جستجوی استاد راهنمای دانشگاهی

• Reservoir Computing
• Machine Learning
• Embedded Systems Design
• +۴ مورد دیگر

Jonas Kantic is a Ph.D. student and Researcher at the Chair of Integrated Systems within the Faculty of Electrical Engineering and Information Technology at Technical University of Munich . Holding a Master of Science in Technical Informatics from Leibniz University Hannover, his work focuses on AI acceleration architectures and embedded systems design. Education Master's in Technical Informatics (2017-2020), Leibniz University Hannover Chinese Language Studies (2018-2019), Beijing Foreign Studies University Bachelor's in Technical Informatics (2013-2017), Leibniz University Hannover Research Focus Specializes in reservoir computing architectures Expertise in FPGA-based AI acceleration Investigates temporal/spatial compression techniques Develops efficient edge AI inference systems Applies machine learning to motorcycle control systems Works on hyperdimensional computing models Supervision Mentored 5+ students in RNN accelerators, CNN optimization, and stochastic computing Collaborates with industry partners (BMW Motorrad, NXP Semiconductors) Publications 2024 - Complex & Intelligent Systems: Cellular Automata for Reservoir Computing 2024 - IEEE NorCAS: FPGA Implementation for High-Speed Reservoir Models 2021 - Current Directions in Biomedical Engineering: Hearing Aid CNN Optimization

Ke Thia Yaoمشاهده پروفایل

استاد پژوهش

• Artificial Intelligence
• Machine Learning
• Quantum Computing
• +۳ مورد دیگر

Ke-Thia Yao is a Research Professor at the University of Southern California's Information Sciences Institute (ISI), where he leads initiatives in the Artificial Intelligence Division. With a Ph.D. in Computer Science from Rutgers University and a B.S. in Electrical Engineering and Computer Science from UC Berkeley, his career spans 25+ years in research and development across artificial intelligence, machine learning, and multidisciplinary engineering design. B.S., Electrical Engineering and Computer Science, UC Berkeley (with honors) M.S. and Ph.D., Computer Science, Rutgers University His research focuses on leveraging AI and machine learning for predictive modeling in engineering systems, quantum computing applications for neural networks, and data-driven approaches in environmental and medical contexts. He has authored influential work on knowledge graph augmentation, simulation data analysis, and automated machine learning pipelines. Recent trends in his publications highlight interdisciplinary applications, including quantum-assisted Boltzmann machines for materials science, environmental toxicology studies linking PM2.5 exposure to corneal diseases, and scalable cyber warfare test environments. His work bridges high-performance computing, neuromorphic systems, and quantum architectures for advanced AI solutions. Ke-Thia Yao is affiliated with the University of Southern California's Information Sciences Institute, collaborating with experts in computer science, quantum computing, and environmental health. His projects emphasize scalable simulation frameworks, failure prediction systems, and semantic interoperability in data management.

• Protein Structure Prediction
• Protein Interactions
• Drug Design
• +۴ مورد دیگر

Yuedong Yang is a Professor at the School of Data and Computer Science, Sun Yat-sen University since August 2017. His academic journey includes a Research Assistant Professor position at Indiana University (2011-2013) and Research Fellow at Griffith University's Institute for Glycomics (2013-2017), following postdoctoral training at Indiana University School of Medicine (2006-2011). His primary research focuses on Protein Structure Prediction and Refinement , Protein Interactions with biomolecules and ligands, and Classification of Human Genetic Variations . His work bridges computational biology, machine learning, and structural bioinformatics, with emphasis on developing AI-driven methods for biomolecular analysis. Recent publications demonstrate strong integration of geometric deep learning, language models, and physics-based approaches. Analysis of his 15 most recent publications (2024-2025) reveals dominant trends in spatial transcriptomics , protein structure-function prediction , foundation models for genomics , and interpretable AI for drug discovery . His work increasingly combines multimodal data integration with causal learning frameworks. Over 50 publications in top journals including PNAS, Genome Biology, Nucleic Acids Research, and Bioinformatics Active community service as Associate Editor for BMC Bioinformatics Extensive peer review activity across 46 publications/grants in 35+ journals His research is supported by multiple major grants including National Natural Science Foundation of China (61772566), National Health and Medical Research Council (1121629), and Australian Research Council (LP150100137). He has advised numerous researchers through collaborative projects in computational biology and bioinformatics. Dr. Yang leads research in protein informatics and multi-omics integration, with current projects focusing on geometric deep learning for biomolecular structure prediction and foundation models for single-cell genomics. His lab maintains strong international collaborations between China, USA, and Australia.

