جست‌وجوی استاد و پژوهشگر

Mohsen Lesaniمشاهده پروفایل

دانشیار

• Reliability and Security of Software Systems
• Concurrent and Distributed Systems
• Secure Replicated Systems
• +۴ مورد دیگر

Mohsen Lesani is an Associate Professor at the Computer Science and Engineering Department of University of California, Santa Cruz . He obtained his PhD from UCLA , MS in Artificial Intelligence from Sharif University of Technology , and BS in Software Engineering from University of Tehran . His research focuses on reliability and security of software systems , particularly concurrent and distributed systems , with recent work on secure replicated systems and distributed machine learning . NSF CAREER Award (2020) DARPA Young Faculty Award (2022) SIGPLAN Research Highlight (2019) Distinguished Paper Award at OOPSLA 2018 Best Paper Award at ISSRE 2015 His recent publications tackle challenges in automated synthesis of distributed protocols , heterogeneous replication , secure blockchain transactions , and verified RDMA-based data types . He advises PhD students Xiao Li , Eric Chan , Javad Saber-Latibari , and Tejas Mane in the Safe and Secure Software (S3) lab . He has taught courses on Distributed Systems , Parallel Programming , and Compiler Design .

Tobias Ullmannمشاهده پروفایل

استاد

• Remote Sensing
• Earth Observation
• Climate Change Impact Analysis
• +۶ مورد دیگر

Prof. Dr. Tobias Ullmann has served as Full University Professor (W2) for Remote Sensing in Geography at the Department of Remote Sensing, University of Würzburg since October 2022. His research integrates Earth observation archives with physical geographic investigations, focusing on polar, semiarid, and arid regions using SAR data, multispectral optical satellite imagery, and digital terrain models. Key projects include climate change impact assessment, interdisciplinary cooperation with biology/ecology fields, and UAV-based environmental data collection. PhD from University of Würzburg (2011-2015) Habilitation at University of Würzburg (2021) Research interests span: Multi-sensor approaches for tropical climate-landscape interactions (Pol)(In)SAR classification/change detection techniques Periglacial dynamics in alpine/polar regions Machine learning and cloud processing applications Recent publications highlight: 2025 work on landscape-scale SAR phenology analysis 2024 NDVI time series for East African crop systems 2023 Arctic snowmelt and wildfire modeling Current affiliations: Chair of Remote Sensing, University of Würzburg Earth Observation Research Cluster Faculty of Philosophy, Institute of Geography and Geology

Michał Sanockiمشاهده پروفایل

پژوهشگر

• Machine Learning
• Graph Neural Networks
• Computational Chemistry
• +۳ مورد دیگر

Michał Sanocki is a researcher in the Department of Mechanical Engineering at Technische Universität München (TUM), affiliated with the Multiscale Modeling of Fluid Materials group. His work bridges computational chemistry, materials science, and machine learning, with a focus on Metal Organic Frameworks (MOF) and non-local molecular interactions. Education : MSc in Computational Science (University of Amsterdam, 2024), BSc in Chemistry (University of Warsaw, 2022), and BSc in Quantitative Methods (Warsaw School of Economics, 2021). Sanocki's research leverages graph neural networks and physics-informed machine learning to model complex fluid materials. His recent publications highlight applications in catalyst risk assessment and equivariant representations for molecular simulations. He is associated with the Atomistic Modeling Center (AMC) and Munich Data Science Institute (MDSI), contributing to projects that integrate machine learning with uncertainty quantification for physics-based models. Contact: m.sanocki@tum.de .

• Digital Twin
• Building Information Modeling (BIM)
• Graph Rewriting in Engineering Design
• +۳ مورد دیگر

Benedikt Hofer is a researcher at the Technical University of Munich (TUM), affiliated with the Professorship for Thermofluid Dynamics. His work focuses on the intersection of civil engineering, computer science, and digital modeling. University: Technical University of Munich Role: Researcher Contact: benedikt.hofer@tum.de Hofer’s research interests include Digital Twin , Building Information Modeling (BIM) , Graph Rewriting , and Parametric Modeling . His work emphasizes automated model generation, infrastructure digitization, and optimization through AI-driven methods. Recent publications highlight advancements in BIM collaboration , point cloud analysis for bridges , and graph-based design systems . These studies focus on scalability, version control, and integration of digital fabrication with BIM. The Technical University of Munich’s Thermofluid Dynamics group, led by Prof. Wolfgang Polifke, supports Hofer’s research in thermoacoustic interactions and fluid dynamics.

