigor in neuroimaging, cognitive mechanisms in learning and rehabilitation, and critical examination of psychosocial factors influencing scientific evidence interpretation. His work increasingly addresses the emotional embedding of scientific communication and placebo effects in neurofeedback research. Wood actively supervises master's students through the 'Computational Social Systems' program and offers thesis topics on brain aging, neurofeedback mechanisms, graph theory applications in neuroscience, and the relationship between motor learning and cognitive function. He leads an FWF-funded research project investigating brain plasticity through fMRI-based neurofeedback training. He is affiliated with several research initiatives including COLIBRI (Complexity of Life), Human Factor in Digital Transformation X, and Neurofeedback Research Graz. His laboratory employs multi-sequence MRI techniques including T1-weighted recordings, diffusion-weighted imaging, resting-state fMRI, and GABA spectroscopy to investigate changes in brain structure, function, and chemistry in response to neurofeedback training.

Klaus MuellerView profile

Professor

• Visual Analytics
• Explainable Machine Learning and AI
• Algorithmic Fairness and Transparency
• +7 more

Klaus Mueller is a Professor in the Computer Science Department at Stony Brook University , with additional appointments in Biomedical Engineering and Radiology. He serves as Director of the Visual Analytics and Imaging (VAI) Lab, Liaison for the SUNY Korea CS Program, and Interim Chair of the Department of Technology and Society . His career spans roles at Brookhaven National Lab and leadership positions at SUNY Korea. Dr. Mueller earned his PhD in Computer and Information Science (1998), MS in Computer and Information Science (1996), and MS in Biomedical Engineering (1990) from The Ohio State University , alongside a BS in Electrical Engineering (1987) from the Polytechnic University of Ulm, Germany. His research focuses on visual analytics , explainable AI , algorithmic fairness , computational imaging , and medical imaging . He has pioneered GPU-accelerated CT reconstruction techniques, bias mitigation frameworks (e.g., D-BIAS), and tools like DOMINO for causal reasoning. His work bridges data science , human-computer interaction , and medical applications , often integrating large language models for visualization tasks. Recent publications highlight advances in multivariate volume rendering , LLM-driven bias detection , and mDDPM-based medical image synthesis . His articles span IEEE Transactions , Nature Machine Intelligence , and conferences like IEEE VIS and ACM CHI . Award highlights include NSF CAREER (2000), SUNY Chancellor Award (2011), IEEE Golden Core Award (2016, 2022), induction into the National Academy of Inventors (2018), and elevation to IEEE Fellow (2024). He has chaired major conferences and served as Editor-in-Chief of IEEE Transactions on Visualization and Computer Graphics (2019-2022). He teaches graduate and undergraduate courses in visualization , medical imaging , and GPGPU programming , and leads the Visual Analytics Seminar (CSE 648). His lab ( VAI Lab ) fosters interdisciplinary research in GPU-accelerated analytics and ethical AI.

• Applied Mathematics
• Computational Physics
• Scientific Machine Learning
• +10 more

Lukas Exl is a Senior Lecturer at the University of Vienna and a Research Director at the Wolfgang Pauli Institute (WPI), where he leads the Mathematical AI/ML Research Division. He holds a habilitation (venia docendi) in Computational Science from the University of Vienna, the first in this interdisciplinary field. Research Platform MMM Mathematics-Magnetism-Materials Wolfgang Pauli Institute (WPI), Vienna His research integrates Applied Mathematics, Computational Physics, and Scientific Machine Learning, focusing on numerical methods for PDE-based simulations, data-driven modeling, and reduced-order approaches. Key applications include computational micromagnetism for green energy materials and developing physics-informed neural networks (PINNs) with interpretable architectures. Recent publications emphasize machine learning techniques for magnetic material optimization, stray field computation, and trustworthy AI (TAI/XAI). He supervises students in Computational Science, Applied Mathematics, and Physics, with a focus on Extreme Learning Machines (ELMs), PINNs, and tensor decomposition methods. Data-driven Reduced Order Approaches for Micromagnetism (FWF Project, €484k, 2024-2028) Design of Nanocomposite Magnets by Machine Learning (FWF Project, €254k, 2022-2027) Reduced Order Approaches for Micromagnetics (FWF Project, €402k, 2018-2024) His team includes researchers like Dr. Sebastian Schaffer (PhD graduate), Kein Gjordeni, and Caroline Maitz. Collaborations span Danube University Krems and Technical University of Denmark's Energy Conversion and Storage department.

