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Nicolás Camilo Zanetta ColomboView profile

Researcher

• Environmental justice
• Health disparities
• Geochemistry
• +7 more

Nicolás Camilo Zanetta Colombo is a Research Associate in the Department of Geography at Heidelberg University's South Asia Institute, where he has worked since 2022. His research bridges environmental health, geochemistry, and community-based monitoring to advance environmental justice in mining communities across the Global South. His educational background includes a Bachelor's and Master's degree in Geography from Pontificia Universidad Católica de Chile, with PhD completion in November 2024. His academic journey reflects a deep commitment to understanding human-environment interactions through interdisciplinary approaches. Zanetta-Colombo specializes in investigating how pollution and extractive industries contribute to health disparities, particularly among vulnerable and marginalized populations. His work integrates spatial analysis, toxicology, and critical geography to examine environmental pollution impacts in the Atacama Desert, where he has built long-term collaborations with Indigenous and urban communities affected by mining-related contamination. His decade-long research in this region is complemented by comparative projects in Central America and South Africa, bringing a global perspective to socio-environmental challenges. His research aims to make visible the often-overlooked health consequences of extractive industries and support pathways toward more just and sustainable futures through science outreach and community engagement. His publication record reveals a strong interdisciplinary focus on mining impacts in the Atacama Desert region, examining metal(loid) dispersion through multiple environmental media and temporal scales. His work uniquely combines contemporary environmental monitoring with long-term historical perspectives, using innovative methods like tree ring analysis and rodent middens to document pollution patterns over decades and even millennia. This approach bridges environmental science, public health, and social justice concerns, highlighting the connections between global resource demands and local community impacts. Dr. Zanetta-Colombo teaches courses in Political Ecology, Critical Physical Geography, and Geographies of Extraction, reflecting his commitment to interdisciplinary approaches that connect environmental processes with social justice concerns. His work emphasizes community involvement and science outreach as essential components of effective environmental research and solutions, particularly in contexts where extractive industries operate with limited accountability to affected communities.

• Causal Inference
• Machine Learning
• Natural Language Processing
• +3 more

Dhanya Sridhar is an Assistant Professor at the Department of Computer Science and Operations Research (DIRO) of the University of Montreal and a core academic member of Mila - Quebec Artificial Intelligence Institute. She holds a Canada CIFAR AI Chair and co-leads the IVADO R3AI working group on safe and aligned AI. PhD from University of California Santa Cruz Postdoctoral research at Columbia University Data Science Institute Her research focuses on integrating causality and machine learning to build AI systems that are robust to distribution shifts, capable of efficient task adaptation, and aligned with human knowledge. Recent work includes causal effects of social interactions on US election participation and causal modeling in geriatric-oncology patient outcomes. Current research themes: Causal representation learning Temporal causal inference Counterfactual modeling Causal abstraction in large models Responsible AI development Scientific awards: Canada CIFAR AI Chair Major grants: NSERC Discovery Grant (PVX20965-RGP) 2023-2029 NSERC DGECR Grant 2023-2025 Multiple MITACS Acceleration Quebec grants Advising: PhD students: Philippe Brouillard, Shruti Joshi, Mizu Nishikawa-Toomey, Tom Marty, Cristian Manta

Dominik DrexlerView profile

Research Fellow

• Artificial Intelligence
• Machine Learning
• Classical Planning
• +3 more

Dominik Drexler is a Research Fellow at Linköping University's Department of Computer and Information Science (IDA), part of the Artificial Intelligence and Integrated Computer Systems (AIICS) division. He completed his PhD at IDA in 2025, focusing on integrating learning and artificial intelligence planning to develop intelligent agents that exploit subgoal structures. His work bridges System 1 (learning-based) and System 2 (reasoning-based) approaches in AI, emphasizing hierarchical planning and policy reuse. He is a recipient of the 2024 Lawson Scholarship for excellence in international collaboration and the 2023 Optimal Planning Track Award at the International Planning Competition. **Education**: Bachelor's and Master's in Computer Science (specializing in AI) from the University of Freiburg, Germany. PhD in Computer Science at Linköping University (2025). **Research Focus**: Combining learning and classical planning to create agents that learn hierarchical policies and subgoal structures. Development of expressive policy languages and sketch-based methods for decomposing planning problems. Applications in autonomous systems and generalizable planning algorithms. **Contributions**: Open-source tools like DLPlan , Vanir , and Odin for state feature extraction and optimal planning. Active in international collaborations through the Wallenberg Autonomous Systems Program (WASP). **Teaching & Supervision**: Transitioning to expanded teaching and student supervision roles while continuing research. **Affiliations**: Member of the AIICS division and the Faculty of Science and Engineering at LiU.