Johannes T. Margrafمشاهده پروفایل

استاد

• Machine Learning in Chemistry
• Electronic Structure Theory
• Density Functional Theory
• +۳ مورد دیگر

Prof. Johannes T. Margraf is a Professor and Chair of Physical Chemistry V: Theory and Machine Learning at the University of Bayreuth. His research group specializes in applying machine learning to chemical phenomena, including predicting properties of molecules and materials, understanding complex reaction networks, and developing data-efficient models that incorporate physical principles like size-extensivity and accurate descriptions of long-range interactions. The group also focuses on electronic structure theory, particularly bridging wavefunction and density functional methods. Margraf's research interests center on machine learning applications in chemistry and materials science, including non-local machine learning-based density functional theory, chemical reaction network analysis, and the development of physics-informed ML models. His work aims to achieve accurate chemical simulations at unprecedented scales for materials discovery and optimization. Analysis of recent publications shows a strong focus on machine learning potentials for materials simulation, density functional theory advancements, catalytic reaction networks, and computational spectroscopy. His research consistently integrates machine learning with fundamental physics principles to solve challenging problems in computational chemistry and materials science. Margraf leads a research team including postdoctoral researchers (Dr. Maciej Baradyn, Dr. Hyunwook Jung, Dr. Karlo Sovi´c) and PhD students (Nils Gönnheimer, David Greten, Konstantin Jakob, Sachin Rangaswamy, Robert Strothmann, Martin Vondrák). The group actively organizes scientific workshops and collaborates with institutions like the Fritz Haber Institute in Berlin.

Kirsten Schorningمشاهده پروفایل

استادیار

• Design of experiments
• Biostatistics
• Model selection
• +۳ مورد دیگر

Kirsten Schorning is a Junior Professor of Mathematical Statistics at the Department of Statistics, Faculty of Statistics, Technical University of Dortmund. She maintains her office in the Mathematics Building, Room 725, and leads a research group focused on statistical methodology development with applications in biostatistics and toxicology. Education: Bachelor's and Master's degree in Mathematics, Ruhr-University Bochum (2007-2012) Doctorate in Mathematics (Dr. rer. nat.), Ruhr-University Bochum (2012-2016) Dr. Schorning's research primarily centers on Design of Experiments , Biostatistics , and Model Selection and Averaging . Her work develops innovative statistical methodologies for optimal experimental design, particularly in drug combination studies, toxicological assessments, and dose-response modeling. She has made significant contributions to addressing day-to-day variability in toxicity assessments and developing optimal concentration designs for in vitro experiments. Analysis of her recent publications (2019-2025) reveals a strong focus on optimal experimental design methodologies with applications in pharmaceutical research and toxicology. Her work bridges theoretical statistics with practical applications, particularly in determining effective drug doses, analyzing mixture toxicity, and developing robust statistical approaches for concentration-response relationships. A notable trend is her increasing focus on addressing real-world complexities such as day-to-day variability in toxicity assessments and developing guidance frameworks for toxicological dose-response experiments. Dr. Schorning leads a research group at TU Dortmund that includes team members Sabine Bell (secretary), Pauline Baur, Julia Eichhorn, Justin Lamberti, and Onur Gül. Her research has significant implications for pharmaceutical development, environmental toxicology, and clinical trial design, providing statisticians and researchers with improved methodological tools for experimental planning and analysis.