• Natural Language Processing
• Machine Learning
• Data Mining
• +7 more

Pedro Henrique Luz de Araujo is a member of the Faculty of Computer Science affiliated with the Research Group Data Mining and Machine Learning . His work focuses on Natural Language Processing , Language Modeling , and Behavioral Learning , particularly in Brazilian legal text domains. He has contributed to advancements in named entity recognition , hate speech detection , and specification-driven AI systems . Active in 2022-2025 with publications in PLoS ONE and top NLP conferences Organized the KONVENS 2024 conference Research emphasizes LLM calibration , multimodal classification , and domain-specific datasets His recent publications show expertise in large language models (2025: persona effects; 2024: specification overfitting, text-guided image clustering), NER techniques (2024), and legal document processing (2023: multimodal classification; 2020: VICTOR dataset, LeNER-Br).

• Machine Learning
• System Architecture
• MLOps
• +6 more

Stephen John Warnett is a researcher affiliated with the Faculty of Computer Science, specializing in Software Architecture. His work focuses on architectural design decisions for machine learning systems, particularly MLOps and RLOps frameworks. Research Interests: Primary areas include Machine Learning, System Architecture, and Reinforcement Learning. Key subtopics involve architectural design decisions, quality assessment, understandability, data integration, and deployment practices in cyber-physical systems. Publications Trends: Recent work emphasizes model-driven metrics for MLOps architectures, understandability in machine learning systems, and decision-making support for training strategies in reinforcement learning. Scientific Awards: Best Paper Award (2022)

Michael AffenzellerView profile

Professor

• Genetic Programming
• Symbolic Regression
• Evolutionary Algorithms
• +6 more

Michael Affenzeller is a Professor at the University of Applied Sciences Upper Austria, leading the HEAL AIST Center of Excellence for Smart Production with a focus on Digital Transformation. His research spans Genetic Programming, Symbolic Regression, and Evolutionary Algorithms, with applications in optimization, AI, and manufacturing automation. He has authored over 400 publications and led major projects such as HCAI (Human-Centered AI) and HEAL, emphasizing interdisciplinary collaboration. Research interests include developing advanced algorithms for dynamic optimization, prescriptive analytics, and federated learning in industrial contexts. He has contributed to projects like LOISI (Logistics Optimization in Steel Industries) and SimGenOpt2, addressing challenges in production planning and workforce optimization. Key collaborations involve institutions in Austria and beyond, focusing on smart manufacturing, logistics, and AI integration. His work bridges theoretical advancements with real-world applications, driving innovation in energy systems, crane scheduling, and predictive maintenance. Grants and projects include funding from FWF (doc.funds.connect) and Upper Austrian initiatives, emphasizing doctoral training and industry partnerships. He actively participates in conferences such as EUROCAST and PPSN, contributing to algorithmic advancements and computational theory.

• Image Processing
• Computer Vision
• Industrial Inspection
• +6 more

Kurt Niel is a Professor at the University of Applied Sciences Wels, affiliated with the Research Center Wels. His research focuses on Image Processing, Computer Vision, and 3D Mapping, with a strong emphasis on archaeological and industrial applications such as tunnel inspection, sensor technology, and quality control. He has conducted extensive work on documenting underground structures like Erdstalls in Upper Austria using advanced 3D surveying techniques. Niel has organized conferences like the OAGM & ARW Joint Workshop and QCAV'09, and has presented on topics including machine vision and wildlife safety systems. His work bridges engineering and archaeology, contributing to both academic and applied fields. His activities include organizing workshops and delivering invited talks on topics such as sensor systems preventing wildlife-vehicle accidents and the necessity of basic machine vision education at universities. While no specific educational background is detailed, his professional roles and research outputs highlight expertise in technical and interdisciplinary domains. Supervised work includes six documented projects, though student names are not specified. Niel’s research interests also extend to industrial inspection and heat treatment processes, leveraging computer vision systems and machine learning for quality control. His contributions to sensor technology and 3D mapping have practical applications in both heritage preservation and industrial safety.