Tim HulsenView profile

Professor

• Artificial Intelligence in Healthcare
• Data Science in Healthcare
• Generative AI
• +6 more

Dr. Tim Hulsen is a Professor of AI & Data-Supported Healthcare at the Knowledge Center for Healthcare Innovation, Rotterdam University of Applied Sciences, where he leads research on the application of data science and artificial intelligence in healthcare. He is also actively involved in the HR Datalab Healthcare and maintains a dual role as Senior Data & AI Scientist at Philips. His work bridges academic research and industry application, focusing on practical implementations of AI technologies in healthcare settings. Tim completed his Biology studies at Radboud University Nijmegen with a strong medical-biological component. After internships in Molecular Animal Physiology and Bioinformatics, he pursued a PhD and postdoctoral position at Radboud University Nijmegen in collaboration with NV Organon (later Schering-Plough). His academic journey transitioned into industry when he joined Philips Research in Eindhoven in 2009, where he has held various scientific positions focused on data management, big data, and artificial intelligence in healthcare. Dr. Hulsen's research focuses on the practical application of data science and AI in healthcare, emphasizing the importance of good data quality through sound data management practices, adherence to the FAIR Guiding Principles, and the use of ontologies and standards. He places particular emphasis on the explainability and responsible use of AI systems in clinical settings. His recent research interests have expanded to include generative AI (GenAI) applications in healthcare, where he seeks to connect various professorships and departments within the Rotterdam University of Applied Sciences with the broader medical technology sector. An analysis of Dr. Hulsen's recent publications reveals a strong focus on AI applications in healthcare, particularly in oncology and prostate cancer research. His work demonstrates a progression from foundational data management and bioinformatics to cutting-edge AI applications, with increasing emphasis on responsible implementation and practical healthcare solutions. The publications span technical aspects of data science, clinical applications, ethical considerations, and future directions for AI in healthcare. Dr. Hulsen has made significant contributions to the academic community through his editorial work, serving as an Editorial Board Member for BMC Cancer and Frontiers Medicine and Public Health, and regularly reviewing scientific publications. While specific awards aren't detailed in the available information, his extensive publication record (over 50 scientific publications) and leadership roles indicate recognition within his field. Throughout his career, Dr. Hulsen has demonstrated strong leadership in research projects, having led various initiatives, co-authored international project proposals, and managed work packages within grant projects. His collaborative approach is evident in his work connecting academic research with industry applications, particularly through his dual roles at Rotterdam University of Applied Sciences and Philips. His work with the Movember GAP3 consortium and PIONEER big data platform for prostate cancer demonstrates his commitment to large-scale collaborative research that addresses real-world healthcare challenges. Dr. Hulsen is actively involved with the HR Datalab Healthcare and the AI & Data-Supported Healthcare research group. He collaborates extensively with various stakeholders in the healthcare ecosystem, including medical institutions like Erasmus MC, technology companies, and research consortia. His current work focuses on practical applications such as registration burden reduction using generative AI, non-invasive monitoring, and optimization of diagnostics and prevention strategies. Through these initiatives, he aims to develop explainable knowledge that will train future healthcare professionals in the responsible use of AI.

• Computer Vision
• Natural Language Processing
• Multimodal Machine Learning
• +3 more

Dr. Josiah Wang is a Visiting Professor in the School of Computer Science at the University of Sheffield. His research focuses on multimodal machine learning, particularly the intersection of computer vision and natural language processing. He has contributed to advancements in image captioning, object detection, and multimodal machine translation. His work emphasizes techniques like semi-supervised learning, distributional similarity, and visual-linguistic integration. Wang's research has been showcased in venues such as the ImageCLEF challenges, where he contributed to scalable concept annotation tasks and multimodal translation systems. His projects often address critical issues in content selection for image descriptions and the exploitation of visual semantics for improved language generation. While no specific awards or grants are listed, his involvement in organizing and participating in ImageCLEF tasks demonstrates his leadership in collaborative research initiatives. His work bridges theoretical machine learning with practical applications in visual language systems, reflecting a commitment to advancing both technical and interdisciplinary research.