Nataša Djurdjevac Conradمشاهده پروفایل

پژوهشگر

• Agent-Based Modeling
• Stochastic Processes
• Network Analysis
• +۷ مورد دیگر

Dr. Nataša Djurdjevac Conrad is the Head of the 'Computational Humanities' research group and Deputy Head of the Modeling and Simulation of Complex Processes department at the Zuse Institute Berlin (ZIB) since 2022. Her work bridges mathematics, archaeology, and social science through data-driven modeling of spatio-temporal processes. Research Focus : Modeling opinion dynamics, innovation dissemination (e.g., Roman road activation in ancient Tunisia), and epidemic spreading (MODUS-COVID project). Methodologies : Expertise in agent-based models (ABMs), stochastic metapopulation models, ODE/SDE compartmental models, and network analysis techniques like Galerkin projection and random-walk-based clustering. Awards : 'Paper of the Semester' award (2023) by MATH+ Activity Group 'Mathematics of Data Science' Corresponding member of the German Archaeological Institute (DAI) since 2022 Projects : Leads interdisciplinary work on cultural dispersion, epidemic policy design, and ancient innovation spreading.

Johanna Geißمشاهده پروفایل

پژوهشگر

• Event Detection
• Information Extraction
• Information Retrieval
• +۵ مورد دیگر

Johanna Geiß is a postdoctoral researcher at the Institute of Computer Science , Heidelberg University, specializing in Natural Language Processing , Information Extraction , and Event Detection . She contributes to projects like SCIDATOS (sepsis diagnosis) and EventAE (event-based Linked Data exploration). Education: PhD from University of Cambridge (2011), Magister Artium in Computational Linguistics (Heidelberg, 2006) Her research integrates Geoparsing , Social Network Analysis , and Graph Theory for tasks such as toponym disambiguation and entity resolution. Recent work includes frameworks like HeidelPlace and tools for semantic word clouds. Key awards include the 2010 Lundgren Research Award and multiple grants from the EPSRC and Cambridge European Trust. She has taught courses on Information Networks and Data Mining at Heidelberg University. As a postdoc , she advises on scientific computing applications in healthcare and contributes to open-source tools like NECKAr and EventAE .

Ke Thia Yaoمشاهده پروفایل

استاد پژوهشی

• 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.

• Control Systems
• Switched Systems
• Networked Control Systems
• +۶ مورد دیگر

Marc Seidel, M.Sc., is a Research Assistant at the Institute for Systems Theory and Automatic Control , University of Stuttgart. His work bridges theoretical and applied control systems research, focusing on autonomous mobility and networked control. Research Interests include: Controller design for real-time systems under weakly-hard constraints Switched systems with graph-constrained switching Networked Control Systems Event-triggered consensus schemes Orientation estimation using inertial sensors Publications reflect expertise in autonomous e-scooter control, multi-agent coordination, and sensor-based safety filters. Key themes: control theory, robotics, and sustainable transportation. Teaching Roles at the University of Stuttgart include: SS 2024: Formal Methods in Control SS 2022-SS 2023: Concepts of Automatic Control SS 2022: Nonlinear Dynamics WS 2021/22-WS 2025/26: Lab and project courses Academic Background : M.Sc. in Engineering Cybernetics (University of Stuttgart, 2021), thesis on periodic event-triggered consensus with cooperation from Prof. Dimos V. Dimarogonas (KTH Stockholm) B.Sc. in Engineering Cybernetics (University of Stuttgart, 2015-2019), thesis on trajectory tracking control Professional Engagements : 01/2025-03/2025: Research stay at Department of Automatic Control, Lund University 03/2021-08/2021: Internship at Robert Bosch GmbH (Renningen, Germany) 08/2020-11/2020: Study abroad at KTH Stockholm for master's thesis Projects : Lead responsibility in the Autonomous E-Scooter initiative, integrating control theory into sustainable urban mobility solutions.

• 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.

Lazaro Alonso Silvaمشاهده پروفایل

پژوهشگر

• Earth System Science
• Hybrid Modeling
• Machine Learning
• +۵ مورد دیگر

Dr. Lazaro Alonso Silva is a Research Associate at the Max Planck Institute for Biogeochemistry , working within the Department of Biogeochemical Integration . His research focuses on advancing Earth system models through hybrid modeling that combines process-based models with machine learning techniques. He advocates for open, reproducible research and contributes to tools like browzarr.io and EasyHybrid.jl .