Michael ReiterView profile

Senior Lecturer

• Machine Learning
• Pattern Recognition
• Computer Vision
• +4 more

Michael Reiter is a Senior Lecturer at the Institute of Visual Computing & Human-Centered Technology, part of the Faculty of Informatics at TU Wien. His research focuses on Machine Learning, Pattern Recognition, and Computer Vision, particularly in medical applications such as flow cytometry data analysis for leukemia diagnosis and minimal residual disease (MRD) assessment. He leads the MyeFLOW project (2020–2025), which develops automated methods for immunophenotyping and MRD detection in pediatric leukemia. His work bridges machine learning and clinical diagnostics, emphasizing explainable AI in healthcare. Reiter collaborates with medical institutions to translate algorithms into clinical tools, such as the FlowMe decision support system. His research interests include transformer networks, UMAP visualization, and Gaussian mixture models for biomedical data. He has supervised diploma theses on topics like meta learning for blast cell classification and U-Net-based flow cytometry analysis. Reiter’s publications span over two decades, with recent emphasis on medical AI applications. His lab, the Computer Vision Lab, focuses on visual computing for healthcare, robotics, and cultural heritage digitization. Current projects address automated analysis of museum herbarium collections and empathic robotic systems for autism therapy.

• Formal Methods
• Machine Learning Verification
• Natural Language Processing
• +1 more

Martin Tappler is a Postdoctoral Researcher in the Cyber-Physical Systems group at TU Wien's Faculty of Informatics. His work focuses on formal methods, scalable testing for AI systems, and NLP model interpretability. Publications include studies on generative automata models, importance-driven testing for reinforcement learning, and semantic analysis of RNNs. He contributes to the TAIGER project (2023-2027) on AI-driven systems.

• Analytical Chemistry
• Chemometrics
• Proteomics
• +3 more

Lutgarde Buydens is a Full Professor and head of the Analytical Chemistry department at Radboud University in the Netherlands, within the Faculty of Sciences and Institute for Molecules and Materials. She has maintained a continuous academic career at Radboud University since 1989 and served as Vice Dean of the Faculty of Science from 2012-2015. Her educational background includes a Pharmacy degree (1978, cum laude) and PhD (1986, cum laude) from the Brussels Free University in Belgium, with doctoral research on "Structure activity relationships" under Prof. Luc Massart and Prof. Paul Geerlings. Prior to joining Radboud, she completed postdoctoral work at the University of Illinois Medical Center in Chicago (1982-1983) and research at the Free University of Brussels (1984-1988). Professor Buydens specializes in developing novel methods for chemometric data handling and interpretation of complex multivariate data in chemical and biological fields. Her research philosophy emphasizes methodological work valuable across application areas, with focus on application-oriented research that validates methods for pioneering science. Her work spans analytical chemistry, chemometrics, proteomics, metabolomics, and chemical data sciences, with particular attention to data that doesn't exhibit well-behaved statistical characteristics. Her publication record includes 264 scientific papers, 4 handbooks, and 270 conference papers, with 30% published in top 10% impact factor journals. Her work has received over 12,100 citations (Google Scholar), with an h-index of 51. Notably, her publications rank her 6th among chemometricians and 24th among analytical chemists globally, placing her in the top 2% of researchers worldwide in her field. EAS award for outstanding achievements in Chemometrics (2012) Christophe Plantin Award for outstanding accomplishments of a Belgian citizen abroad (2000) Honorary member of Italian Chemometric Society (1995) First Elsevier Chemometrics Award for young scientists (1992) Ranked 24th in The Analytical Scientist's Power List 2013 and 2015 (top 100 most influential people in analytical sciences) Professor Buydens has led research through numerous (inter)national collaborative projects, including five European Collaboration Consortia. Her iconic "blue chemometrics books" (Handbook of Chemometrics and Qualimetrics) serve as reference materials in the field with over 3,300 citations. Her work bridges theoretical method development with practical applications in chemistry and biology, demonstrating consistent excellence that has established her as a world leader in chemometrics and analytical chemistry.