• Human-Computer Interaction (HCI)
• User Experience (UX)/Usability
• Domain Analysis
• +3 more

Michaela Bačíková is an Assistant Professor at the Faculty of Electrical Engineering and Informatics (FEI) of the Technical University of Košice (TUKE). Her research focuses on Human-Computer Interaction (HCI), domain usability, and domain analysis, with an emphasis on graphical user interfaces (GUIs), domain-specific languages (DSLs), and gesture-driven interaction. She leads the development of the DEAL tool, a domain analysis framework for extracting domain models from software systems. Her teaching includes courses on component-based programming, web technologies, and user interface design. Research Projects: DEAL (Domain Extraction ALgorithm) : A tool for analyzing GUIs to generate DSLs, ontologies, and usability metrics. EU Project: 'Evolving Architectural Knowledge in the Edge-to-Cloud Continuum' (participant). Educational initiatives: Integrating gesture-driven IDEs and social networks for mentoring in programming courses. Research Interests : Automated domain usability evaluation using DEAL. DSL-driven GUI generation and feature modeling. Innovations in teaching software development and user experience design. Grants & Labs : Recipient of FEI TUKE Grant no. FEI-2015-16 for domain usability metrics research. Active in the FEI lab developing DEAL and related tools.

Katherine FuView profile

Associate Professor

• CAE and Design Engineering
• Design Theory and Methodology
• Computational Design Tools
• +6 more

Katherine Fu is an Associate Professor of Mechanical Engineering at the University of Wisconsin-Madison and holds an adjunct role as Adjunct Associate Professor at the Georgia Institute of Technology's Woodruff School of Mechanical Engineering. Her research focuses on design cognition, computational design tools, and fostering innovation through analogy-based methods. She has a Ph.D. in Mechanical Engineering from Carnegie Mellon University (2012), joined Georgia Tech in 2014, and moved to UW-Madison in 2021. Education: Ph.D., Mechanical Engineering, Carnegie Mellon University, 2012 M.S., Mechanical Engineering, Carnegie Mellon University, 2009 B.S., Mechanical Engineering, Brown University, 2007 Research interests emphasize cognitive studies of design processes, computational tools for creativity, and design-by-analogy methodologies. Notable contributions include frameworks for design heuristics extraction, functional analogy search algorithms, and studies on analogical distance effects. Awards include the 2019 GT LGBTQIA Leadership Award, 2018 ASME Reviewer of the Year, and 2015 ASME Early Career Engineer of the Year. Her work bridges engineering education and design science, with a focus on fostering ethical and socially impactful innovation. Labs and Teams: Active leadership in the Design Research Lab (EDRL) and collaborations with institutions like Jet Propulsion Laboratory (JPL). Teaching contributions include curriculum development in GD&T and ethics integration in design education.

Susanne WenzelView profile

Research Fellow

• Artificial Intelligence
• Remote Sensing
• Machine Learning
• +3 more

Dr. Susanne Wenzel is a Research Fellow at the Structural and Functional Organisation of the Brain (INM-1) within the Forschungszentrum Jülich GmbH. She serves as the Scientific Coordinator for the Helmholtz AI Local Unit at INM-1 and the Jülich Supercomputing Centre (JSC), fostering collaboration with the Helmholtz-wide AI platform. Additionally, she leads the Helmholtz International BigBrain Analytics and Learning Laboratory (HIBALL), a partnership with McGill University’s Montreal Neurological Institute and the Mila Quebec AI Institute. Her work focuses on advancing AI and high-performance computing for neuroimaging, particularly in developing 3D brain models through interdisciplinary research with CIFAR and MILA. Her research expertise spans artificial intelligence, remote sensing, and image analysis, with a strong emphasis on machine learning applications in environmental science, neuroscience, and urban scene understanding. She coordinates projects that integrate cutting-edge technologies to address complex challenges in climate modeling, neuroimaging, and spatial data analysis. Dr. Wenzel’s contributions include pioneering work in neural networks for ocean eddy tracking, sea level anomaly prediction, and facade image interpretation. Her projects emphasize interdisciplinary collaboration, combining computational methods with domain-specific knowledge across multiple institutions globally. Her current initiatives reinforce the utilization of AI and supercomputing resources to enhance scientific discovery, particularly in neuroscience through HIBALL. This collaboration aims to leverage advanced analytics for detailed brain model development, reflecting her commitment to bridging computational innovation with real-world applications.