• Ecology
• Conservation Biology
• Biogeography
• +۴ مورد دیگر

Michelle Lawing is an Associate Professor in the Ecology and Conservation Biology department at Texas A&M University, where she has been employed since 2014. She initially served as an Assistant Professor in Ecosystem Science and Management from 2014 to 2020 before being promoted to Associate Professor in her current department. Her academic journey began with a PhD in Ecology, Evolution, and Behavior; Geological Sciences from Indiana University Bloomington. Dr. Lawing's research spans multiple disciplines within ecology and evolutionary biology, with particular emphasis on conservation biology, biogeography, and paleontology. Her work integrates historical ecological patterns with contemporary conservation challenges, examining how species and ecosystems respond to environmental changes over time. She has made significant contributions to understanding the impacts of climate change on biodiversity, evolutionary adaptations in reptiles and mammals, and the application of ecometric models for conservation planning. Her publication record demonstrates consistent productivity across multiple high-impact journals including Nature Communications, Science, Proceedings of the National Academy of Sciences, and various ecology and herpetology journals. Her recent work shows a strong trend toward interdisciplinary research that bridges paleontology with conservation biology, examining both historical patterns and contemporary applications for species preservation. She frequently collaborates with researchers across institutions, demonstrating engagement with the broader scientific community. Dr. Lawing has contributed to important initiatives in science education, particularly through biological field stations, and has served as a peer reviewer for journals including Biological Invasions and Communications Biology. Her research program integrates field studies, morphometric analyses, and computational modeling to address fundamental questions in evolutionary ecology and conservation science.

Frank Bremmerمشاهده پروفایل

استاد

• Visual Perception
• Self-Motion Encoding
• Multisensory Integration
• +۵ مورد دیگر

Frank Bremmer is a Professor at the Neurophysics Group , Philipps University of Marburg . His research focuses on visual perception during eye movements and the multisensory encoding of self-movement information in primates , aiming to understand neural representations of space and movement in the human brain. The group combines neurophysiological recordings from animal models with behavioral and neuroimaging studies in humans, including collaborations with clinics at the University Hospital of Munich (UKGM) to investigate spatial and motion processing disorders in neurological and psychiatric patients.

Mario Botschمشاهده پروفایل

استاد

• Computer Graphics
• Geometric Modeling
• Virtual Reality
• +۷ مورد دیگر

Mario Botsch is a Professor in the Department of Computer Science at the Faculty of Technology, University of Bielefeld. His research focuses on computer graphics and geometry processing, with major contributions to polygonal mesh modeling, virtual reality, and GPU-accelerated rendering techniques. Research Interests: Immersive data visualization in 3D knowledge spaces Virtual reality for historical relationship discovery Point-based rendering and surface splatting GPU-accelerated geometric modeling Mesh processing and multiresolution modeling Surface reconstruction and anti-aliasing Scientific Contributions: Developed Phong splatting techniques for high-quality rendering Created multiresolution frameworks using displacement volumes Innovated feature-sensitive sampling for mesh optimization Pioneered GPU-based tolerance volume analysis for error control Established OpenMesh data structure for polygonal mesh handling

• 3D Point Cloud Segmentation
• Virtual Reality
• Cultural Heritage Digitization
• +۳ مورد دیگر

Florent Poux is a researcher affiliated with the University of Liège (email: fpoux@uliege.be). His work focuses on 3D point cloud segmentation, virtual reality heritage systems, and unsupervised clustering algorithms. Developed fully automated region-growing systems for large point cloud segmentation (2022) Contributed to object-based classification methodology for indoor 3D models (2020) Designed user-centric VR applications for cultural heritage preservation (2020) His research combines computer science and spatial information systems, with applications in building modeling, heritage conservation, and digital tourism. He has created open-access datasets containing over 300 million labeled 3D points for testing segmentation algorithms.