• Neuroimaging
• Consciousness
• Neurostimulation
• +4 more

Steven Laureys is a Belgian-Canadian neurologist and neuroscientist holding the Canada Excellence Research Chair in Neuroplasticity at the CERVO Brain Centre (Laval University, Canada) since 2024. He also maintains positions as Professor at Laval University's Faculty of Medicine and Research Director at the Belgian National Fund for Scientific Research (FNRS). A pioneer in consciousness studies, Laureys established the Coma Science Group (2006) and GIGA Consciousness Unit (2014) at the University of Liège, Belgium, before co-founding the Hangzhou International Consciousness Institute (2015) and BRAIN-NM Neuromodulation Clinics (2023). MD, PhD, Vrije Universiteit Brussel (1993) M.Sc. in Pharmaceutical Sciences, VU Brussels (1998) PhD in Neurology of Consciousness, University of Liège (2000) His research focuses on clinical and cognitive neuroscience , particularly disorders of consciousness (coma, vegetative states, locked-in syndrome), neuroplasticity, and neuromodulation therapies. Using multimodal neuroimaging (PET, fMRI, EEG), he investigates brain dynamics during anesthesia, sleep, and meditation while developing novel diagnostic tools and rehabilitation strategies for brain-injured patients. Scientific Trends across his 450+ publications reveal extensive work on EEG microstate analysis (2025), thalamic firing patterns (2025), and neurostimulation techniques (taVNS, tDCS) for consciousness disorders. His team's 2024-2025 studies emphasize AI-driven consciousness mechanisms, cross-cultural end-of-life decision-making, and z-drug paradoxical effects. Scientific Recognition includes: 2004 - ASSC William James Prize 2017 - Francqui Prize (Belgium's highest scientific honor) 2024 - Canada Excellence Research Chair ($32M award) 2024 - Health 2.0 Outstanding Leadership Award As Editor-in-Chief of Brain Connectivity (2024), he advances interdisciplinary dialogue between neuroimaging, neurology, and computational science. His translational work bridges basic neuroscience with clinical applications, including co-founding international neuromodulation clinics and leading multicenter trials in traumatic brain injury rehabilitation.

• Cardiac Electrophysiology
• Computational Modeling
• Cardiovascular Disease
• +5 more

Patrick M. Boyle is an Associate Professor of Bioengineering and Associate Chair of Operations at the University of Washington's College of Engineering. He leads the Cardiac Systems Simulation (CardSS) Lab, focusing on computational modeling of cardiac electrophysiology, machine learning for cardiovascular disease prediction, and optogenetics-enabled therapies. His affiliations include the Institute for Stem Cell and Regenerative Medicine, Center for Cardiovascular Biology, and eScience Institute. His research explores fibrotic substrates in arrhythmias, personalized 'virtual heart' simulations, and cardiomyocyte-like cells from stem cells. Education: Ph.D. in Biomedical Engineering, University of Calgary (2011); B.Sc. in Computer Engineering, University of Calgary (2005) Postdoc: Johns Hopkins University (2011-18) Boyle's work integrates machine learning , optogenetics , and multi-physics simulations to address arrhythmias, stroke, and regenerative medicine challenges. Recent projects analyze fibrosis-hemodynamics interactions and light-based defibrillation . Scientific awards include the American Heart Association Collaborative Sciences Award, NIH R01 funding for fibrosis-stroke mechanisms, and the Young Innovator in Cellular and Molecular Bioengineering award. He teaches BIOEN400 (Bioengineering Design) and BIOEN484/584 (Computational Bioelectricity).