• Algebraic Geometry
• Low-Dimensional Topology
• Machine Learning
• +3 more

Benjamin Ruppik is a researcher at the Chair of Algebraic Geometry within the Faculty of Mathematics and Natural Sciences at Heinrich-Heine-Universität Düsseldorf. His work bridges pure mathematics and applied machine learning, focusing on topology-driven approaches to computational problems. He holds a PhD (2022) and a Master's in Mathematics (2018), with expertise in low-dimensional topology and its intersections with natural language processing and dialogue systems. Research Interests Algebraic Geometry 4-Manifold Topology and Homotopy Classification Applications of Topology in Machine Learning Dialogue Systems and Emotion Recognition Active Learning and Label Correction Recent Research Trends Ruppik's recent articles emphasize integrating topological methods into machine learning frameworks, particularly in analyzing latent spaces of language models and enhancing dialogue systems with emotion-aware components. His work on 4-manifold classification demonstrates foundational contributions to geometric topology. Awards and Grants No specific awards or grants mentioned in the provided text. Labs and Collaborations Part of the Chair of Algebraic Geometry at HHU, collaborating on projects merging pure mathematics with computational applications.

• Power Electronics
• Electromagnetic Modeling
• Wide Bandgap Semiconductors
• +3 more

Ivana Kovacevic-Badstübner serves as a Lecturer at ETH Zurich's Department of Information Technology and Electrical Engineering, working within the Chair of Power Semiconductors under Prof. Ulrike Grossner. Her research focuses on advanced electromagnetic modeling techniques for power semiconductor devices and modules, with particular emphasis on Wide Bandgap (WBG) technologies. Her research interests span electromagnetic modeling of power electronics systems, multiphysics simulation of semiconductor devices, Wide Bandgap semiconductor applications, EMI filter design and analysis, and power module reliability. She has made significant contributions to the understanding of parasitic effects in power modules and the development of virtual prototyping methodologies for power electronics design. Kovacevic-Badstübner's publication record demonstrates a strong focus on practical engineering solutions for power electronics challenges, with numerous contributions to IEEE journals and international conferences. Her work shows consistent progression from fundamental electromagnetic modeling techniques to applied research in aircraft power systems and reliability prediction. Her scientific contributions primarily appear in the fields of power electronics and electromagnetic compatibility, with a particular emphasis on the design and analysis of power semiconductor modules using Silicon Carbide technology. Her research has addressed critical challenges in thermal management, parasitic extraction, and reliability prediction for next-generation power electronic systems. As a lecturer at one of the world's leading technical universities, she contributes to the education of future engineers in power electronics and semiconductor technology, bridging theoretical knowledge with practical industry applications.

Monika BlattmeierView profile

Professor

• Business Process Management
• Knowledge Visualization
• Pattern Languages
• +4 more

Monika Blattmeier is a Professor in the Department of Mechanical Engineering at the University of Applied Sciences Emden/Leer. Her work bridges business process management with mechanical engineering, focusing on knowledge visualization and pattern-driven process design. Her research interests include knowledge management in business processes, aesthetic design in organizational structures, process modeling using pattern languages, and cultural influences on international business processes. She leads projects like ProlOg (NBank, ESF+) and Visual GP (Interreg). Recent publications highlight trends in business process visualization, pattern recognition, and aesthetic design. Older articles explore materials science topics like laser sintering and fatigue behavior of plastics in automotive manufacturing. Monika is involved in research-based teaching and learning, emphasizing pattern languages. She collaborates with institutions through Erasmus+ and contributes to academic discourse via journals like the Swiss Journal of Business Research and Practice and the Journal of Information Technology. She oversees the Denkraum (Knowledge Visualization Think Tank) and maintains an active presence on LinkedIn.

Markus WenzelView profile

Associate Professor

• Medical Imaging
• Deep Learning
• Healthcare AI
• +3 more

Prof. Markus Wenzel is an Associate Professor of Medical Cognitive Computing at the School of Computer Science & Engineering, Constructor University Bremen. His research focuses on interpretable deep learning and computer-aided diagnosis systems for healthcare applications, particularly in medical imaging. He holds a PhD in 'Complexity Reduction in Image-Based Breast Care' (2014) from University Bremen and a BSc in Computer Science (With Distinction) from the Distance Learning University, Hagen. Wenzel has extensive industry experience, including roles at Fraunhofer MEVIS as a Key Scientist in Computational Breast Care (since 2021) and Senior Scientist (2005–2021). His work emphasizes clinical practicality, such as breast MRI segmentation and AI-driven diagnostic tools. Research highlights include developing the QuantMed platform for translational research and exploring generative adversarial networks (GANs) for medical data synthesis. He has authored over 50 peer-reviewed publications, with recent work focusing on CNN-based lesion detection, swarm learning, and uncertainty quantification in medical imaging. Awards include a prestigious scholarship from the Studienstiftung des Deutschen Volkes and certification as a Scientific Trainer (2022). Wenzel collaborates with clinicians to bridge AI research and clinical practice, addressing challenges like model interpretability and data scarcity in healthcare. His contributions span medical imaging analysis, AI-driven decision support systems, and robust deep learning frameworks for heterogeneous medical data.