• Urban hydrology
• Water resources management
• Human-environment interactions
• +۵ مورد دیگر

Dr. Klaus Baier serves as a Senior Scientist at the Department of Engineering Geology and Hydrogeology within the Faculty of Civil Engineering at RWTH Aachen University. His research focuses on human-environment interactions, particularly examining urban spaces (especially megacities) and water resources in Asia. He has led multiple significant research projects funded by DFG and DAAD, collaborating with institutions in Germany, India, and China. PhD in Economic Geography, RWTH Aachen University (2001-2005) M.A. in Economic Geography, Geography, and Psychology, RWTH Aachen University (1993-2000) Dr. Baier's research centers on the complex relationships between urban development and water resources, with particular emphasis on groundwater systems in rapidly growing Asian cities. His work integrates geographical information systems (GIS), agent-based modeling, and field studies to analyze how informal urban development processes impact water management. He has conducted extensive research in Hyderabad (India) and the Pearl River Delta (China), examining how land use changes affect water quality, groundwater recharge, and urban water balance. His methodological approach combines quantitative spatial analysis with socio-ecological vulnerability assessments. Analysis of Dr. Baier's publication record reveals a consistent research trajectory focusing on urban hydrology in developing regions. His work demonstrates increasing sophistication in modeling approaches, moving from basic GIS applications to integrated hydrodynamic and agent-based models. The geographic focus has primarily centered on South and East Asian megacities, with particular attention to informal settlement dynamics and their water implications. Recent publications show greater integration of climate change considerations into urban water management frameworks. Dr. Baier has supervised over 30 MSc theses and approximately 10 BSc theses since 2011, demonstrating his commitment to academic mentoring. His research has been supported by significant funding from the German Research Foundation (DFG) and the German Academic Exchange Service (DAAD), including the Priority Program 1233 'Megacities - Megachallenge: Informal Dynamics of Global Change' (2007-2011) and the DAAD project 'Interaction between Urbanization and Water Management' (2010-2015). His editorial work includes serving as editor for special issues on water management in megacities and co-editing proceedings from international conferences focused on urban land use and water management. Dr. Baier has also contributed to interdisciplinary educational initiatives, including an environmental science distance learning course at the University of Hagen.

Andreas Gutمشاهده پروفایل

استاد

• Child and Youth Welfare
• Family Therapy in Social Work
• Systemic Therapy and Counseling
• +۵ مورد دیگر

Prof. Dr. Andreas Gut serves as Professor of Social Work and Head of the Social Work Youth, Family and Social Welfare Programme at Baden-Wuerttemberg Cooperative State University's Faculty of Social Work in Villingen-Schwenningen. His leadership focuses on integrating practice-based learning with theoretical rigor in child and youth welfare systems. His educational trajectory includes: Doctorate in Philosophy (University of Siegen, 2014) on lifeworld orientation in family therapy Systemic couple/family therapist certification (DGSF, 2001-2004) Diploma in Social Pedagogy (Catholic University of Applied Sciences Freiburg, 1989-1994) Theological studies (Universities of Freiburg/Tübingen, 1987-1996) Research centers on systemic approaches to child welfare with specialization in outreach family support, foster care transitions, and trauma-informed practice. His work bridges clinical therapy and social work through lebensweltorientierung (life-world orientation), emphasizing contextual understanding of family dynamics. Key contributions address care leaver professional development and parental support in foster systems. Recent publications (2022-2025) demonstrate evolving focus from methodological frameworks (2022 outreach family support compendium) toward specialized applications in grief counseling (2025) and ethical practice dilemmas. The corpus reveals consistent engagement with systemic therapy integration, rural service challenges, and quality development in mobile interventions. Professional involvement includes active membership in the German Society for Systemic Therapy (DGSF) and extensive field experience as social-pedagogical consultant for foster care (2007-2021). His practice background informs curriculum development in reflection methods and individual assistance techniques. Current work emphasizes developing practice-theory seminars for youth/family welfare with focus on trauma education and foster family support systems. The outreach family support research cluster represents his primary scholarly contribution to the field.

Georgios Priniotakisمشاهده پروفایل

استاد

• Smart Textiles
• Textile Sensors
• Sustainable Technologies
• +۵ مورد دیگر

Prof. Georgios Priniotakis is a Full Professor at the University of West Attica (UNIWA), specializing in Innovative Textile Technologies for Multifunctional Clothing Products . He serves as Head of the Department of Industrial Design and Production Engineering and represents Greece in international research networks like AUTEX and COST Action. His work bridges smart textiles, environmental sustainability, and occupational safety. Education: M.Sc. in Fiber Science and Technology (1992), University of Leeds Ph.D. in Conductive Textiles (2005), Ghent University Research Interests span smart clothing for medical and military applications, conductive textile sensors, sustainable materials from waste, and ergonomic design. He focuses on systems modeling, wearable health monitoring, and integrating AI into textiles. Article Trends highlight advancements in smart textile sensors , sustainable design , machine learning for disaster response, and biomimetic approaches to eco-design. His work intersects textile engineering with environmental science and medical technology . Scientific Leadership: AUTEX Lifetime Achievement Award 2025 Coordinator for EU-funded projects (e.g., DISOLEA, SECOVE, REWAFT) Grants & Projects include EU programs like Erasmus+, COST, and Horizon 2020, focusing on smart textiles, education innovation, and disaster management. He has over 350 citations and an h-index of 12 (Google Scholar).

Andre Borrmannمشاهده پروفایل

استاد

• Building Information Modeling
• Point Cloud Engineering
• Digital Twin
• +۴ مورد دیگر

Andre Borrmann is Professor and Chair of Computing in Civil and Building Engineering at the Technical University of Munich (TUM), where he leads cutting-edge research at the intersection of computer science and construction engineering. His work spans over two decades with consistent publication output since 2003, demonstrating sustained academic leadership in computational methods for the built environment. His research focuses on: Building Information Modeling (BIM) and its advanced applications Digital Twin development for infrastructure management Point cloud processing and semantic enrichment Artificial intelligence integration in construction workflows Construction automation and robotics systems Analysis of his 2023-2025 publications shows a clear trend toward sophisticated AI applications in construction engineering, with increasing emphasis on graph neural networks, transformer models, and large language models. His work bridges theoretical computer science with practical construction challenges, particularly in model automation, code compliance checking, and digital documentation of existing structures. Scientific recognition includes: Konrad Zuse Medal (2024) Professor Borrmann leads multiple significant research initiatives including AM2PM (Additive to Predictive Manufacturing for Multistorey Construction), AI4CADCAM (AI-based CAD processing), and BauPuls360 (a public service platform for the construction industry). His research group maintains extensive international collaborations across academia and industry, contributing to UN Sustainable Development Goals related to sustainable cities, industry innovation, and climate action through their technological advancements in construction engineering.

Medhini Heeramagloreمشاهده پروفایل

پژوهشگر

• 3D City Models
• CityGML
• 3D GIS
• +۴ مورد دیگر

Medhini Heeramaglore, M.Tech. (Geomatics), is a researcher at the Chair of Geoinformatics, Technische Universität München (TUM). Her work focuses on 3D city models, CityGML, and the integration of 3D GIS with urban studies and architectural design. She contributes to teaching courses such as Geospatial Information Science and Applied Geoinformatics II. Research: 3D city models, semantic modeling, urban resilience, sustainability Teaching: Geospatial Information Science, Spatial and Semantic Modelling, Applied Geoinformatics II Heeramaglore is involved in the AI4TWINNING project (since 2021), exploring AI-driven digital twins. Her recent publications address semantic interoperability and data consistency in 3D building models. Contact: medhini.heeramaglore@tum.de

Lea Friedliمشاهده پروفایل

پژوهشگر

• Bayesian Methods
• Inverse Problems
• Value of Information
• +۷ مورد دیگر

Lea Friedli is a Postdoctoral Researcher at the Engineering Risk Analysis Group of the Technical University of Munich (TUM) since March 2025, funded by the Swiss National Science Foundation. She previously served as a Postdoctoral Researcher at the University of Bern's Institute of Mathematical Statistics and Actuarial Science in 2024. PhD in Environmental Sciences (2019–2023), University of Lausanne MSc in Statistics (2016–2019), University of Bern BA in German Language and Literature (2013–2016), University of Bern Her research focuses on Bayesian methods , inverse problems , and rare event probability estimation , with applications in environmental systems and risk analysis. Key methodologies include Gaussian process modeling, sequential design, and pseudo-marginal inference. Lea's publications demonstrate expertise in uncertainty quantification for geophysical and hydrological systems. Recent work includes energy-based rare event estimation (2023), groundwater inverse problems (2024), and lithological tomography using Bayesian frameworks (2022). Teaching: Estimation of rare events (TA, SS25, TUM); Combinatorics and Probability (Lecturer, HS24, University of Bern); Uncertainty quantification in action (Lecturer, HS24, University of Bern) She is affiliated with the TUM Engineering Risk Analysis Group, contributing to projects involving digital twins, wildfire risk, and probabilistic ground models for offshore wind farms. No scientific awards were mentioned in the provided text.

Thomas Waltherمشاهده پروفایل

پژوهشگر ارشد

• Computational Neuroscience
• Spatial Navigation
• Memory and Learning
• +۵ مورد دیگر

Dr. Thomas Walther is a postdoctoral researcher at the Institute of Neuroinformatics (INI), Faculty of Computer Science, Ruhr University Bochum. His work centers on computational neuroscience with a focus on modeling cognitive processes in mammals, particularly spatial navigation, learning, and memory extinction through computational approaches. His educational background includes: Diploma in Electrical Engineering and Information Technology from the University of Dortmund, with thesis research on medical image interpretation, tumor tracking, and robot-assisted radiotherapy Ph.D. in Electrical Engineering and Information Technology from Ruhr University Bochum, dissertation on autonomous visual model learning based on Organic Computing principles Dr. Walther's research spans computational neuroscience, virtual reality systems, robotics, computer vision, computational auditory scene analysis, and Organic Computing. He investigates hippocampal circuit mechanisms underlying spatial cognition and memory processes, employing deep reinforcement learning and computational modeling to bridge neural mechanisms with behavioral outcomes. His work integrates biological plausibility with artificial intelligence techniques to unravel complex cognitive functions. His publication record shows consistent focus on applying reinforcement learning to neuroscience questions, particularly spatial navigation and extinction learning phenomena, while maintaining strong contributions to computer vision (human body modeling) and robotics (RatSLAM optimization). This interdisciplinary trajectory demonstrates evolving integration between machine learning methodologies and neurobiological inquiry. Scientific awards: None mentioned in available information. Dr. Walther has supervised multiple Master's theses including research on deep reinforcement learning algorithms across environments, virtual reality applications, context representation in learning systems, and transfer of learned associations. His advising emphasizes computational approaches to cognitive questions within the INI's collaborative framework. As a core member of the Computational Neuroscience group at INI, he contributes to the institute's mission of understanding natural cognitive systems through experimental psychology, neurophysiology, and AI to develop novel solutions for artificial cognitive systems within interdisciplinary collaborations.

Jakub Nowosadمشاهده پروفایل

دانشیار

• Remote Sensing
• Spatial Data Analysis
• Landscape Ecology
• +۳ مورد دیگر

Dr. Jakub Nowosad is an Associate Professor at Adam Mickiewicz University in Poznan, Poland, with a strong affiliation to the Institute of Landscape Ecology at the University of Münster, Germany. As a computational geographer, he specializes in spatial pattern analysis, remote sensing, and geospatial software development. Primary Affiliation: Adam Mickiewicz University, Poznan, Poland Secondary Affiliation: University of Münster, Faculty of Geosciences His research spans spatial data science , focusing on machine learning applications in geography, landscape metrics , and open-source geocomputation . He actively develops and maintains R packages for spatial analysis, including tools for landscape pattern quantification and colorblind-accessible visualization . Recent publications highlight his work on spatial autocorrelation , remote sensing data analysis , and computational ecology . He co-authored key resources such as Geocomputation with R and Geocomputation with Python , emphasizing reproducible workflows. Dr. Nowosad contributes to spatial data education as a co-author of open-source books and instructor at summer schools like OpenGeoHub and EON. He organizes workshops on spatial machine learning and thematic mapping , often involving collaborative R package development. Email: jakub.nowosad@uni-muenster.de