• Knowledge Graphs
• Datalog
• Database Systems
• +4 more

Aleksandar Pavlović is a researcher at the Institute of Computer Sciences specializing in knowledge graph technologies and Datalog-based systems. His work bridges semantic web standards with machine learning methodologies to advance knowledge representation and query processing efficiency. Research interests focus on: Knowledge graph embedding techniques with spatial and geometric interpretations Datalog optimization for SPARQL query evaluation Integration of graph neural networks with symbolic reasoning systems Interoperability solutions for semantic web technologies Recent publications (2023-2024) demonstrate consistent innovation in knowledge graph completion and reasoning, featuring novel embedding architectures like ExpressivE and region-based neural networks. His work shows strong alignment with database community priorities while pushing boundaries in neural-symbolic integration. Collaborations span international institutions through co-authorships with researchers including Sallinger, Schockaert, Angles, and Gottlob. He actively contributes to academic discourse through conference presentations, including recent work on AI-supported language services for long-term care systems.

• X-ray Computed Tomography
• Non-destructive Testing
• Deep Learning
• +4 more

Patrick Weinberger is a Researcher at the University of Applied Sciences Upper Austria, affiliated with the School of Engineering and the Focal Area ICT - Information & Communications Technology. He leads and collaborates on multiple research initiatives including the VISION project on AI-driven non-destructive testing and HyperMAT for hyperspectral material characterization. His research focuses on advancing X-ray computed tomography analysis through deep learning and augmented reality visualization. Key areas include U-Net architectures for industrial inspection, explainable AI in non-destructive testing, and immersive material analysis systems. His work bridges computer vision with practical industrial applications in aerospace and automotive sectors. Weinberger actively contributes to 4 major research projects including VISION (2025-2028) developing AI systems for non-destructive testing, PEMOWE for energy transition testing networks, HyperMAT for hyperspectral material characterization, and WIFI for future welding industry systems. He operates within the Assistive Technology Lab and Center of Excellence Automotive/Mobility at the Wels campus. Research Output Highlights: 13 total publications (5 articles, 3 conference papers, 2 contributions) Key innovations in U-Net interpretability and AR-based CT visualization Strong focus on industrial AI transfer to manufacturing Technical Collaborations: Primary collaborator with Senck, Kastner, and Fröhler Network spans 6 similar research profiles in deep learning and CT analysis Active in EU Interreg Bavaria-Austria programs

• Bioprocess Engineering
• Gene Therapy Vectors
• Protein Production
• +4 more

Astrid Dürauer is a Privatdozent (Associate Professor) at the Institute of Bioprocess Science and Engineering within the University of Natural Resources and Life Sciences, Vienna (BOKU) . She holds leadership roles including Head of the Christian Doppler Laboratory for Knowledge-based Production of Gene Therapy Vectors and Deputy Head of the Department of Biotechnology . Her career spans over two decades in bioprocess development, with a focus on downstream processing, protein purification, and data-driven process modeling. PhD in Biotechnology (2001, BOKU Vienna) Habilitation in Bioprocess Engineering (2019, BOKU Vienna) Astrid's research centers on bioprocess optimization , particularly for gene therapy vectors and recombinant proteins . She pioneers the integration of machine learning and statistical models to enhance real-time monitoring and predictive analytics in downstream operations. Key projects include automation of process development, membrane fouling mitigation, and scalable chromatography systems. Her 15 most recent publications (2022–2025) highlight trends in explainable AI for biopharmaceutical processes, AAV vector production , HEK293 cell characterization , and continuous downstream processing . These works bridge biotechnology , data science , and industrial scalability . Scientific recognition includes: Head of Christian Doppler Laboratory (2023) Habilitation in Bioprocess Engineering (2019) She has led 7 funded projects (2014–2029) and served as Deputy Scientific Head for the BOKU Core Facility Biomolecular & Cellular Analysis (2020). Her work involves collaborations across statistics , molecular biotechnology , and industrial partners .