Ira AssentView profile

Professor

• Machine Learning
• Data Mining
• Clustering Algorithms
• +5 more

Professor Ira Assent is affiliated with the Department of Computer Science at Aarhus University. Their research focuses on machine learning, data mining, and visualization, with applications in climate science, medical informatics, and computer vision. Professor Assent leads projects such as Light-IoT (analytics on compressed IoT data), WallViz (interactive visualization for massive datasets), and eData (anomaly detection in e-science). Their work emphasizes scalable algorithms, explainable AI, and interdisciplinary applications. Recent publications address rainfall prediction using deep learning, entity summarization via knowledge graphs, and efficient clustering techniques. Projects like RainAI demonstrate contributions to weather modeling and satellite data analysis. Collaborative efforts span academic and industrial domains, with a strong emphasis on practical, user-centric solutions. Selected research contributions include advancements in density-based clustering (e.g., AnyDBC, DISCO), parallel algorithms optimized for GPUs (HUNIPU), and visualization frameworks (AVID). Their work bridges theoretical computer science with real-world challenges, such as improving decision-making through interactive visualizations and enhancing medical information retrieval systems. Ongoing projects aim to address computational efficiency in large-scale data analytics while maintaining interpretability. Key areas of innovation include explainable AI (e.g., InteDisUX), climate modeling (DROPP), and hardware-accelerated algorithms (GPU-FAST-PROCLUS). These efforts reflect a commitment to advancing both foundational methods and applied technologies that impact diverse fields from environmental science to healthcare.

Tony BirdView profile

Professor

• Gamma-ray astronomy
• Instrumentation for astrophysics
• Data analysis techniques
• +4 more

Professor Tony Bird is a member of the astronomy group within the School of Physics and Astronomy at the University of Southampton. He currently serves as Deputy Head of School (Education) and previously held roles as Director of Programmes for Physics and Astronomy. His research focuses on instrumentation and data analysis for high-energy astrophysics, particularly through his role as a co-investigator on the ESA's INTEGRAL mission's IBIS telescope. He leads the compilation of its all-sky survey catalogues and explores applications of astronomical technology in medical imaging and security systems. Research Projects: - Developed novel instrumental techniques for coded-aperture imaging. - Led Knowledge Transfer Partnerships with Symetrica for security tech. - Co-led SPRINT programs advancing space technology innovation. - Active in next-generation space telescope design initiatives. Teaching: Teaches Medical Physics and Applied Nuclear Physics courses, and oversees mission design components of the Tenerife Field Trip for astronomy students. Supervises multiple PhD students in physics and related fields. Labs/Teams: Part of the Southampton Theory Astrophysics and Gravity (STAG) Research Centre and collaborates with interdisciplinary teams on detector development and data analysis challenges.

• Software Engineering
• Software Evolution
• Knowledge Worker Productivity
• +4 more

Gail Murphy is a Professor in the Department of Computer Science and Vice-President Research & Innovation at the University of British Columbia (UBC). A co-founder of Tasktop Technologies Incorporated, she leads the Software Practices Lab and is affiliated with CAIDA (Centre for Artificial Intelligence Decision-making and Action) and ICICS (UBC Institute for Computing, Information and Cognitive Systems). B.Sc. (Honours), University of Alberta (1987) M.Sc., University of Washington (1994) Ph.D., University of Washington (1996) Her research focuses on software engineering , particularly improving knowledge worker productivity and tools for evolving large-scale systems. She combines lightweight design approaches with industrial evaluations, addressing structural degradation in software systems through: Techniques for understanding/imposing structure in existing systems Design approaches for component reuse and flexible architectures Her recent work includes developer discussion analysis for design recovery (2022), semantic-based task relevance detection (2022), and automated resource-task association (2023). Awards include SIGSOFT Outstanding Research Award (2023), ACM Fellow (2023), and Royal Society of Canada Fellow (2023). She has taught courses like Introduction to Software Engineering (CPSC 310) and Software Construction (CPSC 210).