Dominik Endresمشاهده پروفایل

استاد

• Cognitive Neuroscience
• Sensorimotor Integration
• Machine Learning
• +۷ مورد دیگر

Dominik Endres is a Professor at the Department of Psychology , Philipps-Universität Marburg , leading the Theoretical Cognitive Science working group. His research spans Cognitive Neuroscience , Sensorimotor Integration , and Machine Learning , with a focus on modeling how the brain represents knowledge and controls movement. His team investigates Movement Primitives in virtual reality, Bayesian Inference in attention control, and Formal Concept Analysis in decoding neural data. Recent work includes reaction time decomposition , VR immersion studies , and assistive technology development for neurological disorders. Publications highlight sensorimotor hierarchies , relational coding , and dynamic human motion modeling . Collaborations include the International Research Training Group 1901 , SFB/TRR 289 , and GRK 2271 . His team includes researchers like Neda Meibodi (PhD student) and Benjamin Knopp (Scientific Staff), with projects involving VR experiments and cognitive modeling .

Daniel Tenbrinckمشاهده پروفایل

استاد

• Data Science
• Machine Learning
• Biomedical Imaging
• +۶ مورد دیگر

Daniel Tenbrinck is an Academic Councilor and Acting Professor (W3) at the Department of Data Science, Faculty of Natural Sciences, Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU Erlangen-Nürnberg). His career spans roles at the University of Münster, ENSICAEN (France), and FAU Erlangen-Nürnberg. He holds a PhD in Computer Science (2013) from WWU Münster. Research Focus: Data Science, Machine Learning, Biomedical Imaging, Variational Methods, Graph Theory, and Numerical Analysis. Recent Publications: Explore hypergraph p-Laplacians, Fourier neural operators for image classification, and Bregman learning frameworks for sparse networks. Awards: Received the 2022 Teaching Award from FAU's Faculty of Natural Sciences and a 2021 performance bonus for outstanding contributions. Secured significant third-party funding, including a €1.98M BMBF grant for "COMFORT" (2024) and a €915k Bavarian Digitalization grant (2023). Teaching: Offers courses in Numerics, Mathematical Image Processing, Inverse Problems, and Data Science seminars. Actively involved in academic program development through grants like the 2023 Innovation Fund for Teaching.

• Artificial Intelligence
• Medical Imaging
• Educational Technology
• +۳ مورد دیگر

Jie Lu is a Professor at the University of Technology Sydney's Faculty of Engineering and Information Technology with extensive interdisciplinary research spanning medical imaging, educational technology, and artificial intelligence. Their work demonstrates strong collaborations with medical institutions including Capital Medical University and University of Chinese Academy of Sciences. Research interests focus on the application of AI in healthcare diagnostics, educational technology innovation, and computer vision systems. Key projects include developing multimodal AI systems for medical diagnosis, creating educational frameworks for AI literacy, and advancing wireless power transfer technologies. The research program shows particular strength in bridging theoretical AI advancements with practical clinical and educational applications. Recent publication trends reveal a strong focus on medical AI applications (35%), educational technology (25%), and computer vision systems (20%), with growing contributions to cybersecurity and wireless communication research. The work demonstrates consistent methodological innovation through combining deep learning with domain-specific knowledge across multiple disciplines. Notable scientific contributions include the development of multimodal diagnostic frameworks for ophthalmic diseases, novel approaches to AI literacy assessment in educational settings, and advanced image processing techniques for medical imaging applications. Jie Lu actively mentors graduate students and postdoctoral researchers, with supervised work appearing in top-tier conferences including ICLR, NDSS, and MICCAI. Current projects involve developing XR-based assessment tools for autistic adults and creating blockchain-assisted trusted computing systems for industrial IoT applications. Research is conducted through collaborations with medical imaging specialists, educational researchers, and industry partners, with several projects focused on translating AI research into practical clinical decision support systems and educational